1つの入力に対して複数の出力をするには、tee
を使う。
以下は、RTMP入力をパイプする例。
$ gst-launch-1.0 rtmpsrc location=$INPUT ! flvdemux ! flvmux ! tee name=t ! queue ! rtmpsink location=$OUT1 t. ! queue ! rtmpsink location=$OUT2
package main | |
import ( | |
"fmt" | |
) | |
func main() { | |
var a int | |
var answer string | |
fmt.Scan(&a) |
'use strict' | |
function main (input) { | |
let x = Number(input) | |
if (x < 1200) { | |
console.log('ABC') | |
} else { | |
console.log('ARC') | |
} | |
} |
1つの入力に対して複数の出力をするには、tee
を使う。
以下は、RTMP入力をパイプする例。
$ gst-launch-1.0 rtmpsrc location=$INPUT ! flvdemux ! flvmux ! tee name=t ! queue ! rtmpsink location=$OUT1 t. ! queue ! rtmpsink location=$OUT2
$ sudo add-apt-repository ppa:certbot/certbot
$ sudo apt-get update
$ sudo apt-get install certbot
standalone
$ sudo certbot certonly --standalone -d example.com -m some@example.com --agree-tos -n
const Chromy = require('chromy') | |
const word = 'ヘッドレスブラウザ' | |
const chromy = new Chromy() | |
chromy.chain() | |
.goto('https://google.com') | |
.insert('input[type=text]', word) | |
.click('input[value^=Google]', {waitLoadEvent: true}) | |
.evaluate(() => { |
const Chromy = require('chromy') | |
const word = 'JavaScript' | |
const chromy = new Chromy() | |
chromy.chain() | |
.goto('https://www.amazon.co.jp/') | |
.select('select#searchDropdownBox', 'search-alias=stripbooks') | |
.insert('input#twotabsearchtextbox', word) | |
.click('input[type=submit]', {waitLoadEvent: true}) |
const SideEff = require('side-eff') | |
// グローバル変数 | |
let message = '' | |
class Message extends SideEff { | |
// 副作用のあるメソッド | |
affect (payload) { | |
message += payload + '\n' | |
} |
cat package.json | jq '.name = "modified-name"' | sponge package.json |
<!DOCTYPE html> | |
<html> | |
<head> | |
<meta charset="utf-8"> | |
<meta name="viewport" content="width=device-width,initial-scale=1"> | |
<title>Smartphone Textarea</title> | |
</head> | |
<style> | |
html, body { | |
margin: 0; |
import numpy as np | |
import pandas as pd | |
from sklearn.preprocessing import Imputer, StandardScaler | |
from sklearn.model_selection import train_test_split | |
from sklearn.svm import SVC | |
from sklearn.metrics import accuracy_score | |
# data frame オブジェクト | |
df = pd.read_csv("./data/train.csv") |