GitHub30

## index.html
<!DOCTYPE html>
<html>
<head>
  <meta charset="utf-8">
  <meta name="viewport" content="width=device-width">
  <title>JS Bin</title>
<style id="jsbin-css">
.viewport {
  width: 160px;
  overflow-x: hidden;

## index.html
<!DOCTYPE html>
<html>
<head>
  <meta charset="utf-8">
  <meta name="viewport" content="width=device-width">
  <title>JS Bin</title>
</head>
<body>
<textarea name="" id="" cols="30" rows="10">{"hello": "world"}</textarea>
  <a download="hoge.json" onclick="doDownload(this)">download</a>

## death_march.md

      
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                GitHub30
                / death_march.md
            
            
              Created
              May 7, 2018 04:55
                — forked from voluntas/death_march.md
            
              
                デスマーチが起きる理由 - ３つの指標
              
          
    デスマーチが起きる理由 - ３つの指標

著者： 青い鴉（ぶるくろ） @bluecrow2 様
これは結城浩さんの YukiWiki 運用停止で消えてしまった http://www.hyuki.com/yukiwiki/wiki.cgi?%A5%C7%A5%B9%A5%DE%A1%BC%A5%C1%A4%AC%B5%AF%A4%AD%A4%EB%CD%FD%CD%B3 に存在していた文章のバックアップです。
自分がとても感銘を受けた文章なので、このまま読めなくなるのはとてももったいないと思い、バックアップとして公開しています。
もし図を保存されていた方いらっしゃいましたら、 Gist にコメントを頂けると嬉しいです。
この記事に対してなにかある場合はこの Gist にコメントをお願いします。

  
## play-with-docker.sh
# port forwarding(-p 3306:3306) required
docker run -p 3306:3306 -e MYSQL_ROOT_PASSWORD=password -d mysql:5.7 # play with docker (https://stackoverflow.com/questions/49194719/authentication-plugin-caching-sha2-password-cannot-be-loaded/49944625#49944625#answer-49944625 for mysql8)

wget https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip
unzip ngrok-stable-linux-amd64.zip
# tunnel online(free authtoken required)
./ngrok tcp 3306


# In local

## speech-recording.html
<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <meta http-equiv="X-UA-Compatible" content="ie=edge">
    <title>Speech Recording</title>
</head>
<body>
  <script>

## tweepyでリアルタイムハッシュタグ検索
#!/usr/bin/env python
# coding: utf-8

import tweepy
import tw_key

CONSUMER_KEY = tw_key.twdict['cons_key']
CONSUMER_SECRET = tw_key.twdict['cons_sec']
ACCESS_TOKEN_KEY = tw_key.twdict['accto_key']
ACCESS_TOKEN_SECRET = tw_key.twdict['accto_sec']

## gist:85baea6c47c7114684b967207d06522f
#!/usr/bin/env python
# -*- coding: utf-8 -*-

import math
import random
import chainer
import chainer.functions as F
import chainer.links as L
import numpy as np
from chainer import reporter

## turbo_colormap.py
# Copyright 2019 Google LLC.
# SPDX-License-Identifier: Apache-2.0

# Author: Anton Mikhailov

turbo_colormap_data = [[0.18995,0.07176,0.23217],[0.19483,0.08339,0.26149],[0.19956,0.09498,0.29024],[0.20415,0.10652,0.31844],[0.20860,0.11802,0.34607],[0.21291,0.12947,0.37314],[0.21708,0.14087,0.39964],[0.22111,0.15223,0.42558],[0.22500,0.16354,0.45096],[0.22875,0.17481,0.47578],[0.23236,0.18603,0.50004],[0.23582,0.19720,0.52373],[0.23915,0.20833,0.54686],[0.24234,0.21941,0.56942],[0.24539,0.23044,0.59142],[0.24830,0.24143,0.61286],[0.25107,0.25237,0.63374],[0.25369,0.26327,0.65406],[0.25618,0.27412,0.67381],[0.25853,0.28492,0.69300],[0.26074,0.29568,0.71162],[0.26280,0.30639,0.72968],[0.26473,0.31706,0.74718],[0.26652,0.32768,0.76412],[0.26816,0.33825,0.78050],[0.26967,0.34878,0.79631],[0.27103,0.35926,0.81156],[0.27226,0.36970,0.82624],[0.27334,0.38008,0.84037],[0.27429,0.39043,0.85393],[0.27509,0.40072,0.86692],[0.27576,0.41097,0.87936],[0.27628,0.42118,0.89123],[0.27667,0.43134,0.90254],[0.27691,0.44145,0.913

## decryptchromecookies.py
import sqlite3

def get_chrome_cookies(db=None):
    import json
    from base64 import b64decode
    from win32.win32crypt import CryptUnprotectData # pip install pywin32
    # should use Cryptodome in windows instead of Crypto
    # otherwise will raise an import error
    from Cryptodome.Cipher.AES import new, MODE_GCM # pip install pycryptodomex

## hclustering.py
"""
Hierarchial Clustering.
The goal of gist is to show to use scikit-learn to perform agglomerative clustering when:
1. There is a need for a custom distance metric (like levenshtein distance)
2. Use the distance in sklearn's API.

Adapted from: sklearn's FAQ.
http://scikit-learn.org/stable/faq.html
"""
	<!DOCTYPE html>
	<html>
	<head>
	<meta charset="utf-8">
	<meta name="viewport" content="width=device-width">
	<title>JS Bin</title>
	<style id="jsbin-css">
	.viewport {
	width: 160px;
	overflow-x: hidden;
	# port forwarding(-p 3306:3306) required
	docker run -p 3306:3306 -e MYSQL_ROOT_PASSWORD=password -d mysql:5.7 # play with docker (https://stackoverflow.com/questions/49194719/authentication-plugin-caching-sha2-password-cannot-be-loaded/49944625#49944625#answer-49944625 for mysql8)

	wget https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip
	unzip ngrok-stable-linux-amd64.zip
	# tunnel online(free authtoken required)
	./ngrok tcp 3306


	# In local
	<!DOCTYPE html>
	<html lang="en">
	<head>
	<meta charset="UTF-8">
	<meta name="viewport" content="width=device-width, initial-scale=1.0">
	<meta http-equiv="X-UA-Compatible" content="ie=edge">
	<title>Speech Recording</title>
	</head>
	<body>
	<script>
	#!/usr/bin/env python
	# coding: utf-8

	import tweepy
	import tw_key

	CONSUMER_KEY = tw_key.twdict['cons_key']
	CONSUMER_SECRET = tw_key.twdict['cons_sec']
	ACCESS_TOKEN_KEY = tw_key.twdict['accto_key']
	ACCESS_TOKEN_SECRET = tw_key.twdict['accto_sec']
	#!/usr/bin/env python
	# -- coding: utf-8 --

	import math
	import random
	import chainer
	import chainer.functions as F
	import chainer.links as L
	import numpy as np
	from chainer import reporter
	# Copyright 2019 Google LLC.
	# SPDX-License-Identifier: Apache-2.0

	# Author: Anton Mikhailov

	turbo_colormap_data = [[0.18995,0.07176,0.23217],[0.19483,0.08339,0.26149],[0.19956,0.09498,0.29024],[0.20415,0.10652,0.31844],[0.20860,0.11802,0.34607],[0.21291,0.12947,0.37314],[0.21708,0.14087,0.39964],[0.22111,0.15223,0.42558],[0.22500,0.16354,0.45096],[0.22875,0.17481,0.47578],[0.23236,0.18603,0.50004],[0.23582,0.19720,0.52373],[0.23915,0.20833,0.54686],[0.24234,0.21941,0.56942],[0.24539,0.23044,0.59142],[0.24830,0.24143,0.61286],[0.25107,0.25237,0.63374],[0.25369,0.26327,0.65406],[0.25618,0.27412,0.67381],[0.25853,0.28492,0.69300],[0.26074,0.29568,0.71162],[0.26280,0.30639,0.72968],[0.26473,0.31706,0.74718],[0.26652,0.32768,0.76412],[0.26816,0.33825,0.78050],[0.26967,0.34878,0.79631],[0.27103,0.35926,0.81156],[0.27226,0.36970,0.82624],[0.27334,0.38008,0.84037],[0.27429,0.39043,0.85393],[0.27509,0.40072,0.86692],[0.27576,0.41097,0.87936],[0.27628,0.42118,0.89123],[0.27667,0.43134,0.90254],[0.27691,0.44145,0.913
	import sqlite3

	def get_chrome_cookies(db=None):
	import json
	from base64 import b64decode
	from win32.win32crypt import CryptUnprotectData # pip install pywin32
	# should use Cryptodome in windows instead of Crypto
	# otherwise will raise an import error
	from Cryptodome.Cipher.AES import new, MODE_GCM # pip install pycryptodomex
	"""
	Hierarchial Clustering.
	The goal of gist is to show to use scikit-learn to perform agglomerative clustering when:
	1. There is a need for a custom distance metric (like levenshtein distance)
	2. Use the distance in sklearn's API.

	Adapted from: sklearn's FAQ.
	http://scikit-learn.org/stable/faq.html
	"""