xiaolai xiaolai

## gist:3817378
# encoding: UTF-8
require 'spec_helper'

## nodejs-cheatsheet.js
// Node.js CheatSheet.
// Download the Node.js source code or a pre-built installer for your platform, and start developing today.
// Download: http://nodejs.org/download/
// More: http://nodejs.org/api/all.html


// 0. Synopsis.
// http://nodejs.org/api/synopsis.html

## iterm2 changing font-size
cd ~/Library/Preferences/
open com.googlecode.iterm2.plist

## letters-to-100.py
#!/usr/bin/env python
# encoding: utf-8
"""
letters-to-100.py
"""

import string

letter_values = dict((l, i) for i, l in enumerate(string.lowercase, start=1))
english_dict = open('/usr/share/dict/words', 'rU')

## gist:2ffe003b05eb3a2f7ba933858d353d7d
search regex:

~*(?:^|[^~])(~~(\\w+(\\s\\w+)*)~~)

# replace with (attention: there's a white space at the end)
**$1**

## gist:cef9c92caa9c252fe6aaa3f43cecb102
# install anaconda
jupyter-lab --generate-config
edit ~/.jupyter/jupyter_notebook_config.py, add lines:

c.NotebookApp.token = '' #you can set password here.

#https://github.com/jiahaog/nativefier
npm install nativefier -g
nativefier "http://localhost:8888"

## mc-pi.rb
=begin
Find pi by the Monte-Carlo method.

area of a circle = pi r^2
area of a square = (2r)^2 = 4 r^2

Perform random uniform sampling between -1 and 1.
The proportion of points in the unit circle is:

p = (pi r^2) / (4r^2)

## mpipypi.py
"""

This code computes pi. It's not the first python
pi computation tool that I've written.  This program
is a good test of the mpi4py library, which is
essentially a python wrapper to the C MPI library.

To execute this code:

mpiexec -np NUMBER_OF_PROCESSES -f NODES_FILE python mpipypi.py

## brew.md

      
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                xiaolai
                / brew.md
            
            
              Created
              March 16, 2020 17:23
            
              
                homebrew upstream repo for China
              
          
    # 替换brew.git:
cd "$(brew --repo)"
git remote set-url origin https://mirrors.aliyun.com/homebrew/brew.git

# 替换homebrew-core.git:
cd "$(brew --repo)/Library/Taps/homebrew/homebrew-core"
git remote set-url origin https://mirrors.aliyun.com/homebrew/homebrew-core.git

# 替换homebrew-bottles:

  
## pi_mp.py
''' listing 6: pi_mp.py

Multiprocessing based code to estimate the value of PI
using monte carlo sampling
Ref: http://math.fullerton.edu/mathews/n2003/montecarlopimod.html
Uses workers:
http://docs.python.org/library/multiprocessing.html#module-multiprocessing.pool
'''

import random
	// Node.js CheatSheet.
	// Download the Node.js source code or a pre-built installer for your platform, and start developing today.
	// Download: http://nodejs.org/download/
	// More: http://nodejs.org/api/all.html


	// 0. Synopsis.
	// http://nodejs.org/api/synopsis.html
	#!/usr/bin/env python
	# encoding: utf-8
	"""
	letters-to-100.py
	"""

	import string

	letter_values = dict((l, i) for i, l in enumerate(string.lowercase, start=1))
	english_dict = open('/usr/share/dict/words', 'rU')
	search regex:

	~(?:^\|[^~])(~~(\\w+(\\s\\w+))~~)

	# replace with (attention: there's a white space at the end)
	$1
	# install anaconda
	jupyter-lab --generate-config
	edit ~/.jupyter/jupyter_notebook_config.py, add lines:

	c.NotebookApp.token = '' #you can set password here.

	#https://github.com/jiahaog/nativefier
	npm install nativefier -g
	nativefier "http://localhost:8888"
	=begin
	Find pi by the Monte-Carlo method.

	area of a circle = pi r^2
	area of a square = (2r)^2 = 4 r^2

	Perform random uniform sampling between -1 and 1.
	The proportion of points in the unit circle is:

	p = (pi r^2) / (4r^2)
	"""

	This code computes pi. It's not the first python
	pi computation tool that I've written. This program
	is a good test of the mpi4py library, which is
	essentially a python wrapper to the C MPI library.

	To execute this code:

	mpiexec -np NUMBER_OF_PROCESSES -f NODES_FILE python mpipypi.py
	''' listing 6: pi_mp.py

	Multiprocessing based code to estimate the value of PI
	using monte carlo sampling
	Ref: http://math.fullerton.edu/mathews/n2003/montecarlopimod.html
	Uses workers:
	http://docs.python.org/library/multiprocessing.html#module-multiprocessing.pool
	'''

	import random