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''' 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

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"""

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

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=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)

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#!/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')

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#!/bin/bash

if [ "$1" = "-h" -o "$1" = "--help" -o -z "$1" ]; then cat <<EOF
appify v3.0.1 for Mac OS X - http://mths.be/appify
Creates the simplest possible Mac app from a shell script.

Appify takes a shell script as its first argument:

    `basename "$0"` my-script.sh

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58 bytes of CSS to look great nearly everywhere

When making this website, i wanted a simple, reasonable way to make it look good on most displays. Not counting any minimization techniques, the following 58 bytes worked well for me:

main {
  max-width: 38rem;
  padding: 2rem;
  margin: auto;
}