Ben Hoyt benhoyt

## gist:2647005
# On my Windows 7 64-bit machine running CPython 2.6

C:\>python -m timeit -s "lst = [1] * 100" "out = [x for x in lst if x]"
100000 loops, best of 3: 6.55 usec per loop

C:\>python -m timeit -s "lst = [1] * 100" "out = []" "for x in lst:" " if x:" "  out.append(x)"
100000 loops, best of 3: 14.7 usec per loop

## gist:3870305
"""Simple bouncing ball demo."""

import sys

import pygame

pygame.init()

size = (1024, 768)
speed = [1, 1]

## gist:4044946
"""Speed up os.walk() significantly by using file attributes that
FindFirst/Next give us instead of doing an extra stat(). Can also do the same
thing with opendir/readdir on Linux.

This is doubly useful when the user (caller of os.walk) is doing *another*
stat() to get say the file sizes.

On my tests (Windows 64-bit) our walk() is about 5x as fast as os.walk() for
large directory trees, and 9x as fast if you're doing the file size thing.
Note that these timings are "once it's in the cache", not first-time timings.

## ngrams.py
"""Print most frequent N-grams in given file.

Usage: python ngrams.py filename

Problem description: Build a tool which receives a corpus of text,
analyses it and reports the top 10 most frequent bigrams, trigrams,
four-grams (i.e. most frequently occurring two, three and four word
consecutive combinations).

NOTES

## generate_key.py
"""Function to generate a random string (key) of length chars."""

import binascii
import os


def generate_key(length=40, get_bytes=os.urandom):
    """Return a randomly-generated key of length chars.

    >>> len(generate_key())

## atomic_counter.py
"""An atomic, thread-safe incrementing counter."""

import threading


class AtomicCounter:
    """An atomic, thread-safe incrementing counter.

    >>> counter = AtomicCounter()
    >>> counter.increment()

## birthday_probability.py
"""Calculate the probability of generating a duplicate random number after
generating "n" random numbers in the range "d".

Usage: python birthday_probability.py n [d=365]

Each value can either be an integer directly, or in the format "2**x", where
x is the number of bits in the value.

For example, to calculate the probability that two people will have the same
birthday in a room with 23 people:

## snakes_and_ladders.py
"""Calculate the average number of moves in a snakes and ladders game.

Because as a parent one gets roped into these board (boring?) games
every so often, and I wanted to calculate the average duration of a
snakes and ladders game. Turns out it's about 36 moves (though
admittedly that's for a single-player game). :-)

> python snakes_and_ladders.py
Played 10000 rounds, averaged 36.0559 moves, max 324 moves, took 0.508s
"""

## thread_test.py
"""Test how many threads we can run at once."""

import itertools
import threading
import time
import sys

import requests


## sliding_window_sort.py
"""Efficient sliding-window sorting of time-series data in CSV file.

Demo for http://stackoverflow.com/a/42398981/68707

Tested on Python 3.5.
"""

import collections
import csv
import datetime
	# On my Windows 7 64-bit machine running CPython 2.6

	C:\>python -m timeit -s "lst = [1] * 100" "out = [x for x in lst if x]"
	100000 loops, best of 3: 6.55 usec per loop

	C:\>python -m timeit -s "lst = [1] * 100" "out = []" "for x in lst:" " if x:" " out.append(x)"
	100000 loops, best of 3: 14.7 usec per loop
	"""Simple bouncing ball demo."""

	import sys

	import pygame

	pygame.init()

	size = (1024, 768)
	speed = [1, 1]
	"""Speed up os.walk() significantly by using file attributes that
	FindFirst/Next give us instead of doing an extra stat(). Can also do the same
	thing with opendir/readdir on Linux.

	This is doubly useful when the user (caller of os.walk) is doing another
	stat() to get say the file sizes.

	On my tests (Windows 64-bit) our walk() is about 5x as fast as os.walk() for
	large directory trees, and 9x as fast if you're doing the file size thing.
	Note that these timings are "once it's in the cache", not first-time timings.
	"""Print most frequent N-grams in given file.

	Usage: python ngrams.py filename

	Problem description: Build a tool which receives a corpus of text,
	analyses it and reports the top 10 most frequent bigrams, trigrams,
	four-grams (i.e. most frequently occurring two, three and four word
	consecutive combinations).

	NOTES
	"""Function to generate a random string (key) of length chars."""

	import binascii
	import os


	def generate_key(length=40, get_bytes=os.urandom):
	"""Return a randomly-generated key of length chars.

	>>> len(generate_key())
	"""An atomic, thread-safe incrementing counter."""

	import threading


	class AtomicCounter:
	"""An atomic, thread-safe incrementing counter.

	>>> counter = AtomicCounter()
	>>> counter.increment()
	"""Calculate the probability of generating a duplicate random number after
	generating "n" random numbers in the range "d".

	Usage: python birthday_probability.py n [d=365]

	Each value can either be an integer directly, or in the format "2**x", where
	x is the number of bits in the value.

	For example, to calculate the probability that two people will have the same
	birthday in a room with 23 people:
	"""Calculate the average number of moves in a snakes and ladders game.

	Because as a parent one gets roped into these board (boring?) games
	every so often, and I wanted to calculate the average duration of a
	snakes and ladders game. Turns out it's about 36 moves (though
	admittedly that's for a single-player game). :-)

	> python snakes_and_ladders.py
	Played 10000 rounds, averaged 36.0559 moves, max 324 moves, took 0.508s
	"""
	"""Test how many threads we can run at once."""

	import itertools
	import threading
	import time
	import sys

	import requests
	"""Efficient sliding-window sorting of time-series data in CSV file.

	Demo for http://stackoverflow.com/a/42398981/68707

	Tested on Python 3.5.
	"""

	import collections
	import csv
	import datetime