atmb4u

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atmb4u

#!/usr/bin/env python
"""

Usage: Put this script in any /bin folder in the PATH and run
snakecase "Hello World (123).csv"
OR 
Download this snippet and run
python snakecase "Hello World (123).csv"
Output: hello_world_123.csv
"""

atmb4u

import re, itertools

def similarish(string1, string2):
    """
    Find if two different strings (names) are similar enough to make it a single person's name.
    Input
        string1 : a person's full/partial name
        string2 : same person's full/partial name or different formatting from name1
    Output
        Boolean

atmb4u

// http://stackoverflow.com/questions/9160001/how-to-profile-methods-in-scala
def time[R](block: => R): R = {
    val t0 = System.nanoTime()
    val result = block    // call-by-name
    val t1 = System.nanoTime()
    println("Elapsed time: " + (t1 - t0) + "ns")
    result
}

atmb4u

from hashlib import md5
from multiprocessing import Pool
from time import time

"""
A quick and dirty CPU benchmarking in python
Calculates md5 for a text multiple times in
multiple processes to push CPU to its limits.
"""

atmb4u

#!/usr/bin/python
# -*- coding: utf-8 -*-

power_dict = [
    "⁰",
    "¹",
    "²",
    "³",
    "⁴",
    "⁵",

atmb4u

import numpy as np
import numpy.random as npr
import pylab


def bootstrap(data, num_samples, statistic, alpha):
    """Returns bootstrap estimate of 100.0*(1-alpha) CI for statistic."""
    n = len(data)
    idx = npr.randint(0, n, (num_samples, n))
    samples = data[idx]

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class Data:
    def __init__(self, dictionary):
        for k, v in dictionary.items():
        setattr(self, k, v)
o = Data({'a': 1, 'b': 2})
o.a

atmb4u

import csv
import itertools
from collections import Counter, defaultdict
import json
import re
"""
get Blockbuster Database dataset from http://www.crowdflower.com/data-for-everyone
"""
f = open("top_ten_movies_per_year_DFE.csv")
r = csv.DictReader(f)

atmb4u

#add this to your urls file
url(r'^version/$', 'api.views.code_version', name='code_version'),