sixtenbe

#!/usr/bin/python2


# Copyright (C) 2016 Sixten Bergman
# License WTFPL
#
# This program is free software. It comes without any warranty, to the extent
# permitted by applicable law. 
# You can redistribute it and/or modify it under the terms of the Do What The
# Fuck You Want To Public License, Version 2, as published by Sam Hocevar. See

CristinaSolana

1. Clone your fork:

git clone git@github.com:YOUR-USERNAME/YOUR-FORKED-REPO.git

2. Add remote from original repository in your forked repository:

cd into/cloned/fork-repo
git remote add upstream git://github.com/ORIGINAL-DEV-USERNAME/REPO-YOU-FORKED-FROM.git
git fetch upstream

geotheory

// Galaxy collisions

Star[] Stars;
PVector[] galp;
PVector[] galv;
color[] galcol, galcol2;
boolean paused = false;
int side = 100;      // border around Stars' initial positions
int Startot = 300;   // number of Stars
int obsize = 10;     // size of Stars

alexalemi

import math
class Welford(object):
    """ Implements Welford's algorithm for computing a running mean
    and standard deviation as described at: 
        http://www.johndcook.com/standard_deviation.html

    can take single values or iterables

    Properties:
        mean    - returns the mean

Robsteranium

/*
 * NAME
 * 
 * statistics-distributions.js - JavaScript library for calculating
 *   critical values and upper probabilities of common statistical
 *   distributions
 * 
 * SYNOPSIS
 * 
 * 

rochacbruno

#!/usr/bin/env python

# Haversine formula example in Python
# Author: Wayne Dyck

import math

def distance(origin, destination):
    lat1, lon1 = origin
    lat2, lon2 = destination

repustate

import requests

API_KEY = 'YOUR_API_KEY'
BASE_URL = 'http://social.repustate.com/%(api_key)s/%(call)s.json'

# Create a new data source.
kwargs = {'api_key':API_KEY, 'call':'add-datasource'}
response = requests.post(BASE_URL % kwargs, {'name':'Rob Ford', 'language':'en', 'niche':'general'})
datasource_id = response.json()['datasource_id']

debasishg

General Background and Overview

1. Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
2. Models and Issues in Data Stream Systems
3. Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t

rjz

// Depends on `through`
//
//     $ npm install through
//
// Usage:
//
//     $ echo 'hello' | node stdin-and-fs-stream.js 
//     $ echo 'hello' > tmp && node stdin-and-fs-stream.js tmp
//
var fs = require('fs'),

chris-rock

// Part of https://github.com/chris-rock/node-crypto-examples

// Nodejs encryption of buffers
var crypto = require('crypto'),
    algorithm = 'aes-256-ctr',
    password = 'd6F3Efeq';

var fs = require('fs');
var zlib = require('zlib');