AbhiAgarwal

var phys = require('phys');
console.log(phys.mechanics.momentum(2, 3));
console.log(phys.information.mechanics.momentum());

AbhiAgarwal

<script src="http://www.physjs.com/js/phys.min.js"></script>
<script>
  console.log(phys.basic.changein(2, 3));
  console.log(phys.mechanics.momentum(2, 3));
</script>

AbhiAgarwal

// Gets Weather from the ForecastIO API and saves the JSON file
var set_weather = function() {
	jquery.getJSON('https://api.forecast.io/forecast/'+ apiKeys.forecastIO + '/' + person.latitude + ',' + person.longitude, function(data) {
		fs.writeJson('./files/weather' + datetime + '.json', data, function(err){
			if(err){ console.log(err);}
			weather.time = datetime;
			weather.summary = data.currently.summary;
			weather.temperature = String(convert_fahrenheitTocelsius(String(data.currently.temperature)));
			weather.file = true;
		});

AbhiAgarwal

var queryAPI = function() {
  $.ajax({
    // URL of the API you want to access
    url: 'https://housing.nyu.edu/api/nyuapirooms/RoomSpaceDescription/AH-002E1-1',
    // When the data comes back
    success: function(data) {
      // Go through all the data
      for(var eachval in data) {
        // and print all of it out
        console.log(data[eachval]["HallName"]);

AbhiAgarwal

'''
Finds all the circles within your eye
'''

import cv2
import cv2.cv as cv
import numpy as np

image = cv2.imread('eye-image.jpg')
cv2.imshow('main_image', image)

AbhiAgarwal

import urllib2
import simplejson
import urllib
import errno
import os

# Fixed
globalSearchURL = "http://ajax.googleapis.com/ajax/services/search/images?v=1.0&q=%s&start=%s"
fetch = urllib2.build_opener()

AbhiAgarwal

// run using g++-mp-4.7 -fopenmp -std=c++11 openmp_example.cpp -o openmp_example 
// on my mac. Not sure about everyone elses, but my g++ version doesn't have openp
// so I have to use that one

#include <iostream>
using namespace std;
 
#include <omp.h>
 
int main(int argc, char *argv[])

AbhiAgarwal

import numpy as np
import sklearn
from sklearn.tree import *
from sklearn.datasets import *

iris = load_iris()

X = iris.data
y = iris.target

AbhiAgarwal

{
    "_id": "5466b5a438f0d87f029cd0d5",
    "abstract": null,
    "allText": "By KEN BELSONNOV. 14, 2014\n\nThere is no shortage of ways for fans to bet on sports, even though federal law forbids gambling on games in almost all states: Fantasy leagues, state-run lotteries and neighborhood bookies, as well as online offshore ones, will take a fan’s action.\nFor decades, professional sports leagues have steadfastly opposed gambling and backed the prohibition of betting on games because they claimed it would undermine their sports’ integrity and lead to questions about the honesty of players and referees.\nBut the leagues, eager for a slice of a multibillion-dollar moneymaker, have also tiptoed around their own opposition. N.F.L. teams have licensing deals with state lotteries and have pushed to establish a franchise in London, where betting shops are ubiquitous.\nMajor League Baseball, the N.B.A. and the N.H.L. all work with fantasy sports companies. This year, the Devils and the Philadelphia 76ers partnered w

AbhiAgarwal

import csv

ebola = open('Ebola_frequencies.csv')
csv_ebola = csv.reader(ebola)

for i in csv_ebola:
	# Basically csv_ebola contains an array of items
	# The first index: i[0] is the word
	# And the rest i[1] i[2] i[3] i[4] correspond to the dates
	print 'Word', i[0], '2014-10-20', i[1], '2014-10-27', i[2], '2014-10-06', i[3], '2014-10-13', i[4]