Andrew Pendleton apendleton

Cherry-almond Fudge

Adapted from Cook's Illustrated 15-minute walnut fudge.

Ingredients

16oz bittersweet chocolate (I use Ghirardelli 60% bittersweet bars)
2oz unsweetened chocolate (again, I use Ghirardelli, 100% bars)
1 can sweetened condensed milk (whole)

Baked steel-cut oatmeal

This is kind of inspired by this recipe. Amounts are what I did, but if I do it again I might slightly decrease the nutmeg and maybe slightly increase the sugar.

Ingredients

1.5 cups steel cut oatmeal
2.5 cups boiling water
2tsp baking powder

Curried Pear Pie

Ingredients

Note: these are amounts for a nine-inch round pie. For the 13x9 rectangular pie, I used a double batch of filling and a 1.5x batch of crust.

One batch pie crust for a double crust pie (I'm a fan of this one)
1/2 cup golden raisins
2Tbsp garlic-ginger paste (recipe follows)
1Tbsp ground coriander

	# first you need to make sure you have the libraries installed; I'm using pyquery and requests:
	# pip install pyquery requests

	import requests
	from pyquery import PyQuery as pq
	from urlparse import urljoin

	URL = "http://www.stat-gabon.org/"

	response = requests.get(URL)

	void setup() {
	pinMode (2,OUTPUT);//attach pin 2 to vcc
	pinMode (5,OUTPUT);//attach pin 5 to GND
	// initialize serial communication:
	Serial.begin(9600);
	}

	void loop()
	{
	digitalWrite(2, HIGH);

	var ocemail = function(url, domain) {
	var capitalize = function(s) { return s.charAt(0).toUpperCase() + s.slice(1).toLowerCase(); }

	// url parser from http://jsperf.com/url-parsing
	var urlParseRE = /^(((([^:\/#\?]+:)?(?:(\/\/)((?:(([^:@\/#\?]+)(?:\:([^:@\/#\?]+))?)@)?(([^:\/#\?\]\[]+\|\[[^\/\]@#?]+\])(?:\:([0-9]+))?))?)?)?((\/?(?:[^\/\?#]+\/+))([^\?#])))?(\?[^#]+)?)(#.*)?/;
	var hostname = urlParseRE.exec(url)[11];

	if (hostname) {
	var domainMatch = /^(?:www[.])?([-a-z0-9]+)[.](house\|senate)[.]gov$/;
	var match = domainMatch.exec(hostname.toLowerCase());

	use std::num::pow;

	pub struct Point { x: int, y: int }
	struct Line { p1: Point, p2: Point }

	impl Line {
	pub fn length(&self) -> f64 {
	let xdiff = self.p1.x - self.p2.x;
	let ydiff = self.p1.y - self.p2.y;
	((pow(xdiff, 2) + pow(ydiff, 2)) as f64).sqrt()

	import csv, re

	SEARCH_TERMS = [re.compile(term, re.I) for term in [r"(?<!white )house", "HFAC", "Congressman", "Congresswoman", "Congressional", "Senate", "Senator"]]
	SEARCH_COLS = ["contact_title", "contact_name", "contact_office", "contact_agency"]

	csv_infile = open('contacts.csv', 'rb')
	csv_in = csv.DictReader(csv_infile)

	csv_outfile = open('contacts_filtered.csv', 'wb')
	csv_out = csv.DictWriter(csv_outfile, csv_in.fieldnames)

	<script>
	(function() {
	var insertScript = function(url) {
	var elem = document.createElement('script');
	elem.src = (document.location.protocol == "https:" ? "https://cdns" : "http://cdn") + ".gigya.com/js/" + url;
	elem.async = true;
	elem.type = "text/javascript";
	$('script').eq(0).before(elem);
	}
	window.onGigyaServiceReady = function(type) {

	"""
	This script ingests a CSV of last names and builds JSON output on the
	percentage of last names that begin with each letter of the alphabet.

	I initially ran it on a CSV-ified version of the US Census's list of all last
	names with more than 100 occurrences, and used the frequency field within that
	file to weigh the output, but absent that field, it assigns equal weight to
	each name, allowing us to also process our TCamp 2012 attendence list.

	Using the script on the Census data available in: