Tim Hopper tdhopper

So I started developing something remarkably similar about 4 years ago (in Django, too!). Wrote up a business plan with monetization, p&l, etc. I brought the plan and prototype to a few seed folks for early funding before I admittedly lost interest and moved onto something else.

The hardest technical challenge I had was, knowing there would need to be a ton of recipes in the system to make it truly effective, I had to do an automated intake of recipes from many places. But ingredient normalization got in the way, even with really good regex/etl practices. For instance, a recipe says, "boneless skinless chicken breast".. another says, "skinless boneless chicken breast". Some list the # of breasts. Some list pounds. Some mean the breast is split, some don't. But in order for the nutrition info to be accurate, the normalization process had to be near perfect. I ended up "buying" the source code to "recipefox" a recipe parsing plugin for firefox (for $100 or something like that) which helped tremendously, but st

My scripts for building stiglerdiet.com with Pelican and uploading to Amazon S3 bucket.

Requires:

json files should go into templates folder of ofexport. Modify the ofexport commands to fit your needs. In particular, _Nonurgent is the folder my projects are in, and "Posts to Write" and "To Read" are the projects I'm uploading.

	def smallest_missing_number(stream, min_val, max_val, bin_exp):
	###
	# Returns the smallest counting number _not_ contained in a bounded
	# sequence of counting numbers.
	#
	# Inputs:
	# stream: iterator of integer values
	# min_val: lower bound on values in stream
	# max_val: upper bound on values in stream
	# bin_exp: size of bin given as exponent of 2 (bin_size = 2^bin_exp)

	import clipboard
	import urllib2
	import webbrowser


	clipString = clipboard.get()

	marky = 'http://heckyesmarkdown.com/go/?read=1&u='

	queryString = marky + clipString

	{
	"metadata": {
	"name": "OTW hollow slopes"
	},
	"nbformat": 3,
	"nbformat_minor": 0,
	"worksheets": [
	{
	"cells": [
	{

	# Used for unshortening list of urls as wel as checking the HTTP status code of a webpage.
	# When called from the command line, requires either a filename or STDIN as input
	# Filename or standard input should contain one URL per line
	# Output is printed to the standard out and saved to a JSON file using PickleDB.

	__author__ = "Tim Hopper"
	__email__ = "[email protected]"

	import fileinput
	import urllib2

	---------------------------------------------------------------------------
	AttributeError Traceback (most recent call last)
	<ipython-input-7-01896d0e98b9> in <module>()
	5 df = pd.DataFrame({"content":["tim hopper", "this data tim"]})
	6 mapper = DataFrameMapper([("content", CountVectorizer())])
	----> 7 mapper.fit_transform(df)

	C:\Anaconda\lib\site-packages\sklearn\base.pyc in fit_transform(self, X, y, **fit_params)
	406 if y is None:
	407 # fit method of arity 1 (unsupervised transformation)

	import re, string, sys, pandas

	stops = set(open("../stop_words.txt").read().split(",") + list(string.ascii_lowercase))
	words = [x.lower() for x in re.split("[^a-zA-Z]+", open("../pride-and-prejudice.txt").read()) if len(x) > 0 and x.lower() not in stops]
	print pandas.Series(words).value_counts().head(25)

	{
	"metadata": {
	"name": ""
	},
	"nbformat": 3,
	"nbformat_minor": 0,
	"worksheets": [
	{
	"cells": [
	{

	// Use Gists to store code you would like to remember later on
	console.log(window); // log the "window" object to the console