adamreisnz

Setup GitHub issue labels script

adelevie

How to read an FCC Notice of Proposed Rulemaking

This short guide will teach you to read an FCC NPRM with grep. The underlying assumption is that stop words can be used to find key pieces of information in a large body of text. In the case of a notice of proposed rulemaking, those key pieces of information are tentative conclusions and requests for comment.

"We" words

FCC NPRMs often use the word "we" when referring to the office or bureau responsible for the document, or sometimes even the Commission itself. With this knowledge, it's possible to find out the types of actions the FCC is taking.

$ grep -R "We" corpus.txt --no-filename

vdavez

#!/usr/bin/env python3

from urllib import request, parse
import json

base_url = "http://lims.dccouncil.us/_layouts/15/uploader/AdminProxy.aspx/"
keyword_test = ""

# Generic function to get Data from LIMS 
def getFromLIMS(view, payload):

max-mapper

// data comes from here http://stat-computing.org/dataexpo/2009/the-data.html
// download 1994.csv.bz2 and unpack by running: cat 1994.csv.bz2 | bzip2 -d > 1994.csv
// 1994.csv should be ~5.2 million lines and 500MB
// importing all rows into leveldb took ~50 seconds on my machine

// there are two main techniques at work here:
// 1: never create JS objects, leave the data as binary the entire time (binary-split does this)
// 2: group lines into 16 MB batches, to take advantage of leveldbs batch API (byte-stream does this)

var level = require('level')

mikemorris

campeterson

Aviation

National Flight Data Center (NFDC)

https://nfdc.faa.gov/xwiki/bin/view/NFDC/WebHome

FAA Data & Research

http://www.faa.gov/data_research/

Flight Delay Information

http://www.fly.faa.gov/flyfaa/usmap.jsp

ashleygwilliams

class Woodchuck
  attr_accessor :chuck_count

  @@woodchuck_count = 0

  def initialize
    @chuck_count = 0
    @@woodchuck_count += 1
  end

tmcw

redo: Combine Several Fields into One.

A sort of continuation of GIS with Python, Shapely, and Fiona.

Combine several fields into one with Python, Shapely, and Fiona! You'll also want GDAL around.

First, let's start out with data. I'll use Natural Earth Countries 1:50m. Download that file and move it to a directory.