https://nfdc.faa.gov/xwiki/bin/view/NFDC/WebHome
// 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') |
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.
Setup GitHub issue labels script |
class Woodchuck | |
attr_accessor :chuck_count | |
@@woodchuck_count = 0 | |
def initialize | |
@chuck_count = 0 | |
@@woodchuck_count += 1 | |
end |
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.
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
#!/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): |