An introduction to curl using GitHub's API.
Makes a basic GET request to the specifed URI
curl https://api.github.com/users/caspyin
| # http://geospatialpython.com/ | |
| import json, pymongo, shapefile | |
| from json import dumps | |
| database = 'geo' | |
| connection = pymongo.MongoClient() | |
| db = getattr(connection,database) | |
| def renamesh2json(x): | |
| d = x.split(".") |
| #!/bin/bash | |
| # | |
| # Requires: | |
| # - gdal_sieve.py | |
| # - ogr2ogr (GDAL) | |
| # - topojson (node.js) | |
| # Grab the relative directory for source file. | |
| SRC_DIR=`dirname $0` |
| <!DOCTYPE html> | |
| <html> | |
| <head> | |
| <meta charset="utf-8"> | |
| <link rel="stylesheet" href="style.css"> | |
| </head> | |
| <body> | |
| <svg id="defs"> | |
| <defs> | |
| <linearGradient id="gradBlue" x1="0%" y1="0%" x2="100%" y2="0%"> |
| set -o verbose | |
| set -o errexit | |
| echo '* Create and initialize a repository.' | |
| git init gitflow-tutorial | |
| cd gitflow-tutorial/ | |
| git flow init -d | |
| echo '* Develop a Feature' |
D3.js visualization of Anscombe's Quartet. Each dataset in the quartet is graphed separately along with its linear best fit. While all datasets have the same summary statistics (mean, variance, correlation, linear fit), their structures differ markedly.
Anscombe used the quartet in his seminal paper "Graphs in Statistical Analysis" to emphasize the importance of data visualization for exploratory data analysis. Such visualization exploration enables the analyst to make more informed analytical decisions and conclusions.