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@piscisaureus
piscisaureus / pr.md
Created August 13, 2012 16:12
Checkout github pull requests locally

Locate the section for your github remote in the .git/config file. It looks like this:

[remote "origin"]
	fetch = +refs/heads/*:refs/remotes/origin/*
	url = [email protected]:joyent/node.git

Now add the line fetch = +refs/pull/*/head:refs/remotes/origin/pr/* to this section. Obviously, change the github url to match your project's URL. It ends up looking like this:

@paulusm
paulusm / ipython.conf
Created January 19, 2013 21:22
Upstart conf for iPython web
start on runlevel [2345]
stop on runlevel [016]
respawn
setuid <user>
chdir /home/<user>/ipython_notebooks
exec ipython notebook --pylab --profile=nbserver --ipython-dir=/home/<user>/.ipython --log-level=DEBUG
@creationix
creationix / run.js
Last active March 7, 2017 18:36
universal callback/continuable/thunk generator runner
function run(generator) {
// Pass in resume for no-wrap function calls
var iterator = generator(resume);
var data = null, yielded = false;
next();
check();
function next(item) {
var cont = iterator.next(item).value;
@patricksurry
patricksurry / README.md
Last active November 22, 2019 18:46
Illustrate embedding geojson/topojson geometry into a google maps overlay using d3.geo.projection.

Based on http://bl.ocks.org/mbostock/899711 which uses D3 to draw elements into a google maps overlay layer, but doesn't use d3.geo machinery to draw map geometry. This gist illustrates how to align a D3 mercator projection with google maps so we can do standard d3 mapping stuff on top of the google API.

@bomberstudios
bomberstudios / sketch-plugins.md
Last active February 26, 2024 07:02
A list of Sketch plugins hosted at GitHub, in no particular order.
@buritica
buritica / glossy.html
Last active August 29, 2015 14:01
Glossy JSON Spec
<script>
var namespace = 'onswipe';
var message = JSON.stringify({
view_url: '%%VIEW_URL_UNESC%%',
click_url: '%%CLICK_URL_UNESC%%%%DEST_URL%%',
cachebuster: '%%CACHEBUSTER%%',
glossy_id: 'my_id',
type: 'glossy',
pixels: []
});
/*
Swift Programming Language Guide
"A Swift Tour" Solutions
https://developer.apple.com/library/prerelease/ios/documentation/Swift/Conceptual/Swift_Programming_Language/GuidedTour.html#//apple_ref/doc/uid/TP40014097-CH2-XID_1
These are the solutions to all the "experiments" in the pre-release Swift Programming Guide
(released on the same day Apple announced Swift). A couple of things worth noting:
1. Swift syntax, e.g. array declarations, has changed since I releasd these. So this code will
probably cause some errors when you paste it into a playground. Should be easy enough to fix
@mathisonian
mathisonian / index.md
Last active August 10, 2024 20:59
requiring npm modules in the browser console

demo gif

The final result: require() any module on npm in your browser console with browserify

This article is written to explain how the above gif works in the chrome (and other) browser consoles. A quick disclaimer: this whole thing is a huge hack, it shouldn't be used for anything seriously, and there are probably much better ways of accomplishing the same.

Update: There are much better ways of accomplishing the same, and the script has been updated to use a much simpler method pulling directly from browserify-cdn. See this thread for details: mathisonian/requirify#5

inspiration

@d1manson
d1manson / images_in_pandas.md
Last active April 19, 2018 23:10
show images when displaying 2d arrays in iPython and Pandas

As of writing, this is only possible with my fork of pandas, but hopefully it will make its way into the main pandas stable branch.

The purpose of the fork is to let you specify a custom html formatter for individual columns in a data frame.


In this example we create a formatting function which takes a numpy array and returns a string of the form <img src='--base64-encoded-data'/>. This means that the numpy array is displayed as an image.

Below is the code we use to define our custom format function. Note that the function takes a single element from the data frame and returns and html string::

@bartvm
bartvm / dl-frameworks.rst
Last active December 7, 2020 18:18
A comparison of deep learning frameworks

A comparison of Theano with other deep learning frameworks, highlighting a series of low-level design choices in no particular order.

Overview

Symbolic: Theano, CGT; Automatic: Torch, MXNet

Symbolic and automatic differentiation are often confused or used interchangeably, although their implementations are significantly different.