mourner

// Lefalet shortcuts for common tile providers - is it worth adding such 1.5kb to Leaflet core?

L.TileLayer.Common = L.TileLayer.extend({
	initialize: function (options) {
		L.TileLayer.prototype.initialize.call(this, this.url, options);
	}
});

(function () {
	

zziuni

# source : http://code.google.com/p/natvpn/source/browse/trunk/stun_server_list
# A list of available STUN server.

stun.l.google.com:19302
stun1.l.google.com:19302
stun2.l.google.com:19302
stun3.l.google.com:19302
stun4.l.google.com:19302
stun01.sipphone.com
stun.ekiga.net

Kingson

#! /usr/bin/env python
# coding=utf-8
__author__ = 'jszhou'
from bottle import *
import hashlib
import xml.etree.ElementTree as ET
import urllib2
# import requests
import json

turnersr

#Non-mathematical Introductions

• http://gcn.com/articles/2014/01/09/topographical-data-analysis.aspx
• https://www.simonsfoundation.org/quanta/20131004-the-mathematical-shape-of-things-to-come/

#Videos

• http://www.youtube.com/channel/UCThuKLGcSXhBJ5GlwzHobJw
• http://www.youtube.com/watch?v=kctyag2Xi8o
• http://www.youtube.com/watch?v=x3Hl85OBuc0
• http://www.youtube.com/playlist?list=PL6763F57A61FE6FE8
• http://techtalks.tv/talks/topological-data-analysis-part-i/58123/

mitmul

# install miniconda
cd
wget http://repo.continuum.io/miniconda/Miniconda-3.5.5-Linux-armv6l.sh
md5sum Miniconda-3.5.5-Linux-armv6l.sh
bash Miniconda-3.5.5-Linux-armv6l.sh -b
rm -rf Miniconda-3.5.5-Linux-armv6l.sh
echo 'export PATH=/home/pi/miniconda/bin:$PATH' >> .bashrc
source .bashrc
conda install pip --yes
conda install ipython --yes

frozeman

# METEOR CORE:

Anywhere: Meteor.isClient
Anywhere: Meteor.isServer
Anywhere: Meteor.startup(func)
Anywhere: Meteor.absoluteUrl([path], [options])
Anywhere: Meteor.settings
Anywhere: Meteor.release

jsdf

var React = require('react-native')
var {
  View,
  Text,
  LinkingIOS,
  StyleSheet,
} = React

// you might want to compile these two as standalone umd bundles
// using `browserify --standalone` and `derequire` 

derricw

import numpy as np

def bin_ndarray(ndarray, new_shape, operation='sum'):
    """
    Bins an ndarray in all axes based on the target shape, by summing or
        averaging.

    Number of output dimensions must match number of input dimensions.

    Example

bogger

name: "GoogleNet"

input: "data"
input_dim: 10
input_dim: 3
input_dim: 224
input_dim: 224

# hierarchy 1
# conv -> relu -> pool -> lrn

howdydoody123

Servo serv;
int pos = 0;

void setup() {
    serv.attach(D0);
    Spark.function("setpos", setPos);
    Spark.variable("getpos", &pos, INT);
}

void loop() {