jakevdp

ericdill

import json
import glob
import os
from os.path import join, basename
# install this with "conda install -c conda-forge python-graphviz"
import graphviz as gv

# path to your conda environment
path = os.environ.get('CONDA_PREFIX')
if path is None:

nathanleclaire

#!/usr/bin/env python

"""
Example Usage:
$ ansible -i machine.py machinename -m ping
"""


import argparse
import subprocess

minrk

#!/bin/sh

set -e

gh="https://github.com/"

repos="
ipython/traitlets
ipython/ipython
jupyter/jupyter_core

gmr

module("resty.consul", package.seeall)

_VERSION = '0.1.0'

function service_nodes(service)
  local http = require "resty.http"
  local json = require "cjson"
  local hc = http:new()

  local upstream = ""

staltz

The introduction to Reactive Programming you've been missing

(by @andrestaltz)

---

This tutorial as a series of videos

If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.

---

agramfort

"""
Implementation of pairwise ranking using scikit-learn LinearSVC

Reference: "Large Margin Rank Boundaries for Ordinal Regression", R. Herbrich,
    T. Graepel, K. Obermayer.

Authors: Fabian Pedregosa <[email protected]>
         Alexandre Gramfort <[email protected]>
"""

fabianp

"""
Implementation of pairwise ranking using scikit-learn LinearSVC

Reference: 

    "Large Margin Rank Boundaries for Ordinal Regression", R. Herbrich,
    T. Graepel, K. Obermayer 1999

    "Learning to rank from medical imaging data." Pedregosa, Fabian, et al., 
    Machine Learning in Medical Imaging 2012.

ncode

#!/usr/bin/env python
from gevent import monkey 
monkey.patch_all()
import sys
import redis
import urllib2
import traceback
from cgi import escape

# -- gsdproxy 

superchink

# Usage: show <local-port> <subdomain>
function show() {
    DOMAIN=".webhostology.com"
    REMOTE="$2$DOMAIN"
    ssh -tR 8080:127.0.0.1:$1 vps "sudo ssh -Nl \$USER -L $REMOTE:80:127.0.0.1:8080 localhost"
}