nrollr

# UPDATED 17 February 2019
# Redirect all HTTP traffic to HTTPS
server {
   listen 80;
   listen [::]:80; 	
   server_name www.domain.com domain.com;
   return 301 https://$host$request_uri;
}

# SSL configuration

stuaxo

# modified from https://gist.github.com/schlamar/2311116#file-processify-py-L17
# also see http://stackoverflow.com/questions/2046603/is-it-possible-to-run-function-in-a-subprocess-without-threading-or-writing-a-se

import inspect
import os
import sys
import traceback

from functools import wraps
from multiprocessing import Process, Queue

ctokheim

Stacked and Grouped Bar Plot

Oddly enough ggplot2 has no support for a stacked and grouped (position="dodge") bar plot. The seaborn python package, although excellent, also does not provide an alternative. However, I knew it was surely possible to make such a plot in regular matplotlib. Matplotlib, although sometimes clunky, gives you enough flexibility to precisely place plotting elements which is needed for a stacked and grouped bar plot.

Below is a working example of making a stacked and grouped bar plot.

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np

vertexclique

MacOS

Build 3059

MD5: 59bab8f71f8c096cd3f72cd73851515d

Rename it to: Sublime Text

Make it executable with: chmod u+x Sublime\ Text

panzi

// "License": Public Domain
// I, Mathias Panzenböck, place this file hereby into the public domain. Use it at your own risk for whatever you like.
// In case there are jurisdictions that don't support putting things in the public domain you can also consider it to
// be "dual licensed" under the BSD, MIT and Apache licenses, if you want to. This code is trivial anyway. Consider it
// an example on how to get the endian conversion functions on different platforms.

#ifndef PORTABLE_ENDIAN_H__
#define PORTABLE_ENDIAN_H__

#if (defined(_WIN16) || defined(_WIN32) || defined(_WIN64)) && !defined(__WINDOWS__)

aras-p

// Just before switching jobs:
// Add one of these.
// Preferably into the same commit where you do a large merge.
//
// This started as a tweet with a joke of "C++ pro-tip: #define private public",
// and then it quickly escalated into more and more evil suggestions.
// I've tried to capture interesting suggestions here.
//
// Contributors: @r2d2rigo, @joeldevahl, @msinilo, @_Humus_,
// @YuriyODonnell, @rygorous, @cmuratori, @mike_acton, @grumpygiant,

iamatypeofwalrus

What

Roll your own iPython Notebook server with Amazon Web Services (EC2) using their Free Tier.

What are we using? What do you need?

• An active AWS account. First time sign-ups are eligible for the free tier for a year
• One Micro Tier EC2 Instance
• With AWS we will use the stock Ubuntu Server AMI and customize it.
• Anaconda for Python.
• Coffee/Beer/Time

seberg

def rolling_window(array, window=(0,), asteps=None, wsteps=None, axes=None, toend=True):
    """Create a view of `array` which for every point gives the n-dimensional
    neighbourhood of size window. New dimensions are added at the end of
    `array` or after the corresponding original dimension.
    
    Parameters
    ----------
    array : array_like
        Array to which the rolling window is applied.
    window : int or tuple

schlamar

import os
import sys
import traceback
from functools import wraps
from multiprocessing import Process, Queue


def processify(func):
    '''Decorator to run a function as a process.
    Be sure that every argument and the return value

minrk

import time
import random

from IPython import parallel

# create client & view
rc = parallel.Client()
dv = rc[:]

# scatter 'id', so id=0,1,2 on engines 0,1,2