Parallel computing with IPython and Python

I uploaded to github a quick tutorial on how to parallelize easy computing tasks. I have chosen embarrassingly parallel examples which illustrate some of the powerful features of IPython.parallel and the multiprocessing module.

Examples included:

1. Parallel function mapping to a list of arguments (multiprocessing module)
2. Parallel execution of array function (scatter/gather) + parallel execution of scripts
3. Easy parallel Monte Carlo (parallel magics)

Parallel computing with Python. 

Python Installation instructions (including IPython / IPython Notebook)

This page describes how to install Python and the other packages (Numpy, Scipy, IPython, IPython Notebook, Matplotlib) required for the course for Mac OS X, Linux and Windows.

Linux

In Linux, the installation instructions are pretty straightforward. Assuming that you are running Debian or Ubuntu, you just need to execute the following command in the terminal:

sudo apt-get install python-numpy python-scipy python-matplotlib ipython-notebook

For Fedora users, you can use the yum tool.

Mac OS X, Linux, Windows

We recommend downloading and installing the Anaconda Python distribution. The installations instructions are available here. 

Just download the installer and execute it with bash.

Anaconda includes most of the packages we will use and it is pretty easy to install additional packages if required, using the conda or pip command-line tools.

If the above two methods do not work for OS X

The MacPorts way

You can try installing everything using MacPorts. First download and install macports and then issue the following command in a terminal:

sudo port install py27-zmq py27-tornado py27-nose

The avove dependencies are required in order to run IPython notebook. Then run:

sudo port install py27-numpy py27-matplotlib py27-scipy py27-ipython

The advantage of this method is that it easy to do. The downsides:

• It can take a couple of hours to finish the installation depending on your machine and internet connection, since macports will download and compile everything as it goes. 
• If you like having the bleeding edge versions, note that it can take a while for them to be released on macports 
• Finally, macports can create conflicts between different python interpreters installed in your system

Using Apple’s Python interpreted and pip

If you feel adventurous, you can use Apple’s builtin python interpreter and install everything using pip. Please follow the instructions described in this blog.

If you run into trouble

Leave a comment here with the issue you found.

Frequentism and Bayesianism

Jake VanderPlas has been writing a series of posts discussing frequentism and bayesianism. They are well-written, clear and insightful and use IPython for the statistical analysis. Here, I compiled his posts on the topic for convenience.

Frequentism and Bayesianism: A Practical Introduction
where he synthesizes the philosophical and pragmatic aspects of the frequentist and Bayesian approaches as they relate to the analysis of scientific data.

Frequentism and Bayesianism II: When Results Differ
where he discusses the difference between frequentist and Bayesian in the treatment of nuisance parameters.

Frequentism and Bayesianism III: Confidence, Credibility, and why Frequentism and Science do not Mix
where he discusses the subtle difference between frequentist confidence intervals and Bayesian credible intervals.

Frequentism and Bayesianism IV: How to be a Bayesian in Python
where he describes how to do Bayesian statistics in python with emcee, PyMC and PyStan.

Interactive plots in ipython notebooks

A couple of ways of doing interactive plots and animations in an ipython notebook:

• Using plotly
• Using a little hack called ipywidgets

I am sure there are many other ways, and if you know of nice ways please post in the comments. 

Links via reddit.

Citing IPython in papers

If you feel like citing IPython in your papers, you can actually cite the following paper by Pérez & Granger (2007).

You can find the appropriate bibtex citation here.

Easily run fortran code from an IPython notebook cell

I just learned a potentially very useful trick for people running fortran code. By following the instructions on this link, you can copy and paste fortran code to an IPython notebook cell and then use a ipython magic command to run it on-the-fly. I haven't had time to test it yet, so if you have success with it, please let me know. Kudos to @fperez_org.

On a separate note, IPython 1.1 is out!

Update (Sep. 23rd 2013): Here is a tutorial on how to use the fortran IPython magic.

Parallel Bayes with IPython

Excellent tutorial from the folks at Continuum Analytics/Wakari on how to parallelize bayesian parameter estimation using IPython (IPcluster) and pyMC.

Following this tutorial, I was able to make my bayesian spectral fitting code (for astronomical purposes) parallel -- and much faster -- very quickly.

Bayesian Estimation with PyMC and IPCluster

Easy way of installing Python and scientific packages for OS X (non-academic users): Anaconda

Introduction

Mac OS X users know that getting python and its scientific packages (numpy, scipy, ipython etc) installed properly can be a tricky job.

If you are affiliated with a university, you can try the freely available and easy to install Enthought distribution (if you are not affiliated with a university, you can still get the Enthought dsitribution for free but you are stuck with 32 bits binaries).

One way of getting things installed properly (even if you are not affiliated with a university) is by using the native OS X python interpreter and installing everything via pip as I describe in this post. This method is a little tricky but works well for me. One advantage is that it is easy to install new packages in this way: just use pip.

Another way is using macports. This is relatively straightforward, but can take a long time to compile all the dependencies (hours) and, worse of all, can potentially create conflicts between different libraries and different python interpreters.

Anaconda

I recently came across an easy way of getting python and the scientific packages installed. It also provides a convenient framework for installing extra packages. This is the Anaconda python distribution provided by Continuum Analytics. Anaconda provides the most popular packages: numpy, scipy, ipython (+notebook) and even more - astropy, spyder, pandas etc. And 64 bits binaries! For everybody. For free.

How to install it? 

Pretty easy. Go to the downloads page. Download the installer for your operating system. After downloading the file, for OS X you just need to issue the command

 sh <downloaded file.sh>  

Piece of cake. You can choose to let the installer change your PATH variable. After that, when you invoke python or ipython, it will automatically call the appropriate binaries and you will have available all the important packages.

To install additional packages, you can use their conda package manager or the usual pip.

Great gallery of IPython notebooks

You know you can create IPython notebooks "a la Mathematica" and put them in your webpage, right? That's a great way of sharing a step-by-step analysis workflow with someone.

I stumbled upon a great collection of IPython notebooks that illustrate a vast range of capabilities: data analysis, statistics, interfacing with C/Fortran, plots, numpy tricks and a lot more!

You should check it out: A gallery of interesting IPython notebooks at github (via @profjsb).