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. 

Please stop using colormaps which don't translate well to grayscale

I recently printed a paper with very nice results in B&W but the color images simply did not make sense when printed in grayscale (here is the paper if you are curious). Why? Not the best choice of colormap. Jake Vanderplas reminded me of this issue with his very nice blog post.

Please don't choose colormaps for the images in your paper which do not translate well when printed in grayscale.

Check out Nature's advice on color coding. Also check out the advice here.

Feel free to suggest other useful references about this issue in the comments.

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.