How to setup R using conda

Recently I’ve been working with some of the statistics staff at the University of Manchester on sports analytics. Specifically we’ve been looking for useful models in football data. People from this background normally use R to analyze data and fit models.

Normally I would use Python for this kind of task but, since there was already a considerable amount of code in R, it made sense for me to do some work in R. The people at Continuum Analytics (who make the brilliant Anaconda Python distribution) recently announced support for R using their package manager conda. However, it wasn’t easy to find instructions to get a fully working environment, so here is what I did.

Continue reading “How to setup R using conda”

Advertisements

Using implicit matrices in Python

There are lots of new features in SciPy 0.13 (release notes) but for me the most important are the updated matrix functions in scipy.linalg and the one norm estimator in scipy.sparse.linalg.

In some of my recent research (related to section 4 of this) I’ve needed to estimate the one norm of a large (n^4 x n^2) dense matrix without computing each element. All we can assume is the ability to compute matrix-vector products (via some rather complicated function), meaning we only know the entries of the matrix implicitly.

Continue reading “Using implicit matrices in Python”

My Favourite Emacs Packages

After a few years doing my PhD I’ve settled on Emacs as my main editor. This is mainly because it is incredibly powerful and extendable, there are thousands of free packages available to use and everything can be tweaked exactly the way I want.

The price to pay for this freedom is a fairly steep learning curve and the need to get familiar with Emacs Lisp (for customizing packages). However Emacs has been around since 1976 and is still under active development so it must have something going for it!

In this post I’ve described some of what I consider the most useful packages to get started with Emacs. For a guide on how to install Emacs packages in the latest version see this page.

Continue reading “My Favourite Emacs Packages”

Emacs and Anaconda Python

Recently I posted a blog about using Emacs and Python together. It was about having setting in your .emacs file allowing Emacs to interact with the default Python and SciPy/NumPy from the Ubuntu repositories, which is out of date. After changing Python version to Anaconda Python this broke all my Emacs setup! It was a real pain to fix so I’ve collected the main ideas here.

Anaconda Python

To use the latest versions of the Python packages like SciPy and NumPy I downloaded the Anaconda Python distribution. It includes lots of the most useful Python packages for scientific computing by default such as

  • Numba and Cython for speeding up code.
  • SymPy for symbolic computation.
  • F2Py for converting Fortran code.
  • IPython for a better interactive experience.
  • mpi for parallel computing.
  • PEP8 and PyFlakes to enforce coding standards.

I’d really recommend it as an easy way to get a good Python install. You can also pay extra for upgrades like linking NumPy with MKL for enhanced speed (which is free for academics).

Continue reading “Emacs and Anaconda Python”

Emacs and Python

Currently at Manchester we’re debating whether to try and use more open source software like Python within our research. I’d never used Python before but whilst learning it I wanted to configure it for use in Emacs.

Unfortunately I couldn’t find a guide to setting up Emacs for scientific computing. However there is a lot of information out there (references at the end). This post will hopefully bring together the basic information needed to get a working Python setup in Emacs for computational scientists.

Continue reading “Emacs and Python”