5 Tips For Starting Responsive Web Design

According to recent analysis by ComScore the number of mobile users will surpass the number of desktop users this year. This means it is becoming vital that your website is smartphone friendly.

I’ve recently redesigned my website to make it easy to use on desktops, tablets, and smartphones by using responsive web design (RWD): the website layout changes depending on your screen size. In this post I’m going to share a few of the tips I found helpful.


Responsive webite for the Manchester University Maths Dept. Left: Desktop. Right: Mobile.

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Overview of the NA-HPC Workshop at UCL, April 2014

The NA-HPC Network

The NA-HPC Network is one of the groups funded by EPSRC Network Grants tasked with supporting the interaction and collaboration between numerical analysts, computer scientists, developers, and users of HPC systems within the UK.

Run by Nick Higham and David Silvester at Manchester the network has run a number of events over its 3 year lifespan. This post contains my highlights of the recent meeting at UCL, details of which can be found here.

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Software Carpentry – The Highlights

The Software Sustainability Institute, Mathworks, and the Software Carpentry group recently collaborated to run a course at Manchester University. The event was designed to teach best practices in software engineering to young researchers and mainly focused on three points:

  • the command line and shell scripting (mainly in Bash).
  • version control, and in particular Git.
  • data manipulation, unit testing, and performance considerations in MATLAB.

In this post I’ll highlight what I took away from the course and give links to some useful information.

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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.

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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.

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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).

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