ODSC Europe Super Early Bird Sale!

This deal has timed out, but the next deal might just around the corner, or find a way to contact us about writing a blog and we'll talk. See you at ODSC East!

Get 75% Off until Friday at 11pm

Philip Elson

Philip Elson

Technical Lead & Expert Scientific Python Developer at Met Office

Bio: Specialties: Software (development, maintenance and support), Scientific software (Python/SciPy, Fortran, C++), HPC (benchmarking, optimisation, cluster / distributed compute, compilers, MPI), Data analysis & visualisation, Technical writing, Statistics

Classifying segmented strokes as characters – Part 3 of an XKCD font saga

Classifying segmented strokes as characters – Part 3 of an ...

In part two of my XKCD font saga I was able to separate strokes from the XKCD handwriting dataset into many smaller images. I also handled the easier cases of merging some of the strokes back together – I particularly focussed on “dotty” or “liney” type glyphs, such as i, !, % and =. Now […]

Segment, extract, and combine features of an image with SciPy and scikit-image – Part 2 of an XKCD font saga

Segment, extract, and combine features of an image with SciPy and...

In part one of XKCD font saga I gave some background on the XKCD handwriting dataset, and took an initial look at image segmentation in order to extract the individual strokes from the scanned image. In this installment, I will apply the technique from part 1, as well as attempting to merge together strokes to […]

Playing with Randall Munroe’s XKCD handwriting

Playing with Randall Munroe’s XKCD handwriting

The XKCD font (as used by matplotlib et al.) got an update to include lower-case characters. For some time now I have been aware of a handwriting sample produced by Randall Munroe (XKCD’s creator) that I was interested in exploring. The ultimate aim is to automatically produce a font-file using open source tools, and to […]

Dealing with arrays which are bigger than memory – an introduction to biggus

Dealing with arrays which are bigger than memory – an intro...

I often deal with huge gridded datasets which either stretch or indeed are beyond the limits of my computer’s memory. In the past I’ve implemented a couple of workarounds to help me handle this data to extract meaningful analyses from them. One of the most intuitive ways of reducing gridded datasets is through indexing/slicing and […]