According to my revision control systems (rcs in those days), I’ve been
using gnuplot to make graphs since at least 2002. And I’ve got it set up via
a custom gnuplotrc to match the style of the TeX documents I often use the
graphs in.
At work we have an Instron 3369 machine for material testing. Recently, I wanted to visualize some tensile test data in ways that I couldn’t get into the test report.
If you would look over my github repositories, you’ll see that most of my programs are pretty small. And in general they are command-line applications. And that is good, because small is beautiful and simplicity is a virtue.
With Python it is relatively easy to make programs go faster by running things in parallel on multiple cores. This article shows you how.
We sill concentrate on a type of problem that is easy to parallelize.
While regular expressions can’t fully parse XML, they can be sufficient to extract data from it. In cases where the dataset is large and we are only interested in a small part of the data, this can be significantly faster than using a full XML parser.
Sometimes one has to convert numbers from and to different formats. Below are some examples in Python 3 that I …