Recent blog entries for oubiwann

28 Nov 2014 (updated 28 Nov 2014 at 20:13 UTC) »

Scientific Computing with Hy and IPython

This blog post is a bit different than other technical posts I've done in the past in that the majority of the content is not on the blog in or gists; instead, it is in an IPython notebook. Having adored Mathematica back in the 90s, you can imagine how much I love the IPython Notebook app. I'll have more to say on that at a future date.

I've been doing a great deal of NumPy and matplotlib again lately, every day for hours a day. In conjunction with the new features in Python 3, this has been quite a lot of fun -- the most fun I've had with Python in years (thanks Guido, et al!). As you might have guessed, I'm also using it with Erlang (specifically, LFE), but that too is for a post yet to come.

With all this matplotlib and numpy work in standard Python, I've been going through Lisp withdrawals and needed to work with it from a fresh perspective. Needless to say, I had an enormous amount of fun doing this. Naturally, I decided to share with folks how one can do the latest and greatest with the tools of Python scientific computing, but in the syntax of the Python community's best kept secret: Clojure-Flavoured Python (Github, Twitter, Wikipedia).

Spoiler: observed data and
polynomial curve fitting
Looking about for ideas, I decided to see what Clojure's Incanter project had for tutorials, and immediately found what I was looking for: Linear regression with higher-order terms, a 2009 post by David Edgar Liebke.

Nearly every cell in the tutorial notebook is in Hy, and for that we owe a huge thanks to yardsale8 for his Hy IPython magics code. For those that love Python and Lisp equally, who are familiar with the ecosystems' tools, Hy offers a wonderful option for being highly productive with a language supporting Lisp- and Clojure-style macros. You can get your work done, have a great time doing it, and let that inner code artist out!

(In fact, I've started writing a macro for one of the examples in the tutorial, offering a more Lisp-like syntax for creating class methods. We'll see what Paul Tagliamonte has to say about it when it's done ... !)

If you want to check out the notebook code and run it locally, just do the following:

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