Introducing eand.py

One month now that I’ve been in Heraklion, Greece, and joined the Computer Vision and Robotics Laboratory of FORTH-ICS as a visiting researcher until September. Of course many things to say, also about the life in Crete, but I shall leave the best for future posts and rather tell about annoying, computer science and math-related stuff :)

It’s been some time, now, that I’ve recurrently been using open-source software, not so much for philosophical reasons but rather for efficiency over cost ratio (which is not necessarily infinite if you consider cost as ergonomics alongside with price). Recently I’ve also switched from Windows 7 to full Unix-like solutions and finally stuck to Lubuntu, an operating system based on Ubuntu, except lightweight and really fast (and ugly, but that’s part of the deal).

Anyway, now that I will be regularly producing programs as part of my research, I’ve chosen to release all sources publicly when possible (i.e. basically when no licensing or top-secret issue comes in the way). I’ve created a GitHub repository for the occasion, and by the way, my thanks go to AmmarkoV for the help and tips (check out his website, it’s cool and geeky). First contribution is called eand.py (Easy Algebraic Numerical Differentiation for Python) and consists primarily in an implementation of a derivative estimator introduced in a recent article by Mboup et al. (2009) which you can download and read here.

Easy Algebraic Numerical Differentiation for Python

Noisy signal (left) and first-order derivative estimate using eand.py (right)

Although the theoretical framework is relatively dense, I included some demonstration files to try to make things easy to visualize. The whole package is released under the GNU GPL license. Of course, contributions, comments and suggestions of improvement are most welcome.

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