I'm trying something new today: a small experiment falling in between programming and scientific litterature.
I've been interested in "literate programming" for quite some time now, without going any further though. But I recently found the tools that would make it actually practicable and decided to dive in.
So, I've implemented a very small Python module that illustrates the notion of autocovariance and autocorrelation and the way to compute them via Fourier transforms:
The subject is not new at all, but such measures are something that I had to re-implement (for image processing puproses) more than I'd like to in past years, and each possible implementation having its own statistical biases, it always takes time to remember all the tricky details. So, this new document should at least help (me) quickly find all the relevant info.
This was also a subject underpinning a big part of my PhD work... a PhD that I defended 2 years ago, is there some "causation" here ?
For the technical side of things, the "tools" that helped me setup this "experiment" are essentially: