I wrote a Python implementation of the Bayesian Blocks algorithm. There’s already a good implementation out in public from Jake Vanderplas, but it had some issues that I’ll briefly mention below. I’m being a bad member of the community by writing a new version instead of improving the existing one, and I feel bad about that, but I needed to get something going quickly, and I might as well put it out there.
Bayesian Blocks algorithms are nice for binning X-ray lightcurves into sections of different count rates; or, you can think of them as providing dynamic binning for histogram analysis. J. Scargle has been the main person developing the theory, and has put together a very nice detailed paper explaining the approach in 2013 ApJ 764 167. The paper’s written in a Reproducible Research style, coming with code to reproduce all the figures — which is really awesome, but something to talk about another day.
The Scargle implementation is in Matlab, with an IDL implementation mentioned but accidentally not provided, as far as I can tell. Jake Vanderplas’ code in the AstroML package seems to be the main Python implementation. It’s very nice, but has a few issues with “time-tagged” events, which is Scargle’s term for the X-ray-style case of binning up rates from individual Poisson events with precise timing. In particular, the AstroML code doesn’t let you specify the start and stop times of the observation, which can have huge semantic consequences — e.g., if you observed two events at times 1.0 and 2.0, your interpretation is very different depending on whether you observed from times 0.0 to 3.0 or times -1000 to +1000. The Vanderplas code also doesn’t implement iteration on the “p0″ parameter or bootstrap-based assessment of bin height uncertainties, as suggested by Scargle.
My implementation has these features. It also has a fix for a mistake in the Scargle paper: equation 21 has a right-hand side of “4 – [term]” when it should be “4 – log [term]“. The new module is here on GitHub, and a script for doing a blocks analysis on a Chandra events file is here. Docstrings, tests, etc, are lacking, because as mentioned before I’m a bad community member. But if you want to fool around with the code, there it is.