Options for smoothing histograms

[DamienIrving]

Python libraries for calculating your own kernel density estimate:
http://stackoverflow.com/questions/33274506/kernel-density-estimation-in-seaborn-for-cyclic-end-points

[DamienIrving]

Here's a nice summary of the Python libraries:
https://jakevdp.github.io/blog/2013/12/01/kernel-density-estimation/

and some other discussions
http://stats.stackexchange.com/questions/5960/how-to-identify-a-bimodal-distribution

[DamienIrving]

A problem with the pre-existing KDE packages is that they don't let you specify weights like the numpy.histogram function does. (This is especially problematic when you need to weight by volume.) This post describes the problem:
http://blog.technariumas.lt/post/111695107866/weighted-kde-in-python

The people who develop these packages seem to be aware of the problem (e.g. see this issue), but at the moment there's a work around that someone has written:
http://nbviewer.jupyter.org/gist/tillahoffmann/f844bce2ec264c1c8cb5
http://stackoverflow.com/questions/27623919/weighted-gaussian-kernel-density-estimation-in-python

(The other solution suggested in the Stack Overflow post is PyQT-Fit, however the code hasn't been updated for 2 years and when I pip installed it I got error upon importing it.)