Add KDE functionality to hist and hist2d plots #33

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1. lukelbd

   added

   feature

   on Sep 6, 2019
2. lukelbd

   changed the title [-]Add KDE functionality to histograms, box/violin plots, hexbin-style plots[/-] [+]Add KDE functionality to hist and hist2d plots[/+] on Sep 6, 2019
3. bugsuse commented on Apr 19, 2021

   

   bugsuse

   on Apr 19, 2021

   Last edited by bugsuse

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   It's a very nice feature! I can hardly wait!
4. lukelbd

   added

   good first issue

   on Jul 12, 2021
5. lukelbd

   added this to the Version 0.8 milestone on Jul 21, 2021
6. lukelbd

   modified the milestones: Version 0.8, Version 0.9 on Aug 19, 2021
7. Jhsmit commented on Aug 22, 2021

   

   Jhsmit

   on Aug 22, 2021

   Last edited by Jhsmit

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   I made some custom KDE graphs for a publication recently:

   

   These type of graphs are called 'raincloudplots': https://wellcomeopenresearch.org/articles/4-63 (which has a python implementation but is based on seaborn and therefore has the same problems)
   The code for the KDE part of the graph is here: https://github.com/Jhsmit/PyHDX-paper/blob/master/biorxiv_v2/functions/rainbows.py

   Feel free to use the code in proplot if you find it useful (although some parts are from joyplot). I'm using scipy's kde function which mostly works fine but especially for the 2D case it can be slow if you have a lot of datapoints.
   I might want to try to find time to make a PR myself, I'm a big fan of proplot. I've started using it for my last publication and the subplot layout and sizing options in proplot really made my life a lot easier :) (paper / code)

   I'll try to provide some feedback to proplot if that helps you.
   PS. perhaps you could consider connecting your repository to zenodo such that the project can be cited.

   👍React with 👍1Reacted by Luke Davis
8. lukelbd commented on Aug 23, 2021

   

   lukelbd

   on Aug 23, 2021

   Last edited by lukelbd

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   Thanks for the code! This is a good base for adding KDE functionality -- I probably won't have time to work on this until later this year but happy to accept PRs if you feel inclined/want it sooner. Proplot's source code recently underwent some major improvements so it should be much easier to contribute.

   We probably want to add the following:

   1. Add a shared helper function at the top of axes/plot.py that controls KDE estimation for various plotting functions. Users should be able to pass keyword arguments to the KDE algorithm from the plotting functions.
   2. Add a violinplot option to plot a left- or right-half violin (like in your example), maybe with the argument side='left' and side='right' (or side='top' or side='bottom' for horizontal violins), with side='both' being the default.
   3. Rewrite violinplot to use your method for KDE estimation rather than matplotlib's method. It would probably simply call fill_between or fill_betweenx and then you can add outlines to the violins like you would any other patch. It would still be able to add error bars/boxes using the shared PlotAxes._apply_bar method.
   4. Add a raincloudplot method (with the shorthand raincloud, consistent with other plotting commands) as a thin wrapper that calls boxplot, violinplot, and scatter. It would call boxplot and violinplot with reduced default widths arguments and default side='left' or side='top' for the violins.
   5. Make violinplot have no colormap gradations by default, but let users add them by passing cmap='name' to violinplot or raincloudplot (it should also accept vmin and vmax arguments, but set the default vmin and vmax to the minimum and maximum of all the distributions). To implement colormap gradations, violinplot will set the facecolor of the patch to 'none' (i.e., completely transparent) so that an imshow can be drawn underneath the patch border and "clipped" by the border coordinates, as you've done in your code.
   6. Add kdeplot and kdeplot2d commands (with shorthands kde and kde2d, consistent with other functions) that show KDE estimations using lines and contours (respectively). They should be thin wrappers around plot and contour/contourf, similar to how hist and hist2d are thin wrappers around bar and pcolor.
   7. Add the ability to pass kde=True to hist and hist2d and this will draw the kde and kde2d lines on top of the histograms, analogous to the current ability of passing linewidth=N to contourf and proplot adds an additional contour plot on top of the filled contours. KDE-algorithm or KDE-styling keywords could be passed to hist and hist2d with kde_kw={key: value, ...}, analogous to various other arguments ending in _kw.
   8. Update the user guide with lots of examples! By the time all of these features are added we'd probably need a separate "Statistical plotting" section separate from the current "1d plotting" and "2d plotting" sections.

   And glad you find proplot useful :) it's already published on Zenodo but that probably wasn't clear -- there was just a Zenodo badge to the github home page. I've now added a link to the readthedocs homepage.