Matplotlib gurus: I took at stab at the git work flow and incorporated my personal modifications to the boxplot function. Github's diff can be found here: https://github.com/phobson/matplotlib/compare/master...manual_boxplots
In summary, if your data is MxN, you can manually specify medians and the confidence intervals around the medians using Nx1 and Nx2 arrays, respectively. Alternatively, you can use lists or tuples and use Nones if you want to specify those values only for some columns in your MxN data set. In other words, with an Mx5 data array, you can specify conf_intervals=[(ci1a,ci2a), (ci1b,ci2b), (ci1c,ci2c), None, (ci1e,ci2e)]. Within the conf_intervals "array", the CIs can be listed in any order as I use np.max() and np.min() to pull the upper and lower values, respectively. The motivation behind this is that sometimes I need the confidence levels to be different than 95%, and also that I compute those confidence intervals with a bootstrapping routine that is more robust than mpl-compatible one I submitted some time ago. I hope y'all find this to be a useful contribution. I'm an avid matplotlib user. It really is a wonderful tool. Cheers, paul h ------------------------------------------------------------------------------ Cloud Services Checklist: Pricing and Packaging Optimization This white paper is intended to serve as a reference, checklist and point of discussion for anyone considering optimizing the pricing and packaging model of a cloud services business. Read Now! http://www.accelacomm.com/jaw/sfnl/114/51491232/ _______________________________________________ Matplotlib-devel mailing list Matplotlib-devel@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-devel
