Yes, that seems reasonable. @elmar you seem to have a pretty good grasp of
the code and the use case, would you mind taking a crack at adding those
special cases?
Tom
On Sat, Jul 4, 2015 at 8:58 AM elmar werling <el...@net4werling.de> wrote:
> having a look at seaborns ViolinPlotter class
> (https://github.com/mwaskom/seaborn/blob/master/seaborn/categorical.py),
> they explicit handle the special case of "no data" and "single unique
> datapoint" at line 580 ff.
>
> Could something similar be added to matplotlibs violinplot?
>
> On 04.07.2015 12:28, elmar werling wrote:
> > from an end user point of view, matplotlibs violinplot should just do
> > the same as seaborns violinplot.
> >
> >
> > #####################################################################
> > import numpy as np
> > import matplotlib.pyplot as plt
> > import seaborn as sns
> >
> > N = 100
> > y1 = np.random.randn(N) + 3.0
> > y2 = np.random.randn(N) * 5.0 + 50
> > y3 = np.ones(N) * 100 # causing plt.violinplot problem
> > y4 = np.arange(0) # causing plt.violinplot problem
> >
> > #plt.violinplot([y1, y2, y3, y4])
> >
> > sns.violinplot(data=[y1, y2, y3, y4])
> >
> >
> > On 03.07.2015 17:52, Thomas Caswell wrote:
> >> The KDE computation code is a copy of the KDE code from scipy
> >> (https://github.com/scipy/scipy/blob/master/scipy/stats/kde.py), I
> >> suggest raising this issue on their mailing list/github.
> >>
> >> I strongly suspect that violin plot should be doing data sanitation on
> >> the way in or catching exceptions like this, but I am not familiar
> >> enough with the math to be sure what it should do instead.
> >>
> >> Tom
> >>
> >> On Fri, Jul 3, 2015 at 11:41 AM elmar werling
> >> <el...@net4werling.de
> >> <mailto:el...@net4werling.de>> wrote:
> >>
> >>
> >> Hi all,
> >>
> >> violinplot is crashing with singular matrix data. See example.
> >>
> >> Is this behaviour for a singular matrix intended or just a bug?
> >>
> >> Cheers
> >> Elmar
> >>
> >>
> >>
> >> #####################################################
> >> import numpy as np
> >> import matplotlib.pyplot as plt
> >>
> >> # data mimicing the
> >> # original cumsum data (may sum up to 100)
> >> N = 100
> >> y1 = np.random.randn(N) + 3.0
> >> y2 = np.random.randn(N) * 5.0 + 50
> >> y3 = np.ones(N) * 100 # data set causing violinplot problem
> >>
> >> plt.violinplot([y1, y2, y3])
> >>
> >> plt.boxplot([y1, y2, y3]) # ok
> >> plt.ylim(0,110)
> >>
> >> #####################################################
> >>
> >> OS: Debian
> >> Anaconda 2.3.0 (64-bit)
> >> Python 2.7.10
> >> numpy 2.3.0
> >> matplotlib 1.4.3
> >>
> >>
> >>
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