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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