def kde(data, bins=None, kernel=None, **kwargs):
""" kde(a, bins=None, range=None, **kwargs)
Make a kernerl density estimate plot of the data. This is like a
histogram, but produces a smoother result, thereby better represening
the probability density function.
See the vv.StatData for more statistics on data.
Parameters
----------
a : array_like
The data to calculate the historgam of.
bins : int (optional)
The number of bins. If not given, the best number of bins is
determined automatically using the Freedman-Diaconis rule.
kernel : float or sequence (optional)
The kernel to use for distributing the values. If a scalar is given,
a Gaussian kernel with a sigma equal to the given number is used.
If not given, the best kernel is chosen baded on the number of bins.
kwargs : keyword arguments
These are given to the plot function.
"""
# Get stats
from visvis.processing.statistics import StatData
stats = StatData(data)
# Get kde
xx, values = stats.kde(bins, kernel)
# Plot
return vv.plot(xx, values, **kwargs)
def kde(data, bins=None, kernel=None, **kwargs):
""" kde(a, bins=None, range=None, **kwargs)
Make a kernerl density estimate plot of the data. This is like a
histogram, but produces a smoother result, thereby better represening
the probability density function.
See the vv.StatData for more statistics on data.
Parameters
----------
a : array_like
The data to calculate the historgam of.
bins : int (optional)
The number of bins. If not given, the best number of bins is
determined automatically using the Freedman-Diaconis rule.
kernel : float or sequence (optional)
The kernel to use for distributing the values. If a scalar is given,
a Gaussian kernel with a sigma equal to the given number is used.
If not given, the best kernel is chosen baded on the number of bins.
kwargs : keyword arguments
These are given to the plot function.
"""
# Get stats
from visvis.processing.statistics import StatData
stats = StatData(data)
# Get kde
xx, values = stats.kde(bins, kernel)
# Plot
return vv.plot(xx, values, **kwargs)
box.SetWhiskers(value)
return locals()
if __name__ == '__main__':
vv.figure(1)
vv.clf()
a = vv.gca()
d1 = np.random.normal(1, 4, (1000, 1000))
d2 = np.random.normal(2, 3, (20, ))
d3 = np.random.uniform(-1, 3, (100, ))
d4 = [
1, 2, 1, 2.0, 8, 2, 3, 1, 2, 2, 3, 2, 2.1, 8, 8, 8, 8, 8, 1.2, 1.3, 0,
0, 1.5, 2
]
b = boxplot((d1, d2, d3, d4), width=0.8, whiskers='violin')
##
dd = d4
stat = StatData(dd)
bins1, values1 = stat.histogram_np(normed=True)
bins2, values2 = stat.histogram()
bins3, values3 = stat.kde()
vv.figure(2)
vv.clf()
vv.bar(bins2, values2) #, lc='r', ms='.', mc='r')
vv.plot(bins3, values3) #, lc='g', ms='.', mc='g')
vv.plot(bins1, values1, lc='b', ms='.', mc='b', ls=':', mw=4)
#print abs(bins1-bins2).sum()
""" Get/Set the style of the whiskers.
"""
def fget(self):
return self._boxes[0]._whiskers
def fset(self, value):
for box in self._boxes:
box.SetWhiskers(value)
return locals()
if __name__ == '__main__':
vv.figure(1); vv.clf()
a = vv.gca()
d1 = np.random.normal(1, 4, (1000,1000))
d2 = np.random.normal(2, 3, (20,))
d3 = np.random.uniform(-1, 3, (100,))
d4 = [1,2,1,2.0, 8, 2, 3, 1, 2, 2, 3, 2, 2.1, 8, 8, 8, 8, 8, 1.2, 1.3, 0, 0, 1.5, 2]
b = boxplot((d1,d2,d3, d4), width=0.8, whiskers='violin')
##
dd = d4
stat = StatData(dd)
bins1, values1 = stat.histogram_np(normed=True)
bins2, values2 = stat.histogram()
bins3, values3 = stat.kde( )
vv.figure(2); vv.clf()
vv.bar(bins2, values2)#, lc='r', ms='.', mc='r')
vv.plot(bins3, values3)#, lc='g', ms='.', mc='g')
vv.plot(bins1, values1, lc='b', ms='.', mc='b', ls=':', mw=4)
#print abs(bins1-bins2).sum()
