Create a maximum entropy statistics distribution

In this example we are going to build maximum entropy statistics distribution, which yields ordered realizations:

import openturns as ot
import openturns.viewer as viewer
from matplotlib import pylab as plt

ot.Log.Show(ot.Log.NONE)

create a collection of distribution

coll = [ot.Beta(1.5, 1.7, 0.0, 1.0), ot.Beta(2.0, 2.3, 0.5, 1.2)]

create the distribution

distribution = ot.MaximumEntropyOrderStatisticsDistribution(coll)
print(distribution)

MaximumEntropyOrderStatisticsDistribution(collection = [Beta(alpha = 1.5, beta = 1.7, a = 0, b = 1),Beta(alpha = 2, beta = 2.3, a = 0.5, b = 1.2)])

draw a sample (ordered!)

distribution.getSample(10)

	X0	X1
0	0.6380705	0.7940242
1	0.3118801	0.8304515
2	0.4525162	0.6586167
3	0.6724817	0.8490304
4	0.2977258	0.9140666
5	0.1691129	0.9547993
6	0.3545962	0.9400742
7	0.8338034	0.8877587
8	0.1830482	0.8514765
9	0.5859367	0.8921155

draw PDF

graph = distribution.drawPDF()
view = viewer.View(graph)
plt.show()

/home/devel/project/build/python/src/site-packages/openturns/viewer.py:517: UserWarning: No contour levels were found within the data range.
  contourset = self._ax[0].contour(X, Y, Z, **contour_kw)