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#!/usr/bin/env python
import openturns as ot
import chaospy as cp
import numpy as np
# A chaospy Triangle distribution
d0 = cp.Triangle(2.0, 3.5, 4.0)
d1 = cp.Kumaraswamy(2.0, 3.0, -1.0, 4.0)
d2 = cp.J(d0, d1)
for chaospy_dist in [d0, d1, d2]:
np.random.seed(42)
# create an openturns distribution
py_dist = ot.ChaospyDistribution(chaospy_dist)
distribution = ot.Distribution(py_dist)
print("distribution=", distribution)
print("realization=", distribution.getRealization())
sample = distribution.getSample(10000)
print("sample=", sample[0:5])
point = [2.6] * distribution.getDimension()
print("pdf= %.6g" % distribution.computePDF(point))
cdf = distribution.computeCDF(point)
print("cdf= %.6g" % cdf)
print("mean=", distribution.getMean())
print("mean(sampling)=", sample.computeMean())
print("std=", distribution.getStandardDeviation())
print("std(sampling)=", sample.computeStandardDeviation())
print("skewness=", distribution.getSkewness())
print("skewness(sampling)=", sample.computeSkewness())
print("kurtosis=", distribution.getKurtosis())
print("kurtosis(sampling)=", sample.computeKurtosis())
if len(chaospy_dist) == 1:
for i in [1, 2, 3, 4]:
print("moment(" + str(i) + ")=", distribution.getMoment(i))
print("range=", distribution.getRange())
if len(chaospy_dist) == 1:
print("quantile=", distribution.computeQuantile(cdf))
print("quantile (tail)=", distribution.computeQuantile(cdf, True))
print("scalar quantile=%.6g" % distribution.computeScalarQuantile(cdf))
print(
"scalar quantile (tail)=%.6g"
% distribution.computeScalarQuantile(cdf, True)
)
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