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#!/usr/bin/env python
import openturns as ot
import scipy.stats as st
import numpy as np
for scipy_dist in [
st.uniform(-1.0, 4.0),
st.johnsonsu(2.55, 2.25),
st.binom(10, 0.5),
st.poisson(0.6),
]:
np.random.seed(42)
# create an openturns distribution
py_dist = ot.SciPyDistribution(scipy_dist)
distribution = ot.Distribution(py_dist)
print("distribution=", distribution)
print("continuous?", distribution.isContinuous())
print("discrete?", distribution.isDiscrete())
print("integral?", distribution.isIntegral())
print("realization=", distribution.getRealization())
sample = distribution.getSample(10000)
print("sample=", sample[0:5])
print("pdf@0.1= %.6g" % (distribution.computePDF([0.1])))
point = [0.0]
print("pdf= %.6g" % distribution.computePDF(point))
cdf = distribution.computeCDF(point)
print("cdf= %.6g" % cdf)
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))
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())
print("range=", distribution.getRange())
if distribution.isDiscrete():
print("support=", distribution.getSupport())
parameter = distribution.getParameter()
print("parameter=", distribution.getParameter())
if len(parameter) > 0:
parameter[0] = 3.5
distribution.setParameter(parameter)
print("parameter=", distribution.getParameter())
print("parameterDesc=", distribution.getParameterDescription())
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