#! /usr/bin/env python

import openturns as ot
import openturns.testing as ott

ot.TESTPREAMBLE()


# Instantiate one distribution object
distribution = ot.Hypergeometric(25, 7, 14)
print("Distribution ", repr(distribution))
print("Distribution ", distribution)

# Is this distribution elliptical ?
print("Elliptical = ", distribution.isElliptical())

# Is this distribution continuous ?
print("Continuous = ", distribution.isContinuous())

# Test for realization of distribution
oneRealization = distribution.getRealization()
print("oneRealization=", oneRealization)

# Define a point
point = ot.Point(distribution.getDimension(), 5.0)
print("Point= ", point)

# Show PDF and CDF of point
LPDF = distribution.computeLogPDF(point)
print("log pdf= %.12g" % LPDF)
PDF = distribution.computePDF(point)
print("pdf     = %.12g" % PDF)

CDF = distribution.computeCDF(point)
print("cdf= %.12g" % CDF)
CCDF = distribution.computeComplementaryCDF(point)
print("ccdf= %.12g" % CCDF)
CF = distribution.computeCharacteristicFunction(point[0])
print("characteristic function= (%.12g%+.12gj)" % (CF.real, CF.imag))
LCF = distribution.computeLogCharacteristicFunction(point[0])
print("log characteristic function= (%.12g%+.12gj)" % (LCF.real, LCF.imag))
GF = distribution.computeGeneratingFunction(0.3 + 0.7j)
print("generating function= (%.12g%+.12gj)" % (GF.real, GF.imag))
LGF = distribution.computeLogGeneratingFunction(0.3 + 0.7j)
print("log generating function= (%.12g%+.12gj)" % (LGF.real, LGF.imag))
quantile = distribution.computeQuantile(0.95)
print("quantile=", quantile)
print("cdf(quantile)= %.12g" % distribution.computeCDF(quantile))
print("entropy=%.6f" % distribution.computeEntropy())
mean = distribution.getMean()
print("mean=", mean)
standardDeviation = distribution.getStandardDeviation()
print("standard deviation=", standardDeviation)
skewness = distribution.getSkewness()
print("skewness=", skewness)
kurtosis = distribution.getKurtosis()
print("kurtosis=", kurtosis)
covariance = distribution.getCovariance()
print("covariance=", covariance)
parameters = distribution.getParametersCollection()
print("parameters=", parameters)
print("Standard representative=", distribution.getStandardRepresentative())

# should not hang when the range is [0] (dirac)
ot.Hypergeometric(25, 0, 0).computeScalarQuantile(0.6)

ot.Log.Show(ot.Log.TRACE)
ot.RandomGenerator.SetSeed(1)
validation = ott.DistributionValidation(distribution)
validation.run()