#! /usr/bin/env python

from openturns import *

TESTPREAMBLE()
RandomGenerator.SetSeed(0)

try:
    # Instanciate one distribution object
    distribution = Skellam(10.0, 5.0)
    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

    # Test for sampling
    size = 10000
    oneSample = distribution.getSample(size)
    print "oneSample first=", oneSample[0], " last=", oneSample[size - 1]
    print "mean=", oneSample.computeMean()
    print "covariance=", oneSample.computeCovariance()

    # Define a point
    point = NumericalPoint(distribution.getDimension(), 12.0)
    print "Point= ", point

    # Show PDF and CDF of point
    eps = 1e-5
    LPDF = distribution.computeLogPDF(point)
    print "log pdf= %.12g" % LPDF
    PDF = distribution.computePDF(point)
    print "pdf     = %.12g" % PDF
    print "pdf (FD)= %.12g" % (distribution.computeCDF(point + NumericalPoint(1, 0)) - distribution.computeCDF(point + NumericalPoint(1, -1)))
    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)
    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
    for i in range(6):
        print "standard moment n=", i, ", value=", distribution.getStandardMoment(i)
    print "Standard representative=", distribution.getStandardRepresentative()

except:
    import sys
    print "t_Skellam_std.py", sys.exc_type, sys.exc_value
