#! /usr/bin/env python

from openturns import *
from math import *

TESTPREAMBLE()
RandomGenerator.SetSeed(0)

try:
    # 1D tests
    distribution = Dirac(0.7)
    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()
    sampleCovariance = oneSample.computeCovariance()[0, 0]
    if (fabs(sampleCovariance) < 1.0e-16):
        sampleCovariance = 0.0
    print "covariance=", sampleCovariance

    # Define a point
    point = NumericalPoint(distribution.getDimension(), 0.0)
    print "Point= ", point

    # Show PDF and CDF of point
    PDF = distribution.computePDF(point)
    print "pdf     =", PDF
    print "pdf (FD)=", (distribution.computeCDF(point + NumericalPoint(1, 0)) - distribution.computeCDF(point + NumericalPoint(1, -1)))
    CDF = distribution.computeCDF(point)
    print "cdf=", CDF

    # Define a point
    point = NumericalPoint(distribution.getSupport(distribution.getRange())[0])
    print "Point= ", point

    # Show PDF and CDF of point
    PDF = distribution.computePDF(point)
    print "pdf     =", PDF
    print "pdf (FD)=", (distribution.computeCDF(point + NumericalPoint(1, 0)) - distribution.computeCDF(point + NumericalPoint(1, -1)))
    CDF = distribution.computeCDF(point)
    print "cdf=", CDF
    CF = distribution.computeCharacteristicFunction(0.5)
    print "characteristic function= (%.12g%+.12gj)" % (CF.real, CF.imag)
    GF = distribution.computeGeneratingFunction(0.5 + 0.3j)
    print "generating function= (%.12g%+.12gj)" % (GF.real, GF.imag)
    quantile = distribution.computeQuantile(0.95)
    print "quantile=", quantile
    print "cdf(quantile)=", 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()

    # N-D tests

    dim = 4
    distribution = Dirac(NumericalPoint(dim, 2.3))
    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()
    sampleCovariance = oneSample.computeCovariance()
    for i in range(dim):
        for j in range(i + 1):
            if (fabs(sampleCovariance[i, j]) < 1.0e-16):
                sampleCovariance[i, j] = 0.0
    print "covariance=", sampleCovariance

    # Define a point
    point = NumericalPoint(dim, 0.0)
    print "Point= ", point

    # Show PDF and CDF of point
    PDF = distribution.computePDF(point)
    print "pdf     =", PDF
    print "pdf (FD)=", (distribution.computeCDF(point + NumericalPoint(dim, 0)) - distribution.computeCDF(point + NumericalPoint(dim, -1)))
    CDF = distribution.computeCDF(point)
    print "cdf=", CDF

    # Define a point
    point = NumericalPoint(distribution.getSupport(distribution.getRange())[0])
    print "Point= ", point

    # Show PDF and CDF of point
    PDF = distribution.computePDF(point)
    print "pdf     =", PDF
    print "pdf (FD)=", (distribution.computeCDF(point + NumericalPoint(dim, 0)) - distribution.computeCDF(point + NumericalPoint(dim, -1)))
    CDF = distribution.computeCDF(point)
    print "cdf=", CDF

    quantile = distribution.computeQuantile(0.95)
    print "quantile=", quantile
    print "cdf(quantile)=", 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_Dirac.py", sys.exc_type, sys.exc_value
