#! /usr/bin/env python

from __future__ import print_function
from openturns import *

TESTPREAMBLE()
RandomGenerator.SetSeed(0)

try:
    for xi in [-0.2, 0.0, 0.2]:
        # Instanciate one distribution object
        distribution = GeneralizedPareto(1.5, xi)

        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=", repr(oneRealization))

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

        size = 100
        for i in range(2):
            msg = ''
            if FittingTest.Kolmogorov(distribution.getSample(size), distribution).getBinaryQualityMeasure():
                msg = "accepted"
            else:
                msg = "rejected"
            print(
                "Kolmogorov test for the generator, sample size=", size, " is", msg)
            size *= 10

        # Define a point
        point = Point(distribution.getDimension(), 1.0)
        print("Point= ", repr(point))

        # Show PDF and CDF of point
        eps = 1e-5
        # DDF = distribution.computeDDF( point )
        # print "ddf     =", repr(DDF)
        print("ddf (FD)=", repr(Point(1, (distribution.computePDF(
            point + Point(1, eps)) - distribution.computePDF(point + Point(1, -eps))) / (2.0 * eps))))
        PDF = distribution.computePDF(point)
        print("pdf     = %.12g" % PDF)
        print("pdf (FD)= %.9f" % ((distribution.computeCDF(point + Point(1, eps)) -
                                   distribution.computeCDF(point + Point(1, -eps))) / (2.0 * eps), ))
        CDF = distribution.computeCDF(point)
        print("cdf= %.12g" % CDF)
        # CF = distribution.computeCharacteristicFunction( point[0] )
        # print "characteristic function=", CF
        PDFgr = distribution.computePDFGradient(point)
        print("pdf gradient     =", repr(PDFgr))
        PDFgrFD = Point(2)
        PDFgrFD[0] = (GeneralizedPareto(distribution.getSigma() + eps, distribution.getXi()).computePDF(point) -
                      GeneralizedPareto(distribution.getSigma() - eps, distribution.getXi()).computePDF(point)) / (2.0 * eps)
        PDFgrFD[1] = (GeneralizedPareto(distribution.getSigma(), distribution.getXi() + eps).computePDF(point) -
                      GeneralizedPareto(distribution.getSigma(), distribution.getXi() - eps).computePDF(point)) / (2.0 * eps)
        print("pdf gradient (FD)=", repr(PDFgrFD))
        CDFgr = distribution.computeCDFGradient(point)
        print("cdf gradient     =", repr(CDFgr))
        CDFgrFD = Point(2)
        CDFgrFD[0] = (GeneralizedPareto(distribution.getSigma() + eps, distribution.getXi()).computeCDF(point) -
                      GeneralizedPareto(distribution.getSigma() - eps, distribution.getXi()).computeCDF(point)) / (2.0 * eps)
        CDFgrFD[1] = (GeneralizedPareto(distribution.getSigma(), distribution.getXi() + eps).computeCDF(point) -
                      GeneralizedPareto(distribution.getSigma(), distribution.getXi() - eps).computeCDF(point)) / (2.0 * eps)
        print("cdf gradient (FD)=", repr(CDFgrFD))
        quantile = distribution.computeQuantile(0.95)
        print("quantile=", repr(quantile))
        print("cdf(quantile)=", distribution.computeCDF(quantile))
        # Get 95% survival function
        inverseSurvival = Point(
            distribution.computeInverseSurvivalFunction(0.95))
        print("InverseSurvival=", repr(inverseSurvival))
        print("Survival(inverseSurvival)=%.6f" %
              distribution.computeSurvivalFunction(inverseSurvival))

        # Confidence regions
        interval, threshold = distribution.computeMinimumVolumeIntervalWithMarginalProbability(
            0.95)
        print("Minimum volume interval=", interval)
        print("threshold=", Point(1, threshold))
        levelSet, beta = distribution.computeMinimumVolumeLevelSetWithThreshold(
            0.95)
        print("Minimum volume level set=", levelSet)
        print("beta=", Point(1, beta))
        interval, beta = distribution.computeBilateralConfidenceIntervalWithMarginalProbability(
            0.95)
        print("Bilateral confidence interval=", interval)
        print("beta=", Point(1, beta))
        interval, beta = distribution.computeUnilateralConfidenceIntervalWithMarginalProbability(
            0.95, False)
        print("Unilateral confidence interval (lower tail)=", interval)
        print("beta=", Point(1, beta))
        interval, beta = distribution.computeUnilateralConfidenceIntervalWithMarginalProbability(
            0.95, True)
        print("Unilateral confidence interval (upper tail)=", interval)
        print("beta=", Point(1, beta))

        mean = distribution.getMean()
        print("mean=", repr(mean))
        standardDeviation = distribution.getStandardDeviation()
        print("standard deviation=", repr(standardDeviation))
        skewness = distribution.getSkewness()
        print("skewness=", repr(skewness))
        kurtosis = distribution.getKurtosis()
        print("kurtosis=", repr(kurtosis))
        covariance = distribution.getCovariance()
        print("covariance=", repr(covariance))
        parameters = distribution.getParametersCollection()
        print("parameters=", repr(parameters))
        for i in range(6):
            try:
                value = distribution.getStandardMoment(i)
                print("standard moment n=", i, " value=", value)
            except RuntimeError as ex:
                print(ex)
        print("Standard representative=",
              distribution.getStandardRepresentative())

except:
    import sys
    print("t_GeneralizedPareto_std.py", sys.exc_info()[0], sys.exc_info()[1])
