#! /usr/bin/env python

from __future__ import print_function
from openturns import *

TESTPREAMBLE()
RandomGenerator.SetSeed(0)

try:
    all_cases = [GeneralizedExtremeValue(2.0, 1.5, -0.15),
                 GeneralizedExtremeValue(2.0, 1.5,  0.0),
                 GeneralizedExtremeValue(2.0, 1.5,  0.15)]
    for i in range(len(all_cases)):
        distribution = all_cases[i]
        print("#" * 50)
        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())
        size = 100
        for i in range(2):
            print(
                "Kolmogorov test for the generator, sample size=", size, " is ",
                  FittingTest.Kolmogorov(distribution.getSample(size), distribution).getBinaryQualityMeasure())
            size *= size

        # Define a point
        point = [1.0] * distribution.getDimension()
        print("Point= ", point)

        # Show PDF and CDF of point
        eps = 1e-5
        DDF = distribution.computeDDF(point)
        print("ddf     =", DDF)
        print(
            "ddf (FD)=", (distribution.computePDF(point[0] + eps) - distribution.computePDF(point[0] - eps)) / (2.0 * eps))
        LPDF = distribution.computeLogPDF(point)
        print("log pdf=", LPDF)
        PDF = distribution.computePDF(point)
        print("pdf     =", PDF)
        print(
            "pdf (FD)=", (distribution.computeCDF(point[0] + eps) - distribution.computeCDF(point[0] - eps)) / (2.0 * eps))
        CDF = distribution.computeCDF(point)
        print("cdf=", CDF)
        CCDF = distribution.computeComplementaryCDF(point)
        print("ccdf=", CCDF)
        Survival = distribution.computeSurvivalFunction(point)
        print("survival=", Survival)
        InverseSurvival = distribution.computeInverseSurvivalFunction(0.95)
        print("Inverse survival=", InverseSurvival)
        print("Survival(inverse survival)=",
              distribution.computeSurvivalFunction(InverseSurvival))
        PDFgr = distribution.computePDFGradient(point)
        print("pdf gradient     =", PDFgr)
        PDFgrFD = Point(3)
        PDFgrFD[0] = (GeneralizedExtremeValue(distribution.getMu() + eps, distribution.getSigma(), distribution.getXi()).computePDF(point) -
                      GeneralizedExtremeValue(distribution.getMu() - eps, distribution.getSigma(), distribution.getXi()).computePDF(point)) / (2.0 * eps)
        PDFgrFD[1] = (GeneralizedExtremeValue(distribution.getMu(), distribution.getSigma() + eps, distribution.getXi()).computePDF(point) -
                      GeneralizedExtremeValue(distribution.getMu(), distribution.getSigma() - eps, distribution.getXi()).computePDF(point)) / (2.0 * eps)
        PDFgrFD[2] = (GeneralizedExtremeValue(distribution.getMu(), distribution.getSigma(), distribution.getXi() + eps).computePDF(point) -
                      GeneralizedExtremeValue(distribution.getMu(), distribution.getSigma(), distribution.getXi() - eps).computePDF(point)) / (2.0 * eps)
        print("pdf gradient (FD)=", PDFgrFD)
        CDFgr = distribution.computeCDFGradient(point)
        print("cdf gradient     =", CDFgr)
        CDFgrFD = Point(3)
        CDFgrFD[0] = (GeneralizedExtremeValue(distribution.getMu() + eps, distribution.getSigma(), distribution.getXi()).computeCDF(point) -
                      GeneralizedExtremeValue(distribution.getMu() - eps, distribution.getSigma(), distribution.getXi()).computeCDF(point)) / (2.0 * eps)
        CDFgrFD[1] = (GeneralizedExtremeValue(distribution.getMu(), distribution.getSigma() + eps, distribution.getXi()).computeCDF(point) -
                      GeneralizedExtremeValue(distribution.getMu(), distribution.getSigma() - eps, distribution.getXi()).computeCDF(point)) / (2.0 * eps)
        CDFgrFD[2] = (GeneralizedExtremeValue(distribution.getMu(), distribution.getSigma(), distribution.getXi() + eps).computeCDF(point) -
                      GeneralizedExtremeValue(distribution.getMu(), distribution.getSigma(), distribution.getXi() - eps).computeCDF(point)) / (2.0 * eps)
        print("cdf gradient (FD)=", CDFgrFD)
        quantile = distribution.computeQuantile(0.95)
        print("quantile=", quantile)
        print("cdf(quantile)=", distribution.computeCDF(quantile))
        print("entropy=%.6f" % distribution.computeEntropy())
        # Confidence regions
        prob, threshold = distribution.computeMinimumVolumeIntervalWithMarginalProbability(
            0.95)
        print("Minimum volume interval=", prob)
        print("threshold=", threshold)
        levelSet, beta = distribution.computeMinimumVolumeLevelSetWithThreshold(
            0.95)
        print("Minimum volume level set=", levelSet)
        print("beta=", beta)
        interval, beta = distribution.computeBilateralConfidenceIntervalWithMarginalProbability(
            0.95)
        print("Bilateral confidence interval=", interval)
        print("beta=", beta)
        interval, beta = distribution.computeUnilateralConfidenceIntervalWithMarginalProbability(
            0.95, False)
        print("Unilateral confidence interval (lower tail)=", interval)
        print("beta=", beta)
        interval, beta = distribution.computeUnilateralConfidenceIntervalWithMarginalProbability(
            0.95, True)
        print("Unilateral confidence interval (upper tail)=", interval)
        print("beta=", beta)
        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)
        correlation = distribution.getCorrelation()
        print("correlation=", correlation)
        spearman = distribution.getSpearmanCorrelation()
        print("spearman=", spearman)
        kendall = distribution.getKendallTau()
        print("kendall=", kendall)
        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())
        print("mu=", distribution.getMu())
        print("sigma=", distribution.getSigma())
        print("xi=", distribution.getXi())
        print("Actual distribution=", distribution.getActualDistribution())
        distribution.setActualDistribution(
            distribution.getActualDistribution())
        print("Distribution from actual distribution=", distribution)
except:
    import sys
    print("t_GeneralizedExtremeValue_std.py",
          sys.exc_info()[0], sys.exc_info()[1])
