#! /usr/bin/env python

import openturns as ot
import openturns.testing as ott

ot.TESTPREAMBLE()

# Instantiate one distribution object
dim = 3
R = ot.CorrelationMatrix(dim)
for i in range(dim - 1):
    R[i, i + 1] = 0.25

copula = ot.NormalCopula(R)
print("Copula ", repr(copula))
print("Copula ", copula)
print("Mean ", repr(copula.getMean()))
print("Covariance ", repr(copula.getCovariance()))

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

# Is this copula elliptical ?
print("Elliptical copula= ", copula.hasEllipticalCopula())

# Is this copula independent ?
print("Independent copula= ", copula.hasIndependentCopula())

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

# Test for sampling
size = 10
oneSample = copula.getSample(size)
print("oneSample=", repr(oneSample))

# Define a point
point = ot.Point(dim, 0.2)

# Show PDF and CDF of point
pointPDF = copula.computePDF(point)
pointCDF = copula.computeCDF(point)
print("Point = ", repr(point), " pdf=%.6f" % pointPDF, " cdf=%.6f" % pointCDF)

# Get 50% quantile
quantile = copula.computeQuantile(0.5)
print("Quantile=", repr(quantile))
print("CDF(quantile)=%.6f" % copula.computeCDF(quantile))
# Get 95% survival function
inverseSurvival = ot.Point(copula.computeInverseSurvivalFunction(0.95))
print("InverseSurvival=", repr(inverseSurvival))
print(
    "Survival(inverseSurvival)=%.6f" % copula.computeSurvivalFunction(inverseSurvival)
)
print("entropy=%.6f" % copula.computeEntropy())
# Confidence regions
if copula.getDimension() <= 2:
    threshold = ot.Point()
    print(
        "Minimum volume interval=", copula.computeMinimumVolumeInterval(0.95, threshold)
    )
    print("threshold=", threshold)
    beta = ot.Point()
    levelSet = copula.computeMinimumVolumeLevelSet(0.95, beta)
    print("Minimum volume level set=", levelSet)
    print("beta=", beta)
    print(
        "Bilateral confidence interval=",
        copula.computeBilateralConfidenceInterval(0.95, beta),
    )
    print("beta=", beta)
    print(
        "Unilateral confidence interval (lower tail)=",
        copula.computeUnilateralConfidenceInterval(0.95, False, beta),
    )
    print("beta=", beta)
    print(
        "Unilateral confidence interval (upper tail)=",
        copula.computeUnilateralConfidenceInterval(0.95, True, beta),
    )
    print("beta=", beta)

# Covariance and correlation
covariance = copula.getCovariance()
print("covariance=", covariance)
correlation = copula.getCorrelation()
print("correlation=", correlation)
spearman = copula.getSpearmanCorrelation()
print("spearman=", spearman)
kendall = copula.getKendallTau()
print("kendall=", kendall)
x = 0.6
y = [0.2] * (dim - 1)
print("conditional PDF=%.6f" % copula.computeConditionalPDF(x, y))
print("conditional CDF=%.6f" % copula.computeConditionalCDF(x, y))
print("conditional quantile=%.6f" % copula.computeConditionalQuantile(x, y))
pt = ot.Point([0.1 * i + 0.05 for i in range(dim)])
print("sequential conditional PDF=", copula.computeSequentialConditionalPDF(point))
resCDF = copula.computeSequentialConditionalCDF(pt)
print("sequential conditional CDF(", pt, ")=", resCDF)
print(
    "sequential conditional quantile(",
    resCDF,
    ")=",
    copula.computeSequentialConditionalQuantile(resCDF),
)

# Extract the marginals
for i in range(dim):
    margin = copula.getMarginal(i)
    print("margin=", repr(margin))
    print("margin PDF=%.6f" % margin.computePDF(ot.Point(1, 0.25)))
    print("margin CDF=%.6f" % margin.computeCDF(ot.Point(1, 0.25)))
    print("margin quantile=", repr(margin.computeQuantile(0.95)))
    print("margin realization=", repr(margin.getRealization()))

# Extract a 2-D marginal
indices = [1, 0]
print("indices=", repr(indices))
margins = copula.getMarginal(indices)
print("margins=", repr(margins))
print("margins PDF=%.6f" % margins.computePDF(ot.Point(2, 0.25)))
print("margins CDF=%.6f" % margins.computeCDF(ot.Point(2, 0.25)))
quantile = ot.Point(margins.computeQuantile(0.95))
print("margins quantile=", repr(quantile))
print("margins CDF(qantile)=%.6f" % margins.computeCDF(quantile))
print("margins realization=", repr(margins.getRealization()))

# Creation of the correlation matrix from a Spearman correlation matrix
spearman = ot.CorrelationMatrix(dim)
for i in range(1, dim):
    spearman[i, i - 1] = 0.25

correlation = ot.NormalCopula.GetCorrelationFromSpearmanCorrelation(spearman)
print(
    "Normal copula correlation=",
    repr(correlation),
    " from the Spearman correlation=",
    repr(spearman),
)

ot.Log.Show(ot.Log.TRACE)
validation = ott.DistributionValidation(copula)
validation.run()

# computeProbability
spearman_corr = ot.CorrelationMatrix([[1.0, 0.74], [0.74, 1.0]])
copula = ot.NormalCopula(
    ot.NormalCopula.GetCorrelationFromSpearmanCorrelation(spearman_corr)
)
interval = ot.Interval([0.958722, 0.902063], [1.0, 1.0])
prob = copula.computeProbability(interval)
print("prob=%.6f" % prob)

print(ot.NormalCopula(1).getParametersCollection())

# normal quantile inf propagation bug in iso transfo
R = ot.CorrelationMatrix(2)
R[0, 1] = 0.2
copula = ot.NormalCopula(R)
standard_list = [ot.Uniform(), ot.Beta()]
distribution = ot.JointDistribution(standard_list, copula)
standard = ot.JointDistribution(standard_list)
transformation = ot.DistributionTransformation(distribution, standard)
standard_sample = transformation(ot.Normal(distribution.getDimension()).getSample(10))
print(standard_sample)