//                                               -*- C++ -*-
/**
 *  @brief The GeneralizedPareto distribution
 *
 *  Copyright 2005-2024 Airbus-EDF-IMACS-ONERA-Phimeca
 *
 *  This library is free software: you can redistribute it and/or modify
 *  it under the terms of the GNU Lesser General Public License as published by
 *  the Free Software Foundation, either version 3 of the License, or
 *  (at your option) any later version.
 *
 *  This library is distributed in the hope that it will be useful,
 *  but WITHOUT ANY WARRANTY; without even the implied warranty of
 *  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *  GNU Lesser General Public License for more details.
 *
 *  You should have received a copy of the GNU Lesser General Public License
 *  along with this library.  If not, see <http://www.gnu.org/licenses/>.
 *
 */
#include <cmath>
#include "openturns/GeneralizedPareto.hxx"
#include "openturns/SpecFunc.hxx"
#include "openturns/RandomGenerator.hxx"
#include "openturns/PersistentObjectFactory.hxx"
#include "openturns/Distribution.hxx"

BEGIN_NAMESPACE_OPENTURNS

CLASSNAMEINIT(GeneralizedPareto)

static const Factory<GeneralizedPareto> Factory_GeneralizedPareto;

/* Default constructor */
GeneralizedPareto::GeneralizedPareto()
  : ContinuousDistribution()
  , sigma_(1.0)
  , xi_(0.0)
  , u_(0.0)
{
  setName( "GeneralizedPareto" );
  setDimension( 1 );
  computeRange();
}

/* Parameters constructor */
GeneralizedPareto::GeneralizedPareto(const Scalar sigma,
                                     const Scalar xi,
                                     const Scalar u)
  : ContinuousDistribution()
  , sigma_(0.0)
  , xi_(xi)
  , u_(u)
{
  setName( "GeneralizedPareto" );
  // We set the dimension of the GeneralizedPareto distribution
  setDimension( 1 );
  // This call set also the range.
  setSigma(sigma);
}

/* Parameters constructor */
GeneralizedPareto::GeneralizedPareto(const Pareto & pareto)
  : ContinuousDistribution()
  , sigma_(0.0)
  , xi_(1.0 / pareto.getAlpha())
  , u_(pareto.getGamma() + pareto.getBeta())
{
  setName( "GeneralizedPareto" );
  // We set the dimension of the GeneralizedPareto distribution
  setDimension( 1 );
  // This call set also the range.
  setSigma(pareto.getBeta() / pareto.getAlpha());
}

/* Comparison operator */
Bool GeneralizedPareto::operator ==(const GeneralizedPareto & other) const
{
  if (this == &other) return true;
  return (sigma_ == other.sigma_) && (xi_ == other.xi_) && (u_ == other.u_);
}

Bool GeneralizedPareto::equals(const DistributionImplementation & other) const
{
  const GeneralizedPareto* p_other = dynamic_cast<const GeneralizedPareto*>(&other);
  return p_other && (*this == *p_other);
}

/* String converter */
String GeneralizedPareto::__repr__() const
{
  OSS oss;
  oss << "class=" << GeneralizedPareto::GetClassName()
      << " name=" << getName()
      << " dimension=" << getDimension()
      << " sigma=" << sigma_
      << " xi=" << xi_
      << " u=" << u_;
  return oss;
}

String GeneralizedPareto::__str__(const String & ) const
{
  OSS oss;
  oss << getClassName() << "(sigma = " << sigma_ << ", xi=" << xi_ << ", u=" << u_ << ")";
  return oss;
}

/* Virtual constructor */
GeneralizedPareto * GeneralizedPareto::clone() const
{
  return new GeneralizedPareto(*this);
}

/* Compute the numerical range of the distribution given the parameters values */
void GeneralizedPareto::computeRange()
{
  const Point lowerBound(1, u_);
  const Interval::BoolCollection finiteLowerBound(1, true);
  Point upperBound(1, 0.0);
  Interval::BoolCollection finiteUpperBound(1, false);
  if (xi_ >= 0.0) upperBound[0] = computeScalarQuantile(1.0 - SpecFunc::Precision);
  else
  {
    upperBound[0] = u_ - sigma_ / xi_;
    finiteUpperBound[0] = true;
  }
  setRange(Interval(lowerBound, upperBound, finiteLowerBound, finiteUpperBound));
}

/* Get one realization of the distribution */
Point GeneralizedPareto::getRealization() const
{
  const Scalar u = RandomGenerator::Generate();
  if (xi_ == 0.0) return Point(1, u_ - sigma_ * std::log(u));
  return Point(1, u_ + sigma_ * expm1(-xi_ * std::log(u)) / xi_);
}


/* Get the DDF of the distribution */
Point GeneralizedPareto::computeDDF(const Point & point) const
{
  if (point.getDimension() != 1) throw InvalidArgumentException(HERE) << "Error: the given point must have dimension=1, here dimension=" << point.getDimension();
  const Scalar z = (point[0] - u_) / sigma_;
  Point result(1);
  if (z < 0.0) return result;
  if ((xi_ < 0.0) && (z >= -1.0 / xi_)) return result;
  if (std::abs(xi_) < 1.0e-8)
  {
    result[0] = -std::exp(-z) * (1.0 + xi_ * (1.0 - z * (2.0 - 0.5 * z))) / (sigma_ * sigma_);
    return result;
  }
  result[0] = -(1.0 + xi_) * std::exp(-(2.0 + 1.0 / xi_) * log1p(xi_ * z)) / (sigma_ * sigma_);
  return result;
}


/* Get the PDF of the distribution */
Scalar GeneralizedPareto::computePDF(const Point & point) const
{
  if (point.getDimension() != 1) throw InvalidArgumentException(HERE) << "Error: the given point must have dimension=1, here dimension=" << point.getDimension();
  const Scalar z = (point[0] - u_) / sigma_;
  if (z < 0.0) return 0.0;
  if ((xi_ < 0.0) && (z >= -1.0 / xi_)) return 0.0;
  if (std::abs(xi_) < 1.0e-8) return std::exp(-z) * (1.0 - xi_ * z * (1.0 - 0.5 * z)) / sigma_;
  return std::exp(-(1.0 + 1.0 / xi_) * log1p(xi_ * z)) / sigma_;
}

Scalar GeneralizedPareto::computeLogPDF(const Point & point) const
{
  if (point.getDimension() != 1) throw InvalidArgumentException(HERE) << "Error: the given point must have dimension=1, here dimension=" << point.getDimension();
  const Scalar z = (point[0] - u_) / sigma_;
  if (z < 0.0) return SpecFunc::LowestScalar;
  if ((xi_ < 0.0) && (z >= -1.0 / xi_)) return SpecFunc::LowestScalar;
  if (std::abs(xi_) < 1.0e-8) return -z + log1p(-xi_ * z * (1.0 - 0.5 * z)) - std::log(sigma_);
  return -(1.0 + 1.0 / xi_) * log1p(xi_ * z) - std::log(sigma_);
}

/* Get the CDF of the distribution */
Scalar GeneralizedPareto::computeCDF(const Point & point) const
{
  if (point.getDimension() != 1) throw InvalidArgumentException(HERE) << "Error: the given point must have dimension=1, here dimension=" << point.getDimension();
  const Scalar z = (point[0] - u_) / sigma_;
  if (z <= 0.0) return 0.0;
  if ((z == SpecFunc::MaxScalar) || ((xi_ < 0.0) && (z > -1.0 / xi_))) return 1.0;
  if (std::abs(xi_) < 1.0e-8) return -expm1(-z) - 0.5 * xi_ * z * z * std::exp(-z);
  return -expm1(-log1p(xi_ * z) / xi_);
}

/* Get the complementary CDF of the distribution */
Scalar GeneralizedPareto::computeComplementaryCDF(const Point & point) const
{
  if (point.getDimension() != 1) throw InvalidArgumentException(HERE) << "Error: the given point must have dimension=1, here dimension=" << point.getDimension();
  const Scalar z = (point[0] - u_) / sigma_;
  if (z <= 0.0) return 1.0;
  if (std::abs(std::sqrt(xi_) * z) < 1.0e-8) return std::exp(-z) * (1.0 + 0.5 * xi_ * z * z);
  if ((xi_ < 0.0) && (z > -1.0 / xi_)) return 0.0;
  return std::exp(-log1p(xi_ * z) / xi_);
}

/** Get the product minimum volume interval containing a given probability of the distribution */
Interval GeneralizedPareto::computeMinimumVolumeIntervalWithMarginalProbability(const Scalar prob, Scalar & marginalProb) const
{
  return computeUnilateralConfidenceIntervalWithMarginalProbability(prob, false, marginalProb);
}

/** Get the minimum volume level set containing a given probability of the distribution */
LevelSet GeneralizedPareto::computeMinimumVolumeLevelSetWithThreshold(const Scalar prob, Scalar & threshold) const
{
  const Interval interval(computeMinimumVolumeInterval(prob));
  Function minimumVolumeLevelSetFunction(MinimumVolumeLevelSetEvaluation(clone()).clone());
  minimumVolumeLevelSetFunction.setGradient(MinimumVolumeLevelSetGradient(clone()).clone());
  Scalar minusLogPDFThreshold = -computeLogPDF(interval.getUpperBound()[0]);
  threshold = std::exp(-minusLogPDFThreshold);
  return LevelSet(minimumVolumeLevelSetFunction, LessOrEqual(), minusLogPDFThreshold);
}

/* Compute the entropy of the distribution */
Scalar GeneralizedPareto::computeEntropy() const
{
  return std::log(sigma_) + xi_ + 1.0;
}

/* Get the characteristic function of the distribution, i.e. phi(u) = E(exp(I*u*X)) */
Complex GeneralizedPareto::computeCharacteristicFunction(const Scalar x) const
{
  if (xi_ == 0.0) return 1.0 / Complex(1.0, -x);
  return DistributionImplementation::computeCharacteristicFunction(x);
}

Complex GeneralizedPareto::computeLogCharacteristicFunction(const Scalar x) const
{
  if (xi_ == 0.0) return -std::log(Complex(1.0, -x));
  return std::log(computeCharacteristicFunction(x));
}

/* Get the PDFGradient of the distribution */
Point GeneralizedPareto::computePDFGradient(const Point & point) const
{
  if (point.getDimension() != 1) throw InvalidArgumentException(HERE) << "Error: the given point must have dimension=1, here dimension=" << point.getDimension();
  Point result(3);
  const Scalar z = (point[0] - u_) / sigma_;
  if (z < 0.0) return result;
  const Scalar xiZ = xi_ * z;
  if ((xi_ < 0.0) && (xiZ <= -1.0)) return result;
  if (std::abs(xi_) <= 1.0e-8)
  {
    const Scalar expFactor = std::exp(-z) / sigma_;
    result[0] = (z - 1.0) * expFactor / sigma_ * (1.0 + 0.5 * xiZ * (z - 4.0));
    result[1] = z * expFactor * (0.5 * (z - 2.0) + xiZ * z * (24.0 + z * (-20.0 + z * 3.0)) / 12.0);
    result[2] = expFactor / sigma_ * (1.0 + 0.5 * xi_ * (2.0 + z * (-4.0 + z)));
  }
  else
  {
    const Scalar logTerm = log1p(xiZ);
    const Scalar expFactor = std::exp(-(2.0 + 1.0 / xi_) * logTerm) / sigma_;
    result[0] = (z - 1.0) * expFactor / sigma_;
    result[1] = ((1.0 + xiZ) * logTerm - xiZ * (1.0 + xi_)) * expFactor / (xi_ * xi_);
    result[2] = (1.0 + xi_) * expFactor / sigma_;
  }
  return result;
}

/* Get the CDFGradient of the distribution */
Point GeneralizedPareto::computeCDFGradient(const Point & point) const
{
  if (point.getDimension() != 1) throw InvalidArgumentException(HERE) << "Error: the given point must have dimension=1, here dimension=" << point.getDimension();

  Point result(3);
  const Scalar z = (point[0] - u_) / sigma_;
  if (z < 0.0) return result;
  const Scalar xiZ = xi_ * z;
  if ((xi_ < 0.0) && (xiZ <= -1.0)) return result;
  if (std::abs(xi_) <= 1.0e-8)
  {
    const Scalar expFactor = -std::exp(-z);
    result[0] = z * expFactor * (1 + 0.5 * xiZ * (z - 2.0)) / sigma_;
    result[1] = 0.5 * z * z * expFactor * (1.0 + xiZ * (-8.0 + 3.0 * z) / 6.0);
    result[2] = expFactor * (1.0 + 0.5 * xiZ * (-2.0 + z)) / sigma_;
  }
  else
  {
    const Scalar logTerm = log1p(xiZ);
    const Scalar expFactor = -std::exp(-(1.0 + 1.0 / xi_) * logTerm);
    result[0] = z * expFactor / sigma_;
    result[1] = ((1.0 + xiZ) * logTerm - xiZ) * expFactor / (xi_ * xi_);
    result[2] = expFactor / sigma_;
  }
  return result;
}

/* Get the quantile of the distribution */
Scalar GeneralizedPareto::computeScalarQuantile(const Scalar prob,
    const Bool tail) const
{
  if (!((prob >= 0.0) && (prob <= 1.0)))
    throw InvalidArgumentException(HERE) << "computeScalarQuantile expected prob to belong to [0,1], but is " << prob;
  const Scalar logProb = tail ? std::log(prob) : log1p(-prob);
  if (xi_ == 0.0) return u_ - sigma_ * logProb;
  else return u_ + sigma_ * expm1(-xi_ * logProb) / xi_;
}

/* Return level */
Scalar GeneralizedPareto::computeReturnLevel(const Scalar m) const
{
  if (!(m > 1.0))
    throw InvalidArgumentException(HERE) << "in computeReturnLevel m should be > 1";
  return computeQuantile(1.0 / m, true)[0];
}


class GPDReturnLevelEvaluation: public EvaluationImplementation
{
public:
  GPDReturnLevelEvaluation(const GeneralizedPareto & gpd)
    : EvaluationImplementation()
    , gpd_(gpd)
  {
    // Nothing to do
  }

  GPDReturnLevelEvaluation * clone() const override
  {
    return new GPDReturnLevelEvaluation(*this);
  }

  UnsignedInteger getInputDimension() const override
  {
    return 1;
  }

  UnsignedInteger getOutputDimension() const override
  {
    return 1;
  }

  Point operator()(const Point & inP) const override
  {
    const Scalar m = inP[0];
    return Point(1, gpd_.computeReturnLevel(m));
  }

private:
  GeneralizedPareto gpd_;
};

/* Draw return level */
Graph GeneralizedPareto::drawReturnLevel() const
{
  const Scalar xMin = ResourceMap::GetAsScalar("GeneralizedPareto-MMin");
  const Scalar xMax = ResourceMap::GetAsScalar("GeneralizedPareto-MMax");
  const GPDReturnLevelEvaluation wrapper(*this);
  const UnsignedInteger pointNumber = ResourceMap::GetAsUnsignedInteger("Evaluation-DefaultPointNumber");
  Graph graph(wrapper.draw(xMin, xMax, pointNumber, GraphImplementation::LOGX));
  Drawable drawable(graph.getDrawable(0));
  drawable.setLegend("GPD return level");
  drawable.setLineStyle("solid");
  drawable.setLineWidth(2);
  drawable.setColor("red");
  graph.setDrawable(drawable, 0);
  graph.setXTitle("Return period");
  graph.setYTitle("Return level");
  graph.setTitle("");
  graph.setLegendPosition("bottomright");
  return graph;
}

Scalar GeneralizedPareto::computeProbability(const Interval & interval) const
{
  if (interval.getDimension() != 1)
    throw InvalidArgumentException(HERE) << "computeProbability expected an interval of dimension=" << dimension_ << ", got dimension=" << interval.getDimension();
  return computeProbabilityGeneral1D(interval.getLowerBound()[0], interval.getUpperBound()[0]);
}

/* Compute the mean of the distribution */
void GeneralizedPareto::computeMean() const
{
  if (!(xi_ < 1.0)) throw NotDefinedException(HERE) << "Error: the mean is defined only for xi<1, here xi=" << xi_;
  mean_ = Point(1, u_ + sigma_ / (1.0 - xi_));
  isAlreadyComputedMean_ = true;
}

/* Get the standard deviation of the distribution */
Point GeneralizedPareto::getStandardDeviation() const
{
  return Point(1, std::sqrt(getCovariance()(0, 0)));
}

/* Get the skewness of the distribution */
Point GeneralizedPareto::getSkewness() const
{
  if (xi_ >= 1.0 / 3.0) throw NotDefinedException(HERE) << "Error: the skewness is defined only for xi<1/3, here xi=" << xi_;
  return Point(1, 2.0 * (1.0 + xi_) * std::sqrt(1.0 - 2.0 * xi_) / (1.0 - 3.0 * xi_));
}

/* Get the kurtosis of the distribution */
Point GeneralizedPareto::getKurtosis() const
{
  if (xi_ >= 1.0 / 4.0) throw NotDefinedException(HERE) << "Error: the kurtosis is defined only for xi<1/4, here xi=" << xi_;
  return Point(1, 3.0 * (1.0 - 2.0 * xi_) * (3.0 + xi_ * (1.0 + 2.0 * xi_)) / ((1.0 - 4.0 * xi_) * (1.0 - 3.0 * xi_)));
}

/* Get the standard representative in the parametric family, associated with the standard moments */
Distribution GeneralizedPareto::getStandardRepresentative() const
{
  GeneralizedPareto standard(1.0, xi_, 0.0);
  standard.setDescription(getDescription());
  return standard;
}

/* Compute the covariance of the distribution */
void GeneralizedPareto::computeCovariance() const
{
  if (xi_ >= 0.5) throw NotDefinedException(HERE) << "Error: the covariance is defined only for xi<1/2, here xi=" << xi_;
  covariance_ = CovarianceMatrix(1);
  covariance_(0, 0) = sigma_ * sigma_ / ((1.0 - 2.0 * xi_) * (1.0 - xi_) * (1.0 - xi_));
  isAlreadyComputedCovariance_ = true;
}

/* Parameters value  accessor */
Point GeneralizedPareto::getParameter() const
{
  return {sigma_, xi_, u_};
}

void GeneralizedPareto::setParameter(const Point & parameter)
{
  if (parameter.getSize() != 3) throw InvalidArgumentException(HERE) << "Error: expected 3 values, got " << parameter.getSize();
  const Scalar w = getWeight();
  *this = GeneralizedPareto(parameter[0], parameter[1], parameter[2]);
  setWeight(w);
}

/* Parameters description accessor */
Description GeneralizedPareto::getParameterDescription() const
{
  return {"sigma", "xi", "u"};
}

/* Sigma accessor */
void GeneralizedPareto::setSigma(const Scalar sigma)
{
  if (!(sigma > 0.0)) throw InvalidArgumentException(HERE) << "Sigma MUST be positive";
  if (sigma != sigma_)
  {
    sigma_ = sigma;
    isAlreadyComputedMean_ = false;
    isAlreadyComputedCovariance_ = false;
    computeRange();
  }
}

/* Sigma accessor */
Scalar GeneralizedPareto::getSigma() const
{
  return sigma_;
}

/* Xi accessor */
void GeneralizedPareto::setXi(const Scalar xi)
{
  if (xi != xi_)
  {
    xi_ = xi;
    isAlreadyComputedMean_ = false;
    isAlreadyComputedCovariance_ = false;
    // The mean does not depend on xi
    computeRange();
  }
}

/* Xi accessor */
Scalar GeneralizedPareto::getXi() const
{
  return xi_;
}

/* U accessor */
void GeneralizedPareto::setU(const Scalar u)
{
  if (u != u_)
  {
    u_ = u;
    isAlreadyComputedMean_ = false;
    computeRange();
  }
}

/* U accessor */
Scalar GeneralizedPareto::getU() const
{
  return u_;
}

Pareto GeneralizedPareto::asPareto() const
{
  if (!(xi_ > 0.0))
    throw InvalidArgumentException(HERE) << "Cannot convert to Pareto";
  return Pareto(sigma_ / xi_, 1.0 / xi_, u_ - sigma_ / xi_);
}

/* Method save() stores the object through the StorageManager */
void GeneralizedPareto::save(Advocate & adv) const
{
  ContinuousDistribution::save(adv);
  adv.saveAttribute( "sigma_", sigma_ );
  adv.saveAttribute( "xi_", xi_ );
  adv.saveAttribute( "u_", u_ );
}

/* Method load() reloads the object from the StorageManager */
void GeneralizedPareto::load(Advocate & adv)
{
  ContinuousDistribution::load(adv);
  adv.loadAttribute( "sigma_", sigma_ );
  adv.loadAttribute( "xi_", xi_ );
  if (adv.hasAttribute("u_"))
    adv.loadAttribute( "u_", u_ );
  computeRange();
}


END_NAMESPACE_OPENTURNS
