/*=========================================================================
 *
 *  Copyright NumFOCUS
 *
 *  Licensed under the Apache License, Version 2.0 (the "License");
 *  you may not use this file except in compliance with the License.
 *  You may obtain a copy of the License at
 *
 *         http://www.apache.org/licenses/LICENSE-2.0.txt
 *
 *  Unless required by applicable law or agreed to in writing, software
 *  distributed under the License is distributed on an "AS IS" BASIS,
 *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 *  See the License for the specific language governing permissions and
 *  limitations under the License.
 *
 *=========================================================================*/
#ifndef itkGaussianDerivativeSpatialFunction_hxx
#define itkGaussianDerivativeSpatialFunction_hxx

#include <cmath>
#include "itkMath.h"
#include "itkGaussianDerivativeSpatialFunction.h"

namespace itk
{
template <typename TOutput, unsigned int VImageDimension, typename TInput>
GaussianDerivativeSpatialFunction<TOutput, VImageDimension, TInput>::GaussianDerivativeSpatialFunction()
{
  m_Mean = ArrayType::Filled(0.0);
  m_Sigma = ArrayType::Filled(1.0);
  m_Scale = 1.0;
  m_Normalized = false;
  m_Direction = 0;
}

template <typename TOutput, unsigned int VImageDimension, typename TInput>
typename GaussianDerivativeSpatialFunction<TOutput, VImageDimension, TInput>::OutputType
GaussianDerivativeSpatialFunction<TOutput, VImageDimension, TInput>::Evaluate(const TInput & position) const
{
  // Normalizing the Gaussian is important for statistical applications
  // but is generally not desirable for creating images because of the
  // very small numbers involved (would need to use doubles)
  double prefixDenom;

  if (m_Normalized)
  {
    prefixDenom = m_Sigma[m_Direction] * m_Sigma[m_Direction];

    for (unsigned int i = 0; i < VImageDimension; i++)
    {
      prefixDenom *= m_Sigma[i];
    }

    prefixDenom *= 2 * std::pow(2 * itk::Math::pi, VImageDimension / 2.0);
  }
  else
  {
    prefixDenom = 1.0;
  }

  double suffixExp = 0;

  for (unsigned int i = 0; i < VImageDimension; i++)
  {
    suffixExp += (position[m_Direction] - m_Mean[m_Direction]) * (position[m_Direction] - m_Mean[m_Direction]) /
                 (2 * m_Sigma[m_Direction] * m_Sigma[m_Direction]);
  }

  double value =
    -2 * (position[m_Direction] - m_Mean[m_Direction]) * m_Scale * (1 / prefixDenom) * std::exp(-1 * suffixExp);

  return static_cast<TOutput>(value);
}

/** Evaluate the function at a given position and return a vector */
template <typename TOutput, unsigned int VImageDimension, typename TInput>
typename GaussianDerivativeSpatialFunction<TOutput, VImageDimension, TInput>::VectorType
GaussianDerivativeSpatialFunction<TOutput, VImageDimension, TInput>::EvaluateVector(const TInput & position) const
{
  VectorType gradient;

  for (unsigned int i = 0; i < VImageDimension; i++)
  {
    m_Direction = i;
    gradient[i] = this->Evaluate(position);
  }
  return gradient;
}

template <typename TOutput, unsigned int VImageDimension, typename TInput>
void
GaussianDerivativeSpatialFunction<TOutput, VImageDimension, TInput>::PrintSelf(std::ostream & os, Indent indent) const
{
  Superclass::PrintSelf(os, indent);

  os << indent << "Sigma: " << m_Sigma << std::endl;
  os << indent << "Mean: " << m_Mean << std::endl;
  os << indent << "Scale: " << m_Scale << std::endl;
  os << indent << "Normalized?: " << m_Normalized << std::endl;
  os << indent << "Direction: " << m_Direction << std::endl;
}
} // end namespace itk

#endif
