text: /*=========================================================================
   *
   *  Copyright Insight Software Consortium
   *
   *  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 itkGaussianSpatialFunction_hxx
  #define itkGaussianSpatialFunction_hxx
  
  #include <cmath>
  #include "itkMath.h"
  #include "itkGaussianSpatialFunction.h"
  
  namespace itk
  {
  template< typename TOutput, unsigned int VImageDimension, typename TInput >
  GaussianSpatialFunction< TOutput, VImageDimension, TInput >
  ::GaussianSpatialFunction()
  
  {
    m_Mean = ArrayType::Filled(10.0);
    m_Sigma = ArrayType::Filled(5.0);
  }
  
  template< typename TOutput, unsigned int VImageDimension, typename TInput >
  GaussianSpatialFunction< TOutput, VImageDimension, TInput >
  ::~GaussianSpatialFunction() = default;
  
  template< typename TOutput, unsigned int VImageDimension, typename TInput >
  typename GaussianSpatialFunction< TOutput, VImageDimension, TInput >::OutputType
  GaussianSpatialFunction< TOutput, VImageDimension, TInput >
  ::Evaluate(const TInput & position) const
  {
    // We have to compute the Gaussian in several stages, because of the
    // n-dimensional generalization
  
    // 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 = 1.0;
  
    if ( m_Normalized )
      {
      const double squareRootOfTwoPi = std::sqrt(2.0 * itk::Math::pi);
  
      for ( unsigned int i = 0; i < VImageDimension; ++i )
        {
        prefixDenom *= m_Sigma[i] * squareRootOfTwoPi;
        }
      }
  
    double suffixExp = 0;
  
    for ( unsigned int i = 0; i < VImageDimension; ++i )
      {
      suffixExp += ( position[i] - m_Mean[i] ) * ( position[i] - m_Mean[i] )
                   / ( 2 * m_Sigma[i] * m_Sigma[i] );
      }
  
    const double value = m_Scale * ( 1 / prefixDenom ) * std::exp(-1 * suffixExp);
  
    return static_cast< TOutput >( value );
  }
  
  template< typename TOutput, unsigned int VImageDimension, typename TInput >
  void
  GaussianSpatialFunction< 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;
  }
  } // end namespace itk
  
  #endif
  