/*=========================================================================

  Program:   Insight Segmentation & Registration Toolkit
  Module:    $RCSfile: itkGaussianSpatialFunction.txx,v $
  Language:  C++
  Date:      $Date: 2006/03/19 04:36:58 $
  Version:   $Revision: 1.10 $

  Copyright (c) Insight Software Consortium. All rights reserved.
  See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details.

     This software is distributed WITHOUT ANY WARRANTY; without even 
     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR 
     PURPOSE.  See the above copyright notices for more information.

=========================================================================*/
#ifndef __itkGaussianSpatialFunction_txx
#define __itkGaussianSpatialFunction_txx

#include <math.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);
  m_Scale = 1.0;
  m_Normalized = false;
}

template <typename TOutput, unsigned int VImageDimension, typename TInput>
GaussianSpatialFunction<TOutput, VImageDimension, TInput>
::~GaussianSpatialFunction()
{

}

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;

  if (m_Normalized)
    {
    prefixDenom = m_Sigma[0];

    for(unsigned int i = 1; i < VImageDimension; i++)
      {
      prefixDenom *= m_Sigma[i];
      }

    prefixDenom *= 2 * 3.1415927;
    }
  else
    {
    prefixDenom = 1.0;
    }

  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]);
    }

  double value = m_Scale * (1 / prefixDenom) * vcl_exp(-1 * suffixExp);

  return (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);

  unsigned int i;
  os << indent << "Sigma: [";
  for (i=0; i+1 < VImageDimension ; i++)
    {
    os << m_Sigma[i] << ", ";
    }
  os << "]" << std::endl;

  os << indent << "Mean: [";
  for (i=0; i+1 < VImageDimension ; i++)
    {
    os << m_Mean[i] << ", ";
    }
  os << "]" << std::endl;

  os << indent << "Scale: " << m_Scale << std::endl;
  os << indent << "Normalized?: " << m_Normalized << std::endl;
}


} // end namespace itk

#endif
