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/*
* This file is part of the FORS Data Reduction Pipeline
* Copyright (C) 2002-2010 European Southern Observatory
*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 2 of the License, or
* (at your option) any later version.
*
* This program 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 General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
*/
/*
* image_normalisation.cpp
*
* Created on: 2014 3 28
* Author: cgarcia
*/
#include <cpl.h>
#include <vector>
#include <iostream>
#include <iterator>
#include <numeric>
#include <exception>
#include <algorithm>
#include "image_normalisation.h"
#include "vector_utils.h"
#include "profile_providers.h"
/*--------------- Private functions declaration ---------------*/
template<typename T,
typename SpatialProfileProviderType,
typename DispersionProfileProviderType>
static mosca::image image_normalise_internal(const mosca::image& slit_image,
const mosca::image& slit_image_weight,
const SpatialProfileProviderType& spa_profile_provider,
const DispersionProfileProviderType& disp_profile_provider,
std::vector<T>& slit_spa_norm_profile, std::vector<T>& slit_disp_norm_profile);
const char* mosca::no_flux_exception::what() const throw()
{
const char* ret =
"The sum of all the flux contributions for the provided slit "
"is zero, making normalisation not possible";
return ret;
}
template<typename T>
mosca::image mosca::image_normalise(const mosca::image& slit_image,
const mosca::image& slit_image_weight,
int spa_smooth_radius, int disp_smooth_radius, int disp_smooth_radius_aver,
int spa_fit_polyorder, int disp_fit_nknots, double fit_threshold,
std::vector<T>& slit_spa_norm_profile, std::vector<T>& slit_disp_norm_profile)
{
spatial_profile_provider<T> spa_profile_provider{slit_image, slit_image_weight, spa_smooth_radius, spa_fit_polyorder, fit_threshold};
dispersion_profile_provider<T> disp_profile_provider{slit_image, slit_image_weight, disp_smooth_radius, disp_smooth_radius_aver, disp_fit_nknots, fit_threshold};
return image_normalise_internal<T>(slit_image, slit_image_weight,
spa_profile_provider, disp_profile_provider,
slit_spa_norm_profile, slit_disp_norm_profile);
}
template<typename T>
mosca::image mosca::image_normalise_spa_local(const mosca::image& slit_image,
const mosca::image& slit_image_weight,
int spa_smooth_radius, int disp_smooth_radius, int disp_smooth_radius_aver,
int spa_fit_polyorder, int disp_fit_nknots,
double fit_threshold,
bool normalise_spa_local,
std::vector<T>& slit_spa_norm_profile,
std::vector<T>& slit_disp_norm_profile)
{
if(!normalise_spa_local)
return image_normalise(slit_image, slit_image_weight, spa_smooth_radius, disp_smooth_radius, disp_smooth_radius_aver, spa_fit_polyorder,
disp_fit_nknots, fit_threshold, slit_spa_norm_profile, slit_disp_norm_profile);
local_spatial_profile_provider<T> spa_profile_provider{slit_image, slit_image_weight, spa_smooth_radius, spa_fit_polyorder, fit_threshold};
dispersion_profile_provider<T> disp_profile_provider{slit_image, slit_image_weight, disp_smooth_radius, disp_smooth_radius_aver, disp_fit_nknots, fit_threshold};
return image_normalise_internal<T>(slit_image, slit_image_weight,
spa_profile_provider, disp_profile_provider,
slit_spa_norm_profile, slit_disp_norm_profile);
}
/*--------------- Private functions implementation ---------------*/
template<typename T,
typename SpatialProfileProviderType,
typename DispersionProfileProviderType>
static mosca::image image_normalise_internal(const mosca::image& slit_image,
const mosca::image& slit_image_weight,
const SpatialProfileProviderType& spa_profile_provider,
const DispersionProfileProviderType& disp_profile_provider,
std::vector<T>& slit_spa_norm_profile, std::vector<T>& slit_disp_norm_profile){
if(slit_image.size_x() != slit_image_weight.size_x() ||
slit_image.size_y() != slit_image_weight.size_y())
throw std::invalid_argument("image and weight sizes do not match");
if(slit_image.dispersion_axis() != slit_image_weight.dispersion_axis() ||
slit_image.spatial_axis() != slit_image_weight.spatial_axis())
throw std::invalid_argument("image and weight orientation do not match");
slit_spa_norm_profile = spa_profile_provider.get_average_linear_profile();
slit_disp_norm_profile = disp_profile_provider.get_average_linear_profile();
cpl_size nx = slit_image.size_x();
cpl_size ny = slit_image.size_y();
mosca::image result(nx, ny, mosca::type_trait<T>::cpl_eq_type,
slit_image.dispersion_axis());
T * p_res = result.get_data<T>();
const T * p_weight = slit_image_weight.get_data<T>();
for (cpl_size y = 0; y< ny; ++y)
{
for (cpl_size x = 0; x< nx; ++x, ++p_res, ++p_weight)
{
if(*p_weight != 0){
*p_res = disp_profile_provider.get(x, y) * spa_profile_provider.get(x, y);
}
else{
*p_res = 1;
}
}
}
return result;
}
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