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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 "image_normalisation.h"
#include "vector_utils.h"
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
(mosca::image& slit_image,
mosca::image& slit_image_weight,
int spa_smooth_radius, int disp_smooth_radius,
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)
{
//TODO: Check dispersion axis are the same
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");
//TODO: Use size_disp rather than size_x in the relevant places
mosca::image slit_weighted = slit_image;
std::transform (slit_image.get_data<T>(),
slit_image.get_data<T>() + slit_image.size_x() * slit_image.size_y(),
slit_image_weight.get_data<T>(),
slit_weighted.get_data<T>(), std::multiplies<T>());
//Collapsing the data to get the profiles in each direction
std::vector<T> slit_spa_profile =
slit_weighted.collapse<T>(mosca::DISPERSION_AXIS);
std::vector<T> slit_disp_profile =
slit_weighted.collapse<T>(mosca::SPATIAL_AXIS);
//Collapsing the weights
std::vector<T> weight_spa_profile =
slit_image_weight.collapse<T>(mosca::DISPERSION_AXIS);
std::vector<T> weight_disp_profile =
slit_image_weight.collapse<T>(mosca::SPATIAL_AXIS);
//Getting the profiles properly weighted
std::vector<T> slit_spa_profile_w;
std::transform (slit_spa_profile.begin(), slit_spa_profile.end(),
weight_spa_profile.begin(), std::back_inserter(slit_spa_profile_w),
std::divides<T>());
std::vector<T> slit_disp_profile_w;
std::transform (slit_disp_profile.begin(), slit_disp_profile.end(),
weight_disp_profile.begin(), std::back_inserter(slit_disp_profile_w),
std::divides<T>());
T * p_ima = slit_weighted.get_data<T>();
T total_flux =
std::accumulate(p_ima, p_ima + slit_image.size_x() * slit_image.size_y(), T(0));
T * p_weight = slit_image_weight.get_data<T>();
T total_weight =
std::accumulate(p_weight, p_weight + slit_image.size_x() * slit_image.size_y(), T(0));
if(total_flux == T(0) || total_weight == T(0))
{
slit_spa_norm_profile = slit_spa_profile;
slit_disp_norm_profile = slit_disp_profile;
return slit_image;
}
//If we are doing any fitting/smoothing in that direction,
//initialise it to the current profile, if not initialise it to a constant
if (spa_smooth_radius > 0 || spa_fit_polyorder > 0)
slit_spa_norm_profile = slit_spa_profile_w;
else
slit_spa_norm_profile = std::vector<T>(slit_spa_profile_w.size(),
T(total_flux / total_weight));
if (disp_smooth_radius > 0 || disp_fit_nknots > 0)
slit_disp_norm_profile = slit_disp_profile_w;
else
slit_disp_norm_profile = std::vector<T>(slit_disp_profile_w.size(),
T(total_flux / total_weight));
if (spa_smooth_radius > 0)
{
std::vector<bool> mask;
std::transform(weight_spa_profile.begin(), weight_spa_profile.end(),
std::back_inserter(mask), std::bind1st(std::not_equal_to<T>(), T(0)));
mosca::vector_smooth<T>(slit_spa_norm_profile, mask, spa_smooth_radius);
}
if (spa_fit_polyorder > 0)
{
std::vector<bool> mask;
const double max_el = *std::max_element(slit_spa_norm_profile.begin(), slit_spa_norm_profile.end());
const double th_this = fit_threshold * max_el;
std::transform(slit_spa_norm_profile.begin(), slit_spa_norm_profile.end(),
std::back_inserter(mask), std::bind2nd(std::greater_equal<T>(), th_this));
size_t used_spa_fit_polyorder = spa_fit_polyorder;
mosca::vector_polynomial polfit;
polfit.fit<T>(slit_spa_norm_profile, mask, used_spa_fit_polyorder);
}
if (disp_smooth_radius > 0)
{
std::vector<bool> mask;
std::transform(weight_disp_profile.begin(), weight_disp_profile.end(),
std::back_inserter(mask), std::bind1st(std::not_equal_to<T>(), T(0)));
mosca::vector_smooth<T>(slit_disp_norm_profile, mask, disp_smooth_radius);
}
if (disp_fit_nknots > 0)
{
std::vector<bool> mask;
std::transform(weight_disp_profile.begin(), weight_disp_profile.end(),
std::back_inserter(mask), std::bind1st(std::not_equal_to<T>(), T(0)));
size_t used_disp_fit_nknots = disp_fit_nknots;
mosca::vector_cubicspline splfit;
splfit.fit<T>(slit_disp_norm_profile, mask,
used_disp_fit_nknots);
}
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>();
p_weight = slit_image_weight.get_data<T>();
for (cpl_size j = 0; j< ny; ++j)
{
for (cpl_size i = 0; i< nx; ++i, ++p_res, ++p_weight)
{
if(*p_weight != 0)
{
if(slit_image.dispersion_axis() == mosca::X_AXIS)
*p_res = slit_spa_norm_profile[j] * slit_disp_norm_profile[i] /
total_flux * total_weight;
else
*p_res = slit_spa_norm_profile[i] * slit_disp_norm_profile[j] /
total_flux * total_weight;
}
else
*p_res = 1;
}
}
return result;
}
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