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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 <image_normalisation.h>
template<typename T>
mosca::image mosca::image_normalise
(mosca::image& image,
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)
{
//Collapsing to get the profiles in each direction
std::vector<T> slit_spa_profile = image.collapse<T>(mosca::DISPERSION_AXIS);
std::vector<T> slit_disp_profile = image.collapse<T>(mosca::SPATIAL_AXIS);
T * p_ima = image.get_data<T>();
T total_flux =
std::accumulate(p_ima, p_ima + image.size_x() * image.size_y(), T(0));
//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;
else
slit_spa_norm_profile = std::vector<T>(slit_spa_profile.size(),
T(total_flux / slit_spa_profile.size()));
if (disp_smooth_radius > 0 || disp_fit_nknots > 0)
slit_disp_norm_profile = slit_disp_profile;
else
slit_disp_norm_profile = std::vector<T>(slit_disp_profile.size(),
T(total_flux / slit_disp_profile.size()));
if (spa_smooth_radius > 0)
mosca::vector_smooth<T>(slit_spa_norm_profile, spa_smooth_radius);
if (spa_fit_polyorder > 0)
{
size_t used_spa_fit_polyorder = spa_fit_polyorder;
mosca::vector_polynomial polfit;
polfit.fit<T>(slit_spa_norm_profile, used_spa_fit_polyorder,
fit_threshold);
}
if (disp_smooth_radius > 0)
mosca::vector_smooth<T>(slit_disp_norm_profile, disp_smooth_radius);
if (disp_fit_nknots > 0)
{
size_t used_disp_fit_nknots = disp_fit_nknots;
mosca::vector_cubicspline splfit;
splfit.fit<T>(slit_disp_norm_profile,
used_disp_fit_nknots, fit_threshold);
}
cpl_size nx = image.size_x();
cpl_size ny = image.size_y();
mosca::image result(nx, ny, mosca::type_trait<T>::cpl_eq_type,
image.dispersion_axis());
T * p_res = result.get_data<T>();
for (cpl_size j = 0; j< ny; ++j)
{
for (cpl_size i = 0; i< nx; ++i, ++p_res)
{
if(image.dispersion_axis() == mosca::X_AXIS)
*p_res = slit_spa_norm_profile[j] * slit_disp_norm_profile[i] /
total_flux;
else
*p_res = slit_spa_norm_profile[i] * slit_disp_norm_profile[j] /
total_flux;
}
}
return result;
}
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