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#include <array>
#include <memory>
#include <random>
#include <boost/math/special_functions/erf.hpp>
#include <sopt/relative_variation.h>
#include <sopt/sdmm.h>
#include <sopt/utilities.h>
#include <sopt/wavelets.h>
#include <sopt/wavelets/sara.h>
#include "purify/MeasurementOperator.h"
#include "purify/directories.h"
#include "purify/pfitsio.h"
#include "purify/types.h"
#include "purify/utilities.h"
int main(int, char **) {
using namespace purify;
using namespace purify::notinstalled;
sopt::logging::initialize();
std::string const fitsfile = image_filename("M31.fits");
std::string const inputfile = output_filename("M31_input.fits");
std::string const outfile = output_filename("M31.tiff");
std::string const outfile_fits = output_filename("M31_solution.fits");
std::string const residual_fits = output_filename("M31_residual.fits");
std::string const dirty_image = output_filename("M31_dirty.tiff");
std::string const dirty_image_fits = output_filename("M31_dirty.fits");
std::string const output_vis_file = output_filename("M31_Random_coverage.vis");
t_real const over_sample = 2;
auto M31 = pfitsio::read2d(fitsfile);
t_real const max = M31.array().abs().maxCoeff();
M31 = M31 * 1. / max;
pfitsio::write2d(M31.real(), inputfile);
// Following same formula in matlab example
t_real const sigma_m = constant::pi / 3;
// t_int const number_of_vis = std::floor(p * rho * M31.size());
t_int const number_of_vis = 1e4;
// Generating random uv(w) coverage
auto uv_data = utilities::random_sample_density(number_of_vis, 0, sigma_m);
uv_data.units = "radians";
utilities::write_visibility(uv_data, output_vis_file);
std::cout << "Number of measurements / number of pixels: " << uv_data.u.size() * 1. / M31.size()
<< '\n';
// uv_data = utilities::uv_symmetry(uv_data); //reflect uv measurements
MeasurementOperator measurements(uv_data, 4, 4, "kb", M31.cols(), M31.rows(), 20, over_sample);
// putting measurement operator in a form that sopt can use
auto measurements_transform = linear_transform(measurements, uv_data.vis.size());
sopt::wavelets::SARA const sara{
std::make_tuple("Dirac", 3u), std::make_tuple("DB1", 3u), std::make_tuple("DB2", 3u),
std::make_tuple("DB3", 3u), std::make_tuple("DB4", 3u), std::make_tuple("DB5", 3u),
std::make_tuple("DB6", 3u), std::make_tuple("DB7", 3u), std::make_tuple("DB8", 3u)};
auto const Psi
= sopt::linear_transform<t_complex>(sara, measurements.imsizey(), measurements.imsizex());
std::mt19937_64 mersenne;
Vector<t_complex> const y0
= (measurements_transform * Vector<t_complex>::Map(M31.data(), M31.size()));
// working out value of signal given SNR of 30
t_real sigma = utilities::SNR_to_standard_deviation(y0, 30.);
// adding noise to visibilities
uv_data.vis = utilities::add_noise(y0, 0., sigma);
Vector<> dimage = (measurements_transform.adjoint() * uv_data.vis).real();
t_real const max_val = dimage.array().abs().maxCoeff();
dimage = dimage / max_val;
Vector<t_complex> initial_estimate = Vector<t_complex>::Zero(dimage.size());
sopt::utilities::write_tiff(
Image<t_real>::Map(dimage.data(), measurements.imsizey(), measurements.imsizex()),
dirty_image);
pfitsio::write2d(
Image<t_real>::Map(dimage.data(), measurements.imsizey(), measurements.imsizex()),
dirty_image_fits);
auto const epsilon = utilities::calculate_l2_radius(uv_data.vis, sigma);
std::printf("Using epsilon of %f \n", epsilon);
std::cout << "Starting sopt" << '\n';
auto const sdmm
= sopt::algorithm::SDMM<t_complex>()
.itermax(500)
.gamma((measurements_transform.adjoint() * uv_data.vis).real().maxCoeff() * 1e-3)
.is_converged(sopt::RelativeVariation<t_complex>(1e-3))
.conjugate_gradient(100, 1e-3)
.append(
sopt::proximal::translate(sopt::proximal::L2Ball<t_complex>(epsilon), -uv_data.vis),
measurements_transform)
.append(sopt::proximal::l1_norm<t_complex>, Psi.adjoint(), Psi)
.append(sopt::proximal::positive_quadrant<t_complex>);
Vector<t_complex> result;
auto const diagonstic = sdmm(result);
Image<t_complex> image
= Image<t_complex>::Map(result.data(), measurements.imsizey(), measurements.imsizex());
t_real const max_val_final = image.array().abs().maxCoeff();
image = image / max_val_final;
sopt::utilities::write_tiff(image.real(), outfile);
pfitsio::write2d(image.real(), outfile_fits);
Image<t_complex> residual = measurements.grid(y0 - measurements.degrid(image));
pfitsio::write2d(residual.real(), residual_fits);
return 0;
}
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