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// SPDX-License-Identifier: LGPL-3.0-only
#include <boost/test/unit_test.hpp>
#include "algorithms/simple_clean.h"
#include <aocommon/uvector.h>
#include <random>
namespace radler::algorithms {
BOOST_AUTO_TEST_SUITE(simple_clean)
template <typename NumT>
struct NoiseFixture {
NoiseFixture() : n(2048), psf(n * n, 0.0), img(n * n), normal_dist(0.0, 1.0) {
mt.seed(42);
for (size_t i = 0; i != n * n; ++i) {
img[i] = normal_dist(mt);
}
}
size_t n;
aocommon::UVector<NumT> psf, img;
std::mt19937 mt;
std::normal_distribution<NumT> normal_dist;
};
const size_t nRepeats =
3; /* This should be set to 100 to assert the performance */
BOOST_AUTO_TEST_CASE(partialSubtractImagePerformance) {
NoiseFixture<float> f;
size_t x = f.n / 2, y = f.n / 2;
for (size_t repeat = 0; repeat != nRepeats; ++repeat) {
simple_clean::PartialSubtractImage(f.img.data(), f.n, f.n, f.psf.data(),
f.n, f.n, x, y, 0.5, 0, f.n / 2);
}
BOOST_CHECK(true);
}
#if defined __AVX__ && !defined FORCE_NON_AVX
BOOST_AUTO_TEST_CASE(partialSubtractImageAVXPerformance) {
NoiseFixture<double> f;
size_t x = f.n / 2, y = f.n / 2;
for (size_t repeat = 0; repeat != nRepeats; ++repeat)
simple_clean::PartialSubtractImageAVX(f.img.data(), f.n, f.n, f.psf.data(),
f.n, f.n, x, y, 0.5, 0, f.n / 2);
BOOST_CHECK(true);
}
#endif
BOOST_AUTO_TEST_SUITE_END()
} // namespace radler::algorithms
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