mirror of
https://github.com/slsdetectorgroup/aare.git
synced 2026-10-24 23:11:28 +02:00
Erik Fröjdh
2efdd17d81
- For set_nSigma only call update_pedestal if we have accumulated enough samples otherwise just write it - For update threshold check if pd is ready and if not throw closes #390
109 lines
4.0 KiB
C++
109 lines
4.0 KiB
C++
// SPDX-License-Identifier: MPL-2.0
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#include "aare/ClusterFinder.hpp"
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#include "aare/Pedestal.hpp"
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#include <catch2/catch_test_macros.hpp>
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#include <catch2/generators/catch_generators.hpp>
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#include <catch2/matchers/catch_matchers_floating_point.hpp>
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#include <chrono>
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#include <random>
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using namespace aare;
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// TODO! Find a way to test the cluster finder
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// class ClusterFinderUnitTest : public ClusterFinder {
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// public:
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// ClusterFinderUnitTest(int cluster_sizeX, int cluster_sizeY, double nSigma
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// = 5.0, double threshold = 0.0)
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// : ClusterFinder(cluster_sizeX, cluster_sizeY, nSigma, threshold) {}
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// double get_c2() { return c2; }
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// double get_c3() { return c3; }
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// auto get_threshold() { return m_threshold; }
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// auto get_nSigma() { return m_nSigma; }
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// auto get_cluster_sizeX() { return m_cluster_sizeX; }
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// auto get_cluster_sizeY() { return m_cluster_sizeY; }
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// };
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// TEST_CASE("test ClusterFinder constructor") {
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// ClusterFinderUnitTest cf(55, 100);
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// REQUIRE(cf.get_cluster_sizeX() == 55);
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// REQUIRE(cf.get_cluster_sizeY() == 100);
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// REQUIRE(cf.get_threshold() == 0.0);
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// REQUIRE(cf.get_nSigma() == 5.0);
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// double c2 = sqrt((100 + 1) / 2 * (55 + 1) / 2);
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// double c3 = sqrt(55 * 100);
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// // REQUIRE(compare_floats<double>(cf.get_c2(), c2));
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// // REQUIRE(compare_floats<double>(cf.get_c3(), c3));
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// REQUIRE_THAT(cf.get_c2(), Catch::Matchers::WithinRel(c2, 1e-9));
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// REQUIRE_THAT(cf.get_c3(), Catch::Matchers::WithinRel(c3, 1e-9));
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// }
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TEST_CASE("Construct a cluster finder") {
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ClusterFinder clusterFinder({400, 400});
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// REQUIRE(clusterFinder.get_cluster_sizeX() == 3);
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// REQUIRE(clusterFinder.get_cluster_sizeY() == 3);
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// REQUIRE(clusterFinder.get_threshold() == 1);
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// REQUIRE(clusterFinder.get_nSigma() == 1);
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}
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TEST_CASE("nSigma set before the pedestal is ready is used for the threshold") {
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const Shape<2> image_size{10, 10};
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const size_t min_pedestal_samples = 4;
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// Pedestal of 100 with a noise of 2 and a 30 ADU hit: a cluster at the
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// default nSigma of 5, below threshold at nSigma 50.
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NDArray<uint16_t, 2> low(image_size, 98);
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NDArray<uint16_t, 2> high(image_size, 102);
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NDArray<uint16_t, 2> hit(image_size, 100);
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hit(5, 5) = 130;
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const double nSigma = GENERATE(5.0, 50.0);
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const size_t expected_clusters = nSigma == 5.0 ? 1 : 0;
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ClusterFinder<Cluster<int32_t, 3, 3>> cf(image_size, 5.0, 100,
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min_pedestal_samples);
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REQUIRE_FALSE(cf.pedestal_ready());
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REQUIRE_NOTHROW(cf.set_nSigma(nSigma));
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REQUIRE(cf.get_nSigma() == nSigma);
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for (size_t i = 0; i < min_pedestal_samples / 2; ++i) {
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cf.push_pedestal_frame(low.view());
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REQUIRE_FALSE(cf.pedestal_ready());
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cf.push_pedestal_frame(high.view());
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}
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REQUIRE(cf.pedestal_ready());
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cf.find_clusters(hit.view());
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REQUIRE(cf.steal_clusters().size() == expected_clusters);
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}
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TEST_CASE("update_threshold throws until the pedestal is ready") {
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ClusterFinder<Cluster<int32_t, 3, 3>> cf({10, 10});
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REQUIRE_THROWS_AS(cf.update_threshold(), std::runtime_error);
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}
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// TEST_CASE("test cluster finder") {
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// aare::Pedestal pedestal(10, 10, 5);
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// NDArray<double, 2> frame({10, 10});
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// frame = 0;
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// ClusterFinder clusterFinder(3, 3, 1, 1); // 3x3 cluster, 1 nSigma, 1
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// threshold
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// auto clusters =
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// clusterFinder.find_clusters_without_threshold(frame.span(), pedestal);
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// REQUIRE(clusters.size() == 0);
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// frame(5, 5) = 10;
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// clusters = clusterFinder.find_clusters_without_threshold(frame.span(),
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// pedestal); REQUIRE(clusters.size() == 1); REQUIRE(clusters[0].x == 5);
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// REQUIRE(clusters[0].y == 5);
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// for (int i = 0; i < 3; i++) {
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// for (int j = 0; j < 3; j++) {
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// if (i == 1 && j == 1)
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// REQUIRE(clusters[0].get<double>(i * 3 + j) == 10);
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// else
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// REQUIRE(clusters[0].get<double>(i * 3 + j) == 0);
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// }
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// }
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// }
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