A Catch2 executable that builds in the portable configurations (JFJOCH_VIEWER_ONLY / JFJOCH_RUGNUX_ONLY) and links only what those build - JFJochRugnux, JFJochReader, JFJochImageAnalysis, JFJochWriter, JFJochCommon - so it can run on the macOS arm64 and Windows x64 jobs, where the receiver/broker/FPGA/HLS sources are not built and there is no GPU. EXCLUDE_FROM_ALL, so a product build does not pay for it; catch2 is now made available in the portable configure as well (it is EXCLUDE_FROM_ALL too). The cases tagged [portable] cover what depends on the architecture, the compiler or the standard library: bitshuffle/LZ4/zstd, HDF5 read-back (legacy/VDS/integrated, the direct-chunk path), miniCBF/marCCD/SMV header parsing, CBOR, CPU spot finding, azimuthal mapping, Bragg prediction/integration, gemmi MTZ/mmCIF. New in tests/PortableTest.cpp: - a golden FNV-1a hash of two frames of compression_benchmark.h5, decoded from the raw chunk by the hperf and the classic bitshuffle and through the HDF5 filter (x86 hashes affea29c511b6ec2 / e46913009c95a1f1); - a golden hash of a bitshuffle/LZ4 encode (the writer must produce the same bytes everywhere); - the shipped bitshuffle block selector against the classic reference over elem 1/2/4/8 and block tails; - the FFTW indexer, named explicitly, on a synthetic orthorhombic lattice (the existing FFT indexer lattice tests run only under CUDA); - a 4-frame end-to-end rugnux run on the git-LFS rotation dataset (HDF5 via external links, CPU spot finding, indexing, integration); SKIPs when LFS was not pulled. All 45 take ~4 s on Linux (~2 s without the LFS case), CPU-only build. M_PI replaced by PI (common/JFJochMath.h) in the two tagged files that used it, as MSVC does not define M_PI. The CBF gzip test shells out to gzip and is left untagged. CI: build and run jfjoch_portable_test "[portable]" in build-windows (both variants), build-rugnux-windows, build-macos-viewer and build-rugnux-macos, after the build and before packaging. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01D1G8gJVAy6gp1K5Dz3NE5C
188 lines
5.7 KiB
C++
188 lines
5.7 KiB
C++
// SPDX-FileCopyrightText: 2025 Filip Leonarski, Paul Scherrer Institute <filip.leonarski@psi.ch>
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// SPDX-License-Identifier: GPL-3.0-only
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#include <catch2/catch_all.hpp>
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#include "../image_analysis/spot_finding/ImageSpotFinderCPU.h"
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TEST_CASE("ImageSpotFinderCPU_SignalToNoise", "[portable]") {
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size_t width = 100;
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size_t height = 100;
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ImageSpotFinderCPU s(width, height);
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ImagePreprocessorBuffer buffer(width * height);
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auto &input = buffer.getBuffer();
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for (int i = 0; i < width * height; i++)
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input[i] = (i % 2) * 5 + 5;
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input[width * 50 + 50] = 20;
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input[width * 25 + 26] = 16;
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input[width * 75 + 25] = 12;
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// Mean is 7.5, std. dev is 2.5
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SpotFindingSettings settings{
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.signal_to_noise_threshold = 3.0,
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.photon_count_threshold = 0,
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.min_pix_per_spot = 1,
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.max_pix_per_spot = 20,
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.high_resolution_limit = 0.5,
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.low_resolution_limit = 3.0,
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};
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std::vector<bool> mask_resolution(width * height, false);
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s.SetResolutionMask(mask_resolution);
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auto spots = s.Run(buffer, settings);
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REQUIRE(spots.size() == 2);
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REQUIRE(spots[0].RawCoord().y == 25);
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REQUIRE(spots[1].RawCoord().y == 50);
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}
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TEST_CASE("ImageSpotFinderCPU_SignalToNoise_Resolution") {
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size_t width = 100;
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size_t height = 100;
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ImageSpotFinderCPU s(width, height);
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ImagePreprocessorBuffer buffer(width * height);
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auto &input = buffer.getBuffer();
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for (int i = 0; i < width * height; i++)
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input[i] = (i % 2) * 5 + 5;
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input[width * 50 + 50] = 20;
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input[width * 25 + 26] = 16;
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input[width * 75 + 25] = 12;
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// Mean is 7.5, std. dev is 2.5
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SpotFindingSettings settings{
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.signal_to_noise_threshold = 3.0,
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.photon_count_threshold = 0,
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.min_pix_per_spot = 1,
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.max_pix_per_spot = 20,
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.high_resolution_limit = 1.5,
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.low_resolution_limit = 3.0,
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};
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std::vector<bool> mask_resolution(width * height, false);
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mask_resolution[width * 50 + 50] = true;
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s.SetResolutionMask(mask_resolution);
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auto spots = s.Run(buffer, settings);
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REQUIRE(spots.size() == 1);
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REQUIRE(spots[0].RawCoord().x == 26);
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REQUIRE(spots[0].RawCoord().y == 25);
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}
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TEST_CASE("ImageSpotFinderCPU_CountThreshold_Resolution") {
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size_t width = 100;
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size_t height = 100;
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ImageSpotFinderCPU s(width, height);
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ImagePreprocessorBuffer buffer(width * height);
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auto &input = buffer.getBuffer();
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for (int i = 0; i < width * height; i++)
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input[i] = (i % 2) * 5 + 5;
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input[width * 50 + 50] = 20;
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input[width * 25 + 26] = 16;
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input[width * 75 + 25] = 12;
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// Mean is 7.5, std. dev is 2.5
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SpotFindingSettings settings{
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.signal_to_noise_threshold = -1,
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.photon_count_threshold = 11,
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.min_pix_per_spot = 1,
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.max_pix_per_spot = 20,
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.high_resolution_limit = 1.5,
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.low_resolution_limit = 3.0,
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};
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std::vector<bool> mask_resolution(width * height, false);
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mask_resolution[width * 50 + 50] = true;
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s.SetResolutionMask(mask_resolution);
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auto spots = s.Run(buffer, settings);
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REQUIRE(spots.size() == 2);
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REQUIRE(spots[0].RawCoord().y == 25);
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REQUIRE(spots[1].RawCoord().y == 75);
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}
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TEST_CASE("ImageSpotFinderCPU_CountThreshold_Mask", "[portable]") {
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size_t width = 100;
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size_t height = 100;
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ImageSpotFinderCPU s( width, height);
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ImagePreprocessorBuffer buffer(width * height);
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auto &input = buffer.getBuffer();
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for (int i = 0; i < width * height; i++)
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input[i] = (i % 2) * 5 + 5;
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input[width * 50 + 50] = INT32_MIN;
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input[width * 50 + 51] = INT32_MAX;
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input[width * 25 + 26] = 16;
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input[width * 75 + 25] = 12;
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// Mean is 7.5, std. dev is 2.5
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SpotFindingSettings settings{
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.signal_to_noise_threshold = -1,
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.photon_count_threshold = 11,
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.min_pix_per_spot = 1,
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.max_pix_per_spot = 20,
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.high_resolution_limit = 1.5,
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.low_resolution_limit = 3.0,
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};
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std::vector<bool> mask_resolution(width * height, false);
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s.SetResolutionMask(mask_resolution);
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auto spots = s.Run(buffer, settings);
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REQUIRE(spots.size() == 3);
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REQUIRE(spots[0].RawCoord().x == 26);
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REQUIRE(spots[0].RawCoord().y == 25);
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REQUIRE(spots[1].RawCoord().x == 51);
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REQUIRE(spots[1].RawCoord().y == 50);
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REQUIRE(spots[2].RawCoord().x == 25);
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REQUIRE(spots[2].RawCoord().y == 75);
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}
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TEST_CASE("ImageSpotFinderCPU_SignalToNoise_Mask") {
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size_t width = 100;
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size_t height = 100;
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ImageSpotFinderCPU s(width, height);
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ImagePreprocessorBuffer buffer(width * height);
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auto &input = buffer.getBuffer();
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for (int i = 0; i < width * height; i++)
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input[i] = (i % 2) * 5 + 5;
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input[width * 25 + 25] = INT32_MIN;
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input[width * 25 + 26] = 16;
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input[width * 74 + 25] = INT32_MIN;
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input[width * 74 + 26] = INT32_MIN;
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input[width * 75 + 25] = 15;
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input[width * 76 + 27] = INT32_MAX;
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// Mean is 7.5, std. dev is 2.5
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SpotFindingSettings settings{
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.signal_to_noise_threshold = 3.0,
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.photon_count_threshold = 0,
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.min_pix_per_spot = 1,
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.max_pix_per_spot = 20,
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.high_resolution_limit = 1.5,
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.low_resolution_limit = 3.0,
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};
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std::vector<bool> mask_resolution(width * height, false);
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s.SetResolutionMask(mask_resolution);
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auto spots = s.Run(buffer, settings);
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REQUIRE(spots.size() == 3);
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REQUIRE(spots[0].RawCoord().x == 26);
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REQUIRE(spots[0].RawCoord().y == 25);
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REQUIRE(spots[1].RawCoord().x == 25);
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REQUIRE(spots[1].RawCoord().y == 75);
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REQUIRE(spots[2].RawCoord().x == 27);
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REQUIRE(spots[2].RawCoord().y == 76);
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} |