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* jfjoch_broker: Optional per-dataset authentication - statistics, images and plots can require a bearer token, which jfjoch_viewer supports. * jfjoch_viewer: Dark mode and a theme-matched colour scheme, a magnifier panel, and simpler contrast and background controls. * Rugnux: Multiple performance improvements on GPU and CPU (CPU-only processing up to 40% faster, faster image decoding on ARM), with unchanged results. * Rugnux: `--model` rigid-body refinement runs on the GPU, and the model-validation check is faster and more reliable. * Rugnux: Improved scaling and merging - error model, outlier rejection, absorption correction and French-Wilson amplitudes now agree more closely with XDS and ctruncate. * Rugnux: Improved integration - radial background on powder and ice rings, crowded rotation data keep their reflections, and CPU-only builds integrate large unit cells as GPU builds do. * Rugnux: More robust detector geometry - measured beam centre, X-ray bandwidth and goniometer rate, and geometry refinement accepted only on significant evidence. * Rugnux: Merged files are written in the standard setting, or in the setting of a reference MTZ, structure-factor mmCIF or model, with its free-R flags. * Rugnux: Richer report - ice and powder rings, further lattices, superstructure candidates and mosaicity, with warnings worded as prompts to check. * Rugnux: Clear error messages when a data set needs more GPU or host memory than is available. Reviewed-on: #83 Co-authored-by: Filip Leonarski <filip.leonarski@psi.ch>
79 lines
3.7 KiB
C++
79 lines
3.7 KiB
C++
// SPDX-FileCopyrightText: 2026 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 <cmath>
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#include <cstdint>
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#include <vector>
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#include "../common/BraggIntegrationSettings.h"
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#include "../common/DetectorSetup.h"
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#include "../common/DiffractionExperiment.h"
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#include "../common/Reflection.h"
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#include "../image_analysis/bragg_integration/BraggIntegrationEngineCPU.h"
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#include "../image_analysis/image_preprocessing/ImagePreprocessorBuffer.h"
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// One spot on a flat background, inside a grid of predictions 8 px apart that put no flux on the
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// frame - the tails of reflections recorded on the frames either side of a finely sliced one. Their
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// r2 regions cover every background ring, so whether the spot keeps a clean ring depends only on
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// whether those predictions are allowed to mask it.
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TEST_CASE("BraggIntegrationEngineCPU_NeighbourMaskFollowsPartiality", "[Integration][portable]") {
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DiffractionExperiment experiment(DetJF(2));
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experiment.DetectorDistance_mm(100.0f).IncidentEnergy_keV(WVL_1A_IN_KEV).BeamX_pxl(400.0f).BeamY_pxl(400.0f);
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experiment.ImportBraggIntegrationSettings(BraggIntegrationSettings());
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const size_t width = experiment.GetXPixelsNum(), npixel = experiment.GetPixelsNum();
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const float cx = 600.3f, cy = 300.2f, amp = 800.0f, sigma = 1.3f;
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ImagePreprocessorBuffer image(npixel);
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for (size_t i = 0; i < npixel; ++i)
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image[i] = 12;
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for (int dy = -6; dy <= 6; ++dy)
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for (int dx = -6; dx <= 6; ++dx) {
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const int x = static_cast<int>(std::lround(cx)) + dx, y = static_cast<int>(std::lround(cy)) + dy;
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const float ex = x - cx, ey = y - cy;
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image[y * width + x] += static_cast<int32_t>(std::lround(amp * std::exp(-(ex * ex + ey * ey) / (2 * sigma * sigma))));
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}
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auto run = [&](float neighbour_partiality, BraggIntegrationCounts &counts) {
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std::vector<Reflection> predicted;
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for (int gy = -4; gy <= 4; ++gy)
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for (int gx = -4; gx <= 4; ++gx) {
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Reflection r{};
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r.h = gx; r.k = gy; r.l = 1;
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r.predicted_x = cx + 8.0f * gx;
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r.predicted_y = cy + 8.0f * gy;
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r.d = 2.0f;
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r.prescaling_corr = 1.0f;
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r.partiality = (gx == 0 && gy == 0) ? 0.5f : neighbour_partiality;
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predicted.push_back(r);
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}
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BraggIntegrationEngineCPU engine(experiment);
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const auto out = engine.Run(image, predicted, predicted.size(), 0);
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counts = engine.Counts();
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std::vector<Reflection> spot;
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for (const auto &r : out)
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if (r.h == 0 && r.k == 0)
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spot.push_back(r);
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return spot;
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};
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// Tails: no reflection is dropped, and the spot is measured against the clean flat background -
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// while the density the widened-radius guard reads still sees every prediction.
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BraggIntegrationCounts tails;
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const auto spot = run(0.01f, tails);
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CHECK(tails.bkg_starved == 0);
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CHECK(tails.bkg_starved_by_neighbour > 0);
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REQUIRE(spot.size() == 1);
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CHECK(spot[0].bkg == Catch::Approx(12.0f));
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CHECK(spot[0].I == Catch::Approx(6.2831853 * sigma * sigma * amp).epsilon(0.05));
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// The same predictions with their flux on this frame do take the rings, the spot's among them, and
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// it is the neighbours, not the detector, that starve them - the same rings the density counted.
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BraggIntegrationCounts on_frame;
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CHECK(run(1.0f, on_frame).empty());
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CHECK(on_frame.bkg_starved > 0);
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CHECK(on_frame.bkg_starved_by_neighbour == on_frame.bkg_starved);
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CHECK(on_frame.bkg_starved_by_neighbour == tails.bkg_starved_by_neighbour);
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}
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