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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>
138 lines
6.1 KiB
C++
138 lines
6.1 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/CompressedImage.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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// The FPGA workflow integrates straight off the assembled CompressedImage (any pixel type) instead of
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// a preprocessed int32 buffer, reading only the reflection disks. These tests pin that the typed
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// sampler produces exactly the same intensities as the equivalent int32 preprocessed buffer - i.e. the
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// zero-copy route is numerically identical to the buffer route, not merely close.
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namespace {
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struct Scene {
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std::vector<int32_t> image; // INT32_MIN = masked, INT32_MAX = saturated
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std::vector<Reflection> predicted;
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};
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Reflection MakeReflection(float x, float y, float d, int hkl) {
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Reflection r{};
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r.h = hkl; r.k = hkl; r.l = hkl;
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r.predicted_x = x;
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r.predicted_y = y;
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r.d = d;
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r.prescaling_corr = 1.0f;
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r.partiality = 1.0f;
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return r;
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}
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// A grid of clean Gaussian spots on a flat background, values kept well inside int16 range so the same
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// scene can be represented as Int16 and Int32 without clipping. A few masked/saturated pixels exercise
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// the sentinel handling.
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Scene BuildScene(size_t width, size_t height) {
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Scene s;
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s.image.assign(width * height, 12);
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const int margin = 45, spacing = 60;
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int hkl = 1;
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for (int gy = 0; margin + gy * spacing < static_cast<int>(height) - margin; ++gy)
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for (int gx = 0; margin + gx * spacing < static_cast<int>(width) - margin; ++gx) {
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const float cx = static_cast<float>(margin + gx * spacing) + 0.3f;
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const float cy = static_cast<float>(margin + gy * spacing) - 0.2f;
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const double amp = 150.0 + 40.0 * ((gx * 7 + gy * 13) % 20); // <= ~950, safe for int16
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const double sigma = 1.3;
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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;
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const int y = static_cast<int>(std::lround(cy)) + dy;
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if (x < 0 || y < 0 || x >= static_cast<int>(width) || y >= static_cast<int>(height)) continue;
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const double ex = x - cx, ey = y - cy;
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s.image[y * width + x] += static_cast<int32_t>(std::lround(amp * std::exp(-(ex * ex + ey * ey) / (2.0 * sigma * sigma))));
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}
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const float d = 1.4f + 0.12f * static_cast<float>((gx + gy) % 12);
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s.predicted.push_back(MakeReflection(cx, cy, d, hkl++));
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}
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for (int k = 0; k < 20; ++k) {
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const size_t idx = (static_cast<size_t>(k) * 2654435761u) % s.image.size();
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s.image[idx] = (k % 2) ? INT32_MIN : INT32_MAX;
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}
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return s;
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}
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DiffractionExperiment MakeExperiment(IntegratorMode mode) {
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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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BraggIntegrationSettings settings;
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settings.Integrator(mode);
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experiment.ImportBraggIntegrationSettings(settings);
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return experiment;
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}
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void RequireIdentical(const std::vector<Reflection> &a, const std::vector<Reflection> &b) {
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REQUIRE(a.size() == b.size());
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REQUIRE(a.size() > 40);
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for (size_t i = 0; i < a.size(); ++i) {
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INFO("reflection " << i << " hkl " << a[i].h);
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CHECK(a[i].h == b[i].h);
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CHECK(a[i].I == b[i].I);
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CHECK(a[i].sigma == b[i].sigma);
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CHECK(a[i].bkg == b[i].bkg);
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}
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}
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} // namespace
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TEST_CASE("BraggIntegrationEngineCPU_CompressedImageMatchesBuffer", "[Integration][portable]") {
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for (const auto mode : {IntegratorMode::BoxSum, IntegratorMode::ProfileGaussian, IntegratorMode::ProfileEmpirical}) {
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const DiffractionExperiment experiment = MakeExperiment(mode);
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const size_t W = experiment.GetXPixelsNum(), H = experiment.GetYPixelsNum();
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const size_t npixel = experiment.GetPixelsNum();
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const Scene scene = BuildScene(W, H);
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REQUIRE(scene.image.size() == npixel);
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BraggIntegrationEngineCPU engine(experiment);
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// Route A: the preprocessed int32 buffer.
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ImagePreprocessorBuffer buffer(npixel);
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for (size_t i = 0; i < npixel; ++i) buffer[i] = scene.image[i];
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const auto from_buffer = engine.Run(buffer, scene.predicted, scene.predicted.size(), 7);
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SECTION("Int32 CompressedImage") {
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const CompressedImage image(scene.image, W, H);
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const auto from_image = engine.Run(image, scene.predicted, scene.predicted.size(), 7);
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RequireIdentical(from_buffer, from_image);
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}
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SECTION("Int16 CompressedImage") {
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// Same scene as int16 (masked -> INT16_MIN, saturated -> INT16_MAX) with a matching int32
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// buffer built by the exact sampler mapping; the two must integrate identically.
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std::vector<int16_t> img16(npixel);
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std::vector<int32_t> buf32(npixel);
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for (size_t i = 0; i < npixel; ++i) {
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const int32_t v = scene.image[i];
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if (v == INT32_MIN) { img16[i] = INT16_MIN; buf32[i] = INT32_MIN; }
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else if (v == INT32_MAX) { img16[i] = INT16_MAX; buf32[i] = INT32_MAX; }
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else { img16[i] = static_cast<int16_t>(v); buf32[i] = v; }
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}
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ImagePreprocessorBuffer buffer16(npixel);
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for (size_t i = 0; i < npixel; ++i) buffer16[i] = buf32[i];
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const auto ref16 = engine.Run(buffer16, scene.predicted, scene.predicted.size(), 7);
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const CompressedImage image(img16, W, H);
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const auto from_image = engine.Run(image, scene.predicted, scene.predicted.size(), 7);
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RequireIdentical(ref16, from_image);
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}
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}
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}
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