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This is an UNSTABLE release. It includes many experimental features, as well as many AI generated fixes. We recommend using rc.152 for production use. * rugnux: Add `--model model.pdb` - score the merged data against an atomic model and compute initial maps. It reports R-work/R-free (scaling the model to the observed amplitudes with an overall scale, an anisotropic B and a flat bulk solvent - the standard few-parameter model, so a batch of maps stays directly comparable) and writes 2Fo-Fc / Fo-Fc electron-density maps (CCP4) plus a map-coefficient MTZ. The structure itself is not refined; the model is only re-fractionalised into the data cell. * rugnux: The merged reflection output now carries French-Wilson amplitudes (|F| and its sigma) next to the intensities - MTZ `F`/`SIGF`, mmCIF `_refln.F_meas_au`, and the text HKL - computed with the correct centric/acentric Wilson prior and epsilon multiplicity, so a downstream program (e.g. phenix.refine) can refine against amplitudes. The intensity columns are unchanged. * rugnux: R-free test-set flags are now assigned deterministically and consistently across symmetry - a Bijvoet pair I(+)/I(-) is never split between the work and free sets, and the assignment is a reproducible per-hkl hash that depends only on the reflection index, so every dataset of one crystal form gets the same ~5% free set (what a multi-dataset campaign such as PanDDA needs). On small data the fraction is floored so the test set stays large enough for a stable R-free (~500 reflections, capped at 10%); it stays flat at 5% on ordinary data. When a reference MTZ carries a `FreeR_flag` column its test set is imported instead, letting a whole campaign inherit one shared free set. * rugnux: A reference MTZ (`--reference-mtz`) can now fix the space group and cell for rotation data too (previously rejected), without being used to scale - the rotation merge stays self-consistent. When the crystal has an indexing (merohedral) ambiguity - a lattice symmetry higher than its Laue symmetry, e.g. P3/P4/P6/C2 - the reference also resolves it: each candidate reindexing (identity plus the twin-law cosets of the metric symmetry) is scored by its intensity correlation against the reference and the data are re-merged in the best-correlating one. This is a metric-preserving relabelling of hkl (the cell is unchanged) and a no-op for a holohedral crystal such as lysozyme. * rugnux: `--model` validation now aligns the data to the model before scoring - the observed reflections are reindexed into the model's enantiomorph when the two differ only by hand (indistinguishable from merged intensities). A merohedral indexing ambiguity is resolved against the reference MTZ when one is given (so a whole campaign shares one indexing convention); only with a model and no reference does validation fall back to fitting each candidate reindexing and keeping the lowest R-free. * rugnux: De-novo symmetry - recover a genuine high-symmetry group whose data are imperfectly scaled. Such a merge's within-orbit chi² lands just past the self-consistency bound (each real symmetry step adds a little systematic scatter), right where a merohedral twin also lands, so the chi² ratio alone cannot separate them. The candidate is now rescued when the extra intensity-proportional systematic error it invokes stays small relative to the confirmed subgroup - a genuine symmetry step gains multiplicity without inflating the merge error model's b, whereas a twin forces non-equivalent reflections together and b balloons. Fixes cubic insulin (I23 instead of I222) with no change to any other crystal in the test battery, including the twins that must stay in their lower symmetry. * Docs: Document the French-Wilson amplitude estimation, R-free flagging, reference-based space-group/ambiguity resolution, and model-based validation/maps in CPU_DATA_ANALYSIS.md. * Frontend: The status-bar pill now shows a progress bar during detector calibration (previously only during measurement), and the calibration state and its button are labelled "Calibration"/"CALIBRATE" (the internal `Pedestal` state name is unchanged for back-compatibility).Reviewed-on: #70 Co-authored-by: Filip Leonarski <filip.leonarski@psi.ch>
215 lines
8.2 KiB
C++
215 lines
8.2 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 "../common/print_license.h"
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#include "../common/Logger.h"
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#include "../common/DiffractionExperiment.h"
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#include "../writer/HDF5Objects.h"
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#include "../common/PixelMask.h"
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#include "../image_pusher/HDF5FilePusher.h"
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#include "../image_puller/TestImagePuller.h"
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#include "../acquisition_device/AcquisitionDeviceGroup.h"
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#include "../receiver/JFJochReceiverService.h"
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#include "JFJochCompressor.h"
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void print_usage(Logger &logger) {
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logger.Info("Usage ./jfjoch_fpga_test {<options>} <path to repository>");
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logger.Info("Options:");
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logger.Info(" -i<num> Number of images");
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logger.Info(" -N<num> Number of image processing threads (default: 8)");
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logger.Info(" -F{<txt>} Write file, optional parameter is name (default: lite_perf_test)");
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logger.Info(" -X<txt> Indexing (none|fft|fftw|ffbidx), ffbidx is default");
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logger.Info(" -t<num> Indexing thread pool size (default: 4)");
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logger.Info(" -f<num> FFT indexing search vectors");
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logger.Info(" -Q Quick integration");
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logger.Info(" -G Geometry refinement");
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}
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int main(int argc, char **argv) {
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print_license("jfjoch_fpga_test");
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Logger logger("jfjoch_fpga_test");
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logger.Verbose(false);
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bool use_geom = false;
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uint16_t nthreads = 8;
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size_t nimages = 1000;
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std::string numa_policy_name;
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std::string filename = "";
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std::string ml_model = "";
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IndexingAlgorithmEnum indexing = IndexingAlgorithmEnum::FFBIDX;
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std::optional<int64_t> fft_num_vectors;
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uint16_t indexing_threads = 4;
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bool quick_integrate = false;
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RegisterHDF5Filter();
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if (argc == 1) {
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print_usage(logger);
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exit(EXIT_FAILURE);
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}
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int opt;
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while ((opt = getopt(argc, argv, "N:P:i:F::QGvX:t:f:")) != -1) {
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switch (opt) {
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case 'N':
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nthreads = atol(optarg);
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break;
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case 'P':
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logger.Warning("NUMA policy is deprecated and ignored");
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break;
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case 'i':
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nimages = atol(optarg);
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break;
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case 'X':
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if (std::string(optarg) == "none")
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indexing = IndexingAlgorithmEnum::None;
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else if (std::string(optarg) == "fft" || std::string(optarg) == "FFT")
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indexing = IndexingAlgorithmEnum::FFT;
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else if (std::string(optarg) == "ffbidx" || std::string(optarg) == "FFBIDX")
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indexing = IndexingAlgorithmEnum::FFBIDX;
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else if (std::string(optarg) == "fftw" || std::string(optarg) == "FFTW")
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indexing = IndexingAlgorithmEnum::FFTW;
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break;
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case 't':
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indexing_threads = atol(optarg);
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break;
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case 'f':
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fft_num_vectors = atol(optarg);
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break;
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case 'F':
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if (optarg)
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filename = std::string(optarg);
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else
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filename = "lite_perf_test";
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break;
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case 'Q':
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quick_integrate = true;
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break;
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case 'G':
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use_geom = true;
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break;
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case 'v':
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logger.Verbose(true);
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break;
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default: /* '?' */
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print_usage(logger);
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exit(EXIT_FAILURE);
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}
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}
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if (optind != argc - 1) {
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print_usage(logger);
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exit(EXIT_FAILURE);
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}
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std::string jfjoch_path = std::string(argv[optind]) + "/";
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DiffractionExperiment experiment(DetDECTRIS(2068, 2164, "Test", {}));
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experiment.ImagesPerTrigger(nimages).NumTriggers(1).UseInternalPacketGenerator(true).ImagesPerFile(1000)
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.FilePrefix(filename).JungfrauConvPhotonCnt(false).SetFileWriterFormat(FileWriterFormat::NXmxVDS).OverwriteExistingFiles(true)
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.DetectorDistance_mm(75).BeamY_pxl(1136).BeamX_pxl(1090).IncidentEnergy_keV(12.4)
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.SetUnitCell(UnitCell{.a = 36.9, .b = 78.95, .c = 78.95, .alpha =90, .beta = 90, .gamma = 90}).PixelSigned(true);
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PixelMask pixel_mask(experiment);
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experiment.ROI().SetROI(ROIDefinition{.boxes = {ROIBox("ROI1", 123, 180, 500,800) }});
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// Load example image
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HDF5ReadOnlyFile data(jfjoch_path + "tests/test_data/compression_benchmark.h5");
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HDF5DataSet dataset(data, "/entry/data/data");
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HDF5DataSpace file_space(dataset);
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std::vector<int16_t> image_conv(file_space.GetDimensions()[1] * file_space.GetDimensions()[2]);
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std::vector<hsize_t> start = {4,0,0};
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std::vector<hsize_t> file_size = {1, file_space.GetDimensions()[1], file_space.GetDimensions()[2]};
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dataset.ReadVector(image_conv, start, file_size);
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JFJochBitShuffleCompressor compressor(CompressionAlgorithm::BSHUF_LZ4);
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auto image_compressed = compressor.Compress(image_conv);
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HDF5FilePusher pusher;
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auto puller = std::make_shared<TestImagePuller>(nimages + 5);
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StartMessage start_msg;
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experiment.FillMessage(start_msg);
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puller->Put(ImagePullerOutput{
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.cbor = std::make_shared<CBORStream2DeserializerOutput>(start_msg)
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});
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DataMessage data_msg;
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data_msg.image = CompressedImage(image_compressed,
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file_space.GetDimensions()[2],
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file_space.GetDimensions()[1],
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CompressedImageMode::Int16,
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CompressionAlgorithm::BSHUF_LZ4);
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for (int i = 0; i < experiment.GetImageNum(); i++) {
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data_msg.number = i;
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puller->Put(ImagePullerOutput{
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.cbor = std::make_shared<CBORStream2DeserializerOutput>(data_msg)
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});
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}
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EndMessage end_msg{};
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puller->Put(ImagePullerOutput{
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.cbor = std::make_shared<CBORStream2DeserializerOutput>(end_msg)
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});
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AcquisitionDeviceGroup group;
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JFJochReceiverService service(group, logger, pusher);
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service.NumThreads(nthreads);
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IndexingSettings i_settings;
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i_settings.Algorithm(indexing);
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if (fft_num_vectors)
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i_settings.FFT_NumVectors(fft_num_vectors.value());
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i_settings.IndexingThreads(indexing_threads);
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if (use_geom)
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i_settings.GeomRefinementAlgorithm(GeomRefinementAlgorithmEnum::BeamCenter);
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experiment.ImportIndexingSettings(i_settings);
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service.Indexing(i_settings);
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SpotFindingSettings settings = DiffractionExperiment::DefaultDataProcessingSettings();
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settings.signal_to_noise_threshold = 2.5;
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settings.photon_count_threshold = 5;
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settings.min_pix_per_spot = 1;
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settings.max_pix_per_spot = 200;
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settings.high_resolution_limit = 2.0;
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settings.low_resolution_limit = 50.0;
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settings.quick_integration = quick_integrate;
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service.SetSpotFindingSettings(settings);
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auto start_time = std::chrono::system_clock::now();
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service.Start(experiment, pixel_mask, nullptr, puller);
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auto output = service.Stop();
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auto end_time = std::chrono::system_clock::now();
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double receiving_time_s = static_cast<double>(output.end_time_ms - output.start_time_ms) / 1000.0;
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logger.Info("Throughput {:.1f} Hz", experiment.GetImageNum() / receiving_time_s);
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if (output.status.indexing_rate)
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logger.Info("Indexing rate {:.0f}%", output.status.indexing_rate.value() * 100.0);
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if (output.status.max_receive_delay)
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logger.Info("Max delay {}", output.status.max_receive_delay.value());
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logger.Info("Per-image time: (mean; microseconds): compression {:.0f} preprocess {:.0f} spot finding {:.0f} indexing {:.0f} refinement {:.0f} indexing analysis {:.0f} prediction {:.0f} integration {:.0f} total {:.0f}",
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output.processing_time.compression * 1e6,
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output.processing_time.preprocessing * 1e6,
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output.processing_time.spot_finding * 1e6,
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output.processing_time.indexing * 1e6,
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output.processing_time.refinement * 1e6,
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output.processing_time.indexing_analysis * 1e6,
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output.processing_time.bragg_prediction * 1e6,
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output.processing_time.integration * 1e6,
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output.processing_time.processing * 1e6);
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}
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