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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. * jfjoch_process: Major rotation (rot3d) data processing overhaul - robust profile-fit integration, Cauchy-loss scaling with optional absorption surface, de-novo indexing and space-group/centering determination fixes, and merging statistics + ISa in the mmCIF output. * jfjoch_process: Add EXPERIMENTAL ice-ring detection (--detect-ice-rings) that excludes ice reflections from scaling. * Compression: Add BSHUF_ZSTD_RLE_HUFF, make compression size-aware (drop frames that don't fit rather than aborting), and add the jfjoch_recompress tool. * jfjoch_viewer: Report "Multiple lattices detected" and grey out "Analyze dataset" on a live connection. * jfjoch_broker: Write smargon chi/phi goniometer positions to NXmx; read sensor thickness/material from HDF5 metadata. * CI: Build Windows (CUDA and non-CUDA) installers.Reviewed-on: #66 Co-authored-by: Filip Leonarski <filip.leonarski@psi.ch>
60 lines
2.4 KiB
C++
60 lines
2.4 KiB
C++
// SPDX-FileCopyrightText: 2024 Filip Leonarski, Paul Scherrer Institute <filip.leonarski@psi.ch>
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// SPDX-License-Identifier: GPL-3.0-only
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#pragma once
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#include "../common/DiffractionExperiment.h"
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#include "bragg_prediction/BraggPrediction.h"
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#include "bragg_integration/BraggIntegrationEngineCPU.h"
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#include "indexing/IndexerThreadPool.h"
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#include "spot_finding/StrongPixelSet.h"
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#include "azint/AzIntEngineCPU.h"
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#include "IndexAndRefine.h"
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class MXAnalysisAfterFPGA {
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mutable std::mutex read_from_cpu_mutex;
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const DiffractionExperiment &experiment;
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const AzimuthalIntegrationMapping &integration;
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IndexAndRefine &indexer;
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std::unique_ptr<BraggPrediction> prediction;
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std::unique_ptr<AzIntEngineCPU> cpu_azint;
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// The FPGA host has no usable GPU bandwidth for integration, so Bragg integration here is always on
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// the CPU, reading the assembled detector image straight (only the reflection disks - no copy).
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std::unique_ptr<BraggIntegrationEngineCPU> bragg_engine;
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bool find_spots = false;
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std::vector<DiffractionSpot> spots;
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constexpr static const float spot_distance_threshold_pxl = 2.0f;
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std::vector<float> arr_mean;
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std::vector<float> arr_sttdev;
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std::vector<uint32_t> arr_valid_count;
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std::vector<uint32_t> arr_strong_pixel;
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enum class State {Idle, Disabled, Enabled} state = State::Idle;
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std::chrono::duration<double, std::micro> spot_finding_time_total{0.0};
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bool spot_finding_timing_active = false;
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public:
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MXAnalysisAfterFPGA(const DiffractionExperiment& experiment,
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const AzimuthalIntegrationMapping &integration,
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IndexAndRefine &indexer);
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void ReadFromFPGA(const DeviceOutput* output,
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const SpotFindingSettings& settings,
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size_t module_number);
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void ReadFromCPU(DeviceOutput *output,
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const SpotFindingSettings &settings,
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size_t module_number);
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// Computes the azimuthal integration profile on the CPU from the assembled image.
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// Only active when the settings force the CPU backend; otherwise a no-op (the FPGA
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// fills the profile). image points to the uncompressed image of GetByteDepthImage() pixels.
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void RunAzimuthalIntegration(const void *image, AzimuthalIntegrationProfile &profile);
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void Process(DataMessage &message, const SpotFindingSettings& settings);
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};
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