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Jungfraujoch/image_analysis/MXAnalysisWithoutFPGA.h
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v1.0.0-rc.156 (#66)
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>
2026-07-03 19:18:56 +02:00

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2.5 KiB
C++

// SPDX-FileCopyrightText: 2024 Filip Leonarski, Paul Scherrer Institute <filip.leonarski@psi.ch>
// SPDX-License-Identifier: GPL-3.0-only
#pragma once
#include <mutex>
#include "../common/JFJochMessages.h"
#include "../common/DiffractionExperiment.h"
#include "../common/AzimuthalIntegrationMapping.h"
#include "../common/PixelMask.h"
#include "../common/AzimuthalIntegrationProfile.h"
#include "bragg_prediction/BraggPrediction.h"
#include "bragg_integration/BraggIntegrationEngine.h"
#include "spot_finding/ImageSpotFinder.h"
#include "indexing/IndexerThreadPool.h"
#include "azint/AzIntEngine.h"
#include "roi/ROIIntegration.h"
#include "IndexAndRefine.h"
#include "image_preprocessing/ImagePreprocessor.h"
#include "image_preprocessing/ImagePreprocessorBuffer.h"
class CudaStream;
// MXAnalysisWithoutFPGA is not thread safe - it has to owned by a single thread
class MXAnalysisWithoutFPGA {
const DiffractionExperiment &experiment;
const AzimuthalIntegrationMapping &integration;
std::vector<uint8_t> decompression_buffer;
std::unique_ptr<ImagePreprocessor> preprocessor;
size_t npixels;
size_t xpixels;
std::unique_ptr<AzIntEngine> azint;
std::unique_ptr<ROIIntegration> roi;
std::unique_ptr<ImageSpotFinder> spotFinder;
IndexAndRefine &indexer;
std::unique_ptr<BraggPrediction> prediction;
std::unique_ptr<BraggIntegrationEngine> bragg_engine;
std::unique_ptr<ImagePreprocessorBuffer> preprocessor_buffer;
const PixelMask &mask;
std::vector<bool> mask_resolution;
float mask_high_res;
float mask_low_res;
void UpdateMaskResolution(const SpotFindingSettings& settings);
#ifdef JFJOCH_USE_CUDA
std::shared_ptr<CudaStream> stream; // kept so RebuildROI() can recreate the GPU ROI engine
#endif
public:
MXAnalysisWithoutFPGA(const DiffractionExperiment &experiment, const AzimuthalIntegrationMapping &integration,
const PixelMask &mask, IndexAndRefine &indexer);
void Analyze(DataMessage &output, AzimuthalIntegrationProfile &profile, const SpotFindingSettings &spot_finding_settings);
// Surgical ROI-only paths used when a full re-analysis is not wanted: rebuild the
// ROI engine after the ROI set changes, recompute ROIs after preprocessing a new
// image (reanalyze off), or just rerun ROIs on the current preprocessed image (an
// interactive ROI move). A full Analyze() already computes ROIs, so needs nothing.
void RebuildROI();
void AnalyzeROIOnly(DataMessage &output);
void RunROIOnly(DataMessage &output);
};