Merge branch '2405-versioning' into 'main'

Improve release/versioning of Jungfraujoch repository

See merge request jungfraujoch/nextgendcu!58

.gitignore

@@ -0,0 +1,3 @@
+cmake-build-debug/
+cmake-build-release/
+build*/

.gitlab-ci.yml

@@ -7,7 +7,6 @@ stages:
 build:x86:gcc:
   stage: build
   variables:
-    GIT_SUBMODULE_STRATEGY: recursive
     CC: gcc
     CXX: g++
   tags:
@@ -24,7 +23,6 @@ build:x86:gcc:
 build:x86:gcc_writer:
   stage: build
   variables:
-    GIT_SUBMODULE_STRATEGY: recursive
     CC: gcc
     CXX: g++
   tags:
@@ -41,7 +39,6 @@ build:x86:gcc_writer:
 build:x86:driver:
   stage: build
   variables:
-    GIT_SUBMODULE_STRATEGY: recursive
     CC: gcc
     CXX: g++
   tags:
@@ -60,8 +57,6 @@ build:x86:driver:
 
 build:x86:vitis_hls:
   stage: build
-  variables:
-    GIT_SUBMODULE_STRATEGY: recursive
   tags:
     - x86
   needs: []
@@ -84,8 +79,6 @@ build:x86:vitis_hls:
 
 build:x86:frontend:
   stage: build
-  variables:
-    GIT_SUBMODULE_STRATEGY: recursive
   tags:
     - x86
   needs: []
@@ -104,7 +97,6 @@ test:x86:gcc:
   stage: test
   timeout: 90m
   variables:
-    GIT_SUBMODULE_STRATEGY: recursive
     CTEST_OUTPUT_ON_FAILURE: 1
     CC: gcc
     CXX: g++
@@ -118,9 +110,9 @@ test:x86:gcc:
     - mkdir -p build
     - cd build
     - cmake -DCMAKE_BUILD_TYPE=Release ..
-    - make -j48 CatchTest HDF5DatasetWriteTest
+    - make -j48 jfjoch_test HDF5DatasetWriteTest
     - cd tests
-    - ./CatchTest -r junit -o report.xml
+    - ./jfjoch_test -r junit -o report.xml
     - cd ../tools
     - ./HDF5DatasetWriteTest ../../tests/test_data/compression_benchmark.h5
   artifacts:
@@ -132,7 +124,6 @@ test:x86:crystfel:
   stage: test
   timeout: 90m
   variables:
-    GIT_SUBMODULE_STRATEGY: recursive
     CTEST_OUTPUT_ON_FAILURE: 1
     CC: gcc
     CXX: g++
@@ -156,7 +147,6 @@ test:x86:xds_durin:
   stage: test
   timeout: 90m
   variables:
-    GIT_SUBMODULE_STRATEGY: recursive
     CTEST_OUTPUT_ON_FAILURE: 1
     CC: gcc
     CXX: g++
@@ -180,7 +170,6 @@ test:x86:xds_neggia:
   stage: test
   timeout: 90m
   variables:
-    GIT_SUBMODULE_STRATEGY: recursive
     CTEST_OUTPUT_ON_FAILURE: 1
     CC: gcc
     CXX: g++
@@ -204,7 +193,6 @@ test:x86:xia2.ssx:
   stage: test
   timeout: 90m
   variables:
-    GIT_SUBMODULE_STRATEGY: recursive
     CTEST_OUTPUT_ON_FAILURE: 1
     CC: gcc
     CXX: g++
@@ -230,7 +218,6 @@ synthesis:vivado_pcie_100g:
   stage: synthesis
   dependencies: []
   variables:
-    GIT_SUBMODULE_STRATEGY: recursive
     CC: gcc
     CXX: g++
   rules:
@@ -243,8 +230,6 @@ synthesis:vivado_pcie_100g:
         - fpga/scripts/*
         - fpga/xdc/*
         - fpga/pcie_driver/jfjoch_fpga.h
-    - when: manual
-      allow_failure: true
   tags:
     - vivado
   artifacts:
@@ -266,7 +251,6 @@ synthesis:vivado_pcie_8x10g:
   stage: synthesis
   dependencies: []
   variables:
-    GIT_SUBMODULE_STRATEGY: recursive
     CC: gcc
     CXX: g++
   rules:
@@ -279,8 +263,6 @@ synthesis:vivado_pcie_8x10g:
         - fpga/xdc/*
         - fpga/pcie_driver/jfjoch_fpga.h
       allow_failure: true
-    - when: manual
-      allow_failure: true
   tags:
     - vivado
   artifacts:
@@ -300,7 +282,9 @@ synthesis:vivado_pcie_8x10g:
 
 release:
   stage: release
-  when: manual
+  rules:
+    - if: $CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH
+      when: manual
   tags:
     - x86
   dependencies:

CMakeLists.txt

@@ -31,13 +31,6 @@ SET(JFJOCH_WRITER_ONLY    OFF CACHE BOOL "Compile HDF5 writer only")
 INCLUDE_DIRECTORIES(include)
 INCLUDE(CheckIncludeFile)
 
-#This is to supress error in TORCH
-
-IF ((NOT EXISTS /usr/bin/python) AND (EXISTS /usr/bin/python3))
-    SET(PYTHON_EXECUTABLE /usr/bin/python3)
-ENDIF()
-
-FIND_PACKAGE(Torch HINTS /opt/libtorch/share/cmake/Torch/)
 FIND_LIBRARY(NUMA_LIBRARY NAMES numa DOC "NUMA Library")
 CHECK_INCLUDE_FILE(numaif.h HAS_NUMAIF)
 CHECK_INCLUDE_FILE(numa.h HAS_NUMA_H)
@@ -90,8 +83,7 @@ ELSE()
     ADD_SUBDIRECTORY(tests)
     ADD_SUBDIRECTORY(tools)
     ADD_SUBDIRECTORY(preview)
-    ADD_SUBDIRECTORY(resonet)
-    SET(jfjoch_executables jfjoch_broker jfjoch_writer CatchTest CompressionBenchmark HDF5DatasetWriteTest jfjoch_udp_simulator sls_detector_put sls_detector_get)
+    SET(jfjoch_executables jfjoch_broker jfjoch_writer jfjoch_test CompressionBenchmark HDF5DatasetWriteTest jfjoch_udp_simulator sls_detector_put sls_detector_get)
 ENDIF()
 
 ADD_CUSTOM_COMMAND(OUTPUT frontend_ui/build/index.html

README.md

@@ -39,10 +39,9 @@ Required:
 * JPEG library (turbo-jpeg is also OK)
 
 Optional:
-* CUDA compiler version 11 or newer - required for MX indexing and ML resolution estimation
+* CUDA compiler version 11 or newer - required for MX fast feedback indexer
 * NUMA library - to pin threads to nodes/CPUs
 * Node.js - to make frontend
-* libtorch - for resolution estimation using model from Stanford - see below
 
 Automatically downloaded by CMake: 
 * SLS Detector Package - see [github.com/slsdetectorgroup/slsDetectorPackage](https://github.com/slsdetectorgroup/slsDetectorPackage)
@@ -76,7 +75,6 @@ For license check LICENSE file in respective directory
 Use the following commands:
 
 ```
-git submodule update --init --recursive
 mkdir build
 cd build
 cmake ..
@@ -88,7 +86,6 @@ In most use cases it is better to have a separate machine, with access to distri
 Such machine needs only a HDF5 writer service with fewer dependencies. For compilation use the following commands:
 
 ```
-git submodule update --init --recursive
 mkdir build
 cd build
 cmake -DJFJOCH_WRITER_ONLY=ON ..
@@ -112,7 +109,6 @@ Frontend is written in TypeScript. For details see [frontend_ui/](frontend_ui) d
 
 Jungfraujoch Cmake scripts have an option to start frontend build with the following command:
 ```
-git submodule update --init --recursive
 mkdir build
 cd build
 cmake ..
@@ -122,7 +118,7 @@ Contrary to standard CMake way, frontend will be built in "source" `frontend_ui/
 
 ## Tests
  
-Automated test routine is then accessible as `tests/CatchTest`. There are also benchmark routines:
+Automated test routine is then accessible as `tests/jfjoch_test`. There are also benchmark routines:
 
 * `HDF5DatasetWriteTest` to measure HDF5 dataset writing speed (single threaded)
 * `jfjoch_spot_finding_test` to apply spot finding and indexing routines in Jungfraujoch to an example dataset - this is equivalent to FPGA spot finding algorithm, but NOT performance equivalent as it is particularly not-efficient
@@ -130,16 +126,3 @@ Automated test routine is then accessible as `tests/CatchTest`. There are also b
 
 In addition, tests are executed to verify that datasets written by Jungfraujoch are readable with XDS Durin plugin, XDS Neggia plygin and CrystFEL. 
 Input files for these programs are placed in `xds_durin`, `xds_neggia` and `crystfel` folders. See `.gitlab-ci.yml` for details.
-
-## Resolution estimation
-
-Resolution estimation can be done with a recent deep learning model by D. Mendez et al. 
-(see [Acta Cryst D, 80, 26-43](https://doi.org/10.1107/S2059798323010586)), adapted to Jungfraujoch. 
-Model used in the original paper is located in the [resonet/](resonet) directory, after converting to TorchScript format.
-
-To use the feature it is necessary to install [libtorch](https://pytorch.org/) library, preferably in `/opt/libtorch` location. The C++11 ABI version needs to be chosen.
-For RHEL 8 systems, please download older version 2.1.0, as version 2.2.0 requires newer `glibc` library than available with the operating system. 
-Version 2.1.0 can be downloaded with the following command:
-```
-wget https://download.pytorch.org/libtorch/cu121/libtorch-cxx11-abi-shared-with-deps-2.1.0%2Bcu121.zip
-```

VERSION

@@ -1 +1 @@
-1.0.0-test5
+1.0.0-rc.1

broker/JFJochBrokerHttp.cpp

@@ -651,10 +651,6 @@ void JFJochBrokerHttp::preview_image_tiff_get(Pistache::Http::ResponseWriter &re
         response.send(Pistache::Http::Code::Not_Found);
 }
 
-void JFJochBrokerHttp::plot_resolution_estimate_histogram_get(Pistache::Http::ResponseWriter &response) {
-    GenericPlot(PlotType::ResEstimation, 0, response);
-}
-
 void JFJochBrokerHttp::config_internal_generator_image_put(const Pistache::Rest::Request &request,
                                                            Pistache::Http::ResponseWriter &response) {
     int64_t image_number = 0;

broker/JFJochBrokerHttp.h

@@ -75,8 +75,6 @@ class JFJochBrokerHttp : public org::openapitools::server::api::DefaultApi {
 
     void plot_rad_int_per_file_get(Pistache::Http::ResponseWriter &response) override;
 
-    void plot_resolution_estimate_histogram_get(Pistache::Http::ResponseWriter &response) override;
-
     void statistics_calibration_get(Pistache::Http::ResponseWriter &response) override;
 
     void statistics_data_collection_get(Pistache::Http::ResponseWriter &response) override;

broker/JFJochBrokerParser.cpp

@@ -319,7 +319,6 @@ void ParseFacilityConfiguration(const nlohmann::json &input, const std::string&
                                       "omega_axis must be float array of 3");
         }
 
-        experiment.NeuralNetModelPath(GET_STR(j, "neural_net_model", ""));
         if (j.contains("pedestal_g0_frames"))
             experiment.PedestalG0Frames(GET_I64(j, "pedestal_g0_frames"));
         if (j.contains("pedestal_g1_frames"))

broker/README.md

@@ -25,7 +25,6 @@ To run the test:
 
 ### Compile Jungfraujoch with frontend
 ```
-git submodule update --init --recursive
 mkdir build
 cd build
 cmake ..

broker/gen/api/DefaultApi.cpp

@@ -56,7 +56,6 @@ void DefaultApi::setupRoutes() {
     Routes::Get(*router, base + "/plot/rad_int_per_file", Routes::bind(&DefaultApi::plot_rad_int_per_file_get_handler, this));
     Routes::Get(*router, base + "/plot/receiver_delay", Routes::bind(&DefaultApi::plot_receiver_delay_get_handler, this));
     Routes::Get(*router, base + "/plot/receiver_free_send_buffers", Routes::bind(&DefaultApi::plot_receiver_free_send_buffers_get_handler, this));
-    Routes::Get(*router, base + "/plot/resolution_estimate_histogram", Routes::bind(&DefaultApi::plot_resolution_estimate_histogram_get_handler, this));
     Routes::Get(*router, base + "/plot/roi_max_count", Routes::bind(&DefaultApi::plot_roi_max_count_get_handler, this));
     Routes::Get(*router, base + "/plot/roi_sum", Routes::bind(&DefaultApi::plot_roi_sum_get_handler, this));
     Routes::Get(*router, base + "/plot/roi_valid_pixels", Routes::bind(&DefaultApi::plot_roi_valid_pixels_get_handler, this));
@@ -672,26 +671,6 @@ void DefaultApi::plot_receiver_free_send_buffers_get_handler(const Pistache::Res
         response.send(Pistache::Http::Code::Internal_Server_Error, e.what());
     }
 
-}
-void DefaultApi::plot_resolution_estimate_histogram_get_handler(const Pistache::Rest::Request &, Pistache::Http::ResponseWriter response) {
-    try {
-
-
-    try {
-        this->plot_resolution_estimate_histogram_get(response);
-    } catch (Pistache::Http::HttpError &e) {
-        response.send(static_cast<Pistache::Http::Code>(e.code()), e.what());
-        return;
-    } catch (std::exception &e) {
-        const std::pair<Pistache::Http::Code, std::string> errorInfo = this->handleOperationException(e);
-        response.send(errorInfo.first, errorInfo.second);
-        return;
-    }
-
-    } catch (std::exception &e) {
-        response.send(Pistache::Http::Code::Internal_Server_Error, e.what());
-    }
-
 }
 void DefaultApi::plot_roi_max_count_get_handler(const Pistache::Rest::Request &request, Pistache::Http::ResponseWriter response) {
     try {

broker/gen/api/DefaultApi.h

@@ -80,7 +80,6 @@ private:
     void plot_rad_int_per_file_get_handler(const Pistache::Rest::Request &request, Pistache::Http::ResponseWriter response);
     void plot_receiver_delay_get_handler(const Pistache::Rest::Request &request, Pistache::Http::ResponseWriter response);
     void plot_receiver_free_send_buffers_get_handler(const Pistache::Rest::Request &request, Pistache::Http::ResponseWriter response);
-    void plot_resolution_estimate_histogram_get_handler(const Pistache::Rest::Request &request, Pistache::Http::ResponseWriter response);
     void plot_roi_max_count_get_handler(const Pistache::Rest::Request &request, Pistache::Http::ResponseWriter response);
     void plot_roi_sum_get_handler(const Pistache::Rest::Request &request, Pistache::Http::ResponseWriter response);
     void plot_roi_valid_pixels_get_handler(const Pistache::Rest::Request &request, Pistache::Http::ResponseWriter response);
@@ -293,13 +292,6 @@ private:
     /// <param name="binning">Binning of frames for the plot (0 &#x3D; default binning) (optional, default to 0)</param>
     virtual void plot_receiver_free_send_buffers_get(const std::optional<int32_t> &binning, Pistache::Http::ResponseWriter &response) = 0;
     /// <summary>
-    /// Generate resolution estimate histogram
-    /// </summary>
-    /// <remarks>
-    /// Generate histogram of crystal resolutions from 1.0 to 5.0 A based on ML model
-    /// </remarks>
-    virtual void plot_resolution_estimate_histogram_get(Pistache::Http::ResponseWriter &response) = 0;
-    /// <summary>
     /// Generate plot of ROI max count
     /// </summary>
     /// <remarks>

broker/jfjoch_api.yaml

@@ -1445,17 +1445,6 @@ paths:
             application/json:
               schema:
                 $ref: '#/components/schemas/plots'
-  /plot/resolution_estimate_histogram:
-    get:
-      summary: Generate resolution estimate histogram
-      description: Generate histogram of crystal resolutions from 1.0 to 5.0 A based on ML model
-      responses:
-        "200":
-          description: Everything OK
-          content:
-            application/json:
-              schema:
-                $ref: '#/components/schemas/plots'
   /plot/rad_int:
     get:
       summary: Generate radial integration profile

common/DiffractionExperiment.cpp

@@ -1064,15 +1064,6 @@ const DetectorSetup &DiffractionExperiment::GetDetectorSetup() const {
     return detector;
 }
 
-DiffractionExperiment &DiffractionExperiment::NeuralNetModelPath(const std::string &input) {
-    neural_net_model_path = input;
-    return *this;
-}
-
-std::string DiffractionExperiment::GetNeuralNetModelPath() const {
-    return neural_net_model_path;
-}
-
 DiffractionExperiment &DiffractionExperiment::PulsedSource(bool input) {
     pulsed_source = input;
     return *this;

common/DiffractionExperiment.h

@@ -89,8 +89,6 @@ class DiffractionExperiment {
 
     uint64_t series_id;
 
-    std::string neural_net_model_path;
-
     bool rad_int_solid_angle_corr;
     bool rad_int_polarization_corr;
     float rad_int_polarization_factor{};
@@ -144,7 +142,6 @@ public:
     DiffractionExperiment& FixedGainG1(bool input);
     DiffractionExperiment& IncrementSeriesID();
     DiffractionExperiment& ConversionOnFPGA(bool input);
-    DiffractionExperiment& NeuralNetModelPath(const std::string& input);
     DiffractionExperiment& PulsedSource(bool input);
 
     DiffractionExperiment& ImagesPerTrigger(int64_t input);
@@ -282,7 +279,6 @@ public:
     bool IsConversionOnFPGA() const;
 
     const DetectorSetup& GetDetectorSetup() const;
-    std::string GetNeuralNetModelPath() const;
 
     bool IsPulsedSource() const;
 

common/Plot.h

@@ -9,7 +9,7 @@
 
 enum class PlotType {BkgEstimate, RadInt, RadIntPerTimePoint, SpotCount, IndexingRate, IndexingRatePerTimePoint,
                      ErrorPixels, ImageCollectionEfficiency, ReceiverDelay, ReceiverFreeSendBuf, StrongPixels,
-                     ROISum, ROIMaxCount, ROIPixels, ResEstimation};
+                     ROISum, ROIMaxCount, ROIPixels};
 
 struct PlotRequest {
     PlotType type;

frame_serialize/JFJochMessages.h

@@ -75,8 +75,6 @@ struct DataMessage {
     std::optional<uint64_t> receiver_free_send_buf;
     std::optional<uint64_t> storage_cell;
 
-    std::optional<float> resolution_estimation;
-
     std::optional<uint64_t> xfel_pulse_id;
     std::optional<uint64_t> xfel_event_code;
 

frontend_ui/src/components/DataProcessingPlot.tsx

@@ -16,9 +16,7 @@ export enum PlotType {
     STRONG_PIXELS,
     ROI_SUM,
     ROI_MAX_COUNT,
-    RES_ESTIMATION,
-    RECEIVER_FREE_SEND_BUFS,
-    BEAM_CENTER_DRIFT
+    RECEIVER_FREE_SEND_BUFS
 }
 
 type MyProps = {
@@ -145,13 +143,6 @@ class DataProcessingPlot extends Component<MyProps, MyState> {
                         this.setState({connection_error: true});
                     });
                 break;
-            case PlotType.RES_ESTIMATION:
-                DefaultService.getPlotResolutionEstimateHistogram()
-                    .then(data => this.setState({plots: data, connection_error: false}))
-                    .catch(error => {
-                        this.setState({connection_error: true});
-                    });
-                break;
             case PlotType.RAD_INT_PER_FILE:
                 DefaultService.getPlotRadIntPerFile()
                     .then(data => this.setState({plots: data, connection_error: false}))

frontend_ui/src/components/DataProcessingPlots.tsx

@@ -69,9 +69,6 @@ class DataProcessingPlots extends Component<MyProps, MyState> {
             case "11":
                 this.setState({type: PlotType.ROI_SUM, xlabel: "Image number", ylabel: "Photon count"});
                 break;
-            case "12":
-                this.setState({type: PlotType.RES_ESTIMATION, xlabel: "Resolution [&#8491;]", ylabel: "Number of crystals"});
-                break;
             case "13":
                 this.setState({type: PlotType.RECEIVER_FREE_SEND_BUFS, xlabel: "Image number", ylabel: "Number of buffers"});
                 break;
@@ -100,7 +97,6 @@ class DataProcessingPlots extends Component<MyProps, MyState> {
                             <MenuItem value={3}>Azimuthal integration profile</MenuItem>
                             <MenuItem value={11}>ROI area sum</MenuItem>
                             <MenuItem value={6}>ROI area max count</MenuItem>
-                            <MenuItem value={12}>Crystal resolution histogram</MenuItem>
                             <MenuItem value={4}>Indexing rate (per time point)</MenuItem>
                             <MenuItem value={5}>Azimuthal integration profile (per time point)</MenuItem>
                             <MenuItem value={8}>Error and saturated pixels</MenuItem>

frontend_ui/src/openapi/services/DefaultService.ts

@@ -724,19 +724,6 @@ export class DefaultService {
         });
     }
 
-    /**
-     * Generate resolution estimate histogram
-     * Generate histogram of crystal resolutions from 1.0 to 5.0 A based on ML model
-     * @returns plots Everything OK
-     * @throws ApiError
-     */
-    public static getPlotResolutionEstimateHistogram(): CancelablePromise<plots> {
-        return __request(OpenAPI, {
-            method: 'GET',
-            url: '/plot/resolution_estimate_histogram',
-        });
-    }
-
     /**
      * Generate radial integration profile
      * Generate average radial integration profile

image_analysis/CMakeLists.txt

@@ -21,8 +21,8 @@ IF (CMAKE_CUDA_COMPILER)
 
     FetchContent_Declare(
             fast-indexer
-            GIT_REPOSITORY https://github.com/paulscherrerinstitute/fast-feedback-indexer/
-            GIT_TAG 2d6fa7511f240eeb4918dd7647edd0642e987317
+            GIT_REPOSITORY https://github.com/fleon-psi/fast-feedback-indexer/
+            GIT_TAG 651cbc9
     )
 
     FetchContent_MakeAvailable(fast-indexer)

receiver/CMakeLists.txt

@@ -11,7 +11,7 @@ ADD_LIBRARY(JFJochReceiver STATIC
         LossyFilter.cpp
         LossyFilter.h)
 
-TARGET_LINK_LIBRARIES(JFJochReceiver ImagePusher JFJochImageAnalysis JFJochAcquisitionDevice JFJochCommon JFJochHLSSimulation JFJochPreview JFJochResonet)
+TARGET_LINK_LIBRARIES(JFJochReceiver ImagePusher JFJochImageAnalysis JFJochAcquisitionDevice JFJochCommon JFJochHLSSimulation JFJochPreview)
 
 ADD_EXECUTABLE(jfjoch_action_test jfjoch_action_test.cpp)
 TARGET_LINK_LIBRARIES(jfjoch_action_test JFJochReceiver)

receiver/JFJochReceiver.cpp

@@ -7,7 +7,6 @@
 #include "../image_analysis/MXAnalyzer.h"
 #include "../common/DiffractionGeometry.h"
 #include "ImageMetadata.h"
-#include "../resonet/NeuralNetResPredictor.h"
 #include "../common/time_utc.h"
 #include "../common/PixelMask.h"
 
@@ -266,7 +265,6 @@ void JFJochReceiver::FrameTransformationThread() {
         Cancel(e);
     }
 
-    NeuralNetResPredictor neural_net_predictor(experiment.GetNeuralNetModelPath());
     FrameTransformation transformation(experiment);
 
     MXAnalyzer analyzer(experiment);
@@ -339,7 +337,6 @@ void JFJochReceiver::FrameTransformationThread() {
             message.receiver_free_send_buf = send_buf_ctrl.GetAvailBufLocations();
             message.az_int_profile = az_int_profile_image.GetResult();
             message.bkg_estimate = az_int_profile_image.GetMeanValueOfBins(az_int_min_bin, az_int_max_bin);
-            message.resolution_estimation = neural_net_predictor.Inference(experiment, transformation.GetImage());
 
             plots.Add(message, image_number % experiment.GetTimePointNumber(), az_int_profile_image);
 

receiver/JFJochReceiverPlots.cpp

@@ -6,7 +6,7 @@ JFJochReceiverPlots::JFJochReceiverPlots(const DiffractionExperiment &experiment
                                          const AzimuthalIntegrationMapping &mapping)
         : indexing_solution_per_time_point(experiment.GetTimePointNumber()),
           default_binning(experiment.GetDefaultPlotBinning()),
-          az_int_profile(mapping), resolution_estimation(50, 1.0, 5.0) {
+          az_int_profile(mapping) {
     az_int_profile.SetTitle("dataset");
     for (int i = 0; i < experiment.GetTimePointNumber(); i++) {
         az_int_profile_per_time_point.emplace_back(mapping);
@@ -36,9 +36,6 @@ void JFJochReceiverPlots::Add(const DataMessage &msg, uint64_t file_number, cons
     indexing_solution.AddElement(msg.number, msg.indexing_result);
     indexing_solution_per_time_point.Add(file_number, msg.indexing_result);
 
-    if (msg.resolution_estimation)
-        resolution_estimation.Add(msg.resolution_estimation.value());
-
     az_int_profile += profile;
     az_int_profile_per_time_point.at(file_number) += profile;
 
@@ -121,8 +118,6 @@ MultiLinePlot JFJochReceiverPlots::GetPlots(const PlotRequest &request) {
                 ret.emplace_back(tmp.at(0));
             }
             return ret;
-        case PlotType::ResEstimation:
-            return resolution_estimation.GetPlot();
         case PlotType::RadIntPerTimePoint:
             tmp = az_int_profile.GetPlot();
             ret.emplace_back(tmp.at(0));

receiver/JFJochReceiverPlots.h

@@ -31,7 +31,6 @@ class JFJochReceiverPlots {
     std::map<std::string, std::unique_ptr<StatusVector<int64_t>>> roi_sum;
     std::map<std::string, std::unique_ptr<StatusVector<int64_t>>> roi_max_count;
     std::map<std::string, std::unique_ptr<StatusVector<uint64_t>>> roi_pixels;
-    FloatHistogram resolution_estimation;
 
     AzimuthalIntegrationProfile az_int_profile;
     std::vector<AzimuthalIntegrationProfile> az_int_profile_per_time_point;

receiver/jfjoch_action_test.cpp

@@ -19,7 +19,6 @@ void print_usage(Logger &logger) {
     logger.Info("   -i<num>  number of images");
     logger.Info("   -N<num>  number of image processing threads");
     logger.Info("   -P<txt>  NUMA Policy (none|n2g2|n8g4|n8g4_hbm), none is default");
-    logger.Info("   -D<path> use resonet deep learning model for resolution estimation - path to TorchScript");
     logger.Info("   -B<num>  size of send buffer in MiB (default 2048)");
 }
 
@@ -37,7 +36,6 @@ int main(int argc, char **argv) {
     std::string numa_policy_name;
     bool raw_data = false;
     bool force_32bit = false;
-    std::string resonet_path;
     DetectorType detector_type = DetectorType::JUNGFRAU;
     bool hls_simulation = false;
     size_t send_buffer_size_MiB = 2048;
@@ -48,7 +46,7 @@ int main(int argc, char **argv) {
     }
 
     int opt;
-    while ((opt = getopt(argc, argv, "s:i:m:N:P:vRIS:D:EHB:")) != -1) {
+    while ((opt = getopt(argc, argv, "s:i:m:N:P:vRIS:EHB:")) != -1) {
         switch (opt) {
             case 'i':
                 nimages = atol(optarg);
@@ -77,9 +75,6 @@ int main(int argc, char **argv) {
             case 'I':
                 force_32bit = true;
                 break;
-            case 'D':
-                resonet_path = std::string(optarg);
-                break;
             case 'E':
                 detector_type = DetectorType::EIGER;
                 break;
@@ -114,8 +109,6 @@ int main(int argc, char **argv) {
 
     if (force_32bit)
         x.FPGAOutputMode(FPGAPixelOutput::Int32);
-    if (!resonet_path.empty())
-        x.NeuralNetModelPath(resonet_path);
 
     logger.Info("Data streams {} Total modules {} Total images {} Threads {}", nstreams, nmodules, nimages, nthreads);
 

resonet/CMakeLists.txt

@@ -1,10 +0,0 @@
-ADD_LIBRARY(JFJochResonet STATIC NeuralNetResPredictor.cpp NeuralNetResPredictor.h)
-TARGET_LINK_LIBRARIES(JFJochResonet JFJochCommon)
-
-IF (${TORCH_FOUND})
-    TARGET_COMPILE_DEFINITIONS(JFJochResonet PUBLIC -DJFJOCH_USE_TORCH)
-    TARGET_LINK_LIBRARIES(JFJochResonet ${TORCH_LIBRARIES})
-    TARGET_INCLUDE_DIRECTORIES(JFJochResonet PUBLIC ${TORCH_INCLUDE_DIRS})
-    ADD_EXECUTABLE(resonet_test resonet_test.cpp)
-    TARGET_LINK_LIBRARIES(resonet_test JFJochResonet JFJochWriter)
-ENDIF()

resonet/LICENSE.resonet

@@ -1,28 +0,0 @@
-BSD 3-Clause License
-
-Copyright (c) 2023, Macromolecular Crystallography at SSRL
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted provided that the following conditions are met:
-
-1. Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
-
-2. Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
-
-3. Neither the name of the copyright holder nor the names of its
-   contributors may be used to endorse or promote products derived from
-   this software without specific prior written permission.
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
-FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
-DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
-SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
-CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
-OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

resonet/NeuralNetResPredictor.cpp

@@ -1,101 +0,0 @@
-// Copyright (2019-2024) Paul Scherrer Institute
-
-#include "NeuralNetResPredictor.h"
-
-#include <cmath>
-#include "../common/JFJochException.h"
-
-NeuralNetResPredictor::NeuralNetResPredictor(const std::string& model_path)
-        : model_input(512*512),
-          enable(!model_path.empty())
-#ifdef JFJOCH_USE_TORCH
-        , device(torch::kCUDA)
-#endif
-{
-#ifdef JFJOCH_USE_TORCH
-    if (enable) {
-        module = torch::jit::load(model_path);
-        module.to(device);
-    }
-#else
-    enable = false;
-#endif
-}
-
-template<class T>
-void NeuralNetResPredictor::PrepareInternal(const DiffractionExperiment& experiment, const T* image) {
-    size_t pool_factor = GetMaxPoolFactor(experiment);
-    size_t xpixel = experiment.GetXPixelsNum();
-    size_t ypixel = experiment.GetYPixelsNum();
-    size_t start_x = std::lround(experiment.GetBeamX_pxl());
-    size_t start_y = std::lround(experiment.GetBeamY_pxl());
-
-    for (size_t y = 0; y < 512; y++) {
-        size_t y0 = y * pool_factor + start_y;
-        size_t min_yp = std::min(y0 + pool_factor, ypixel);
-        for (size_t x = 0; x < 512; x++) {
-            float val = 0.0;
-            size_t x0 = x * pool_factor + start_x;
-            size_t min_xp = std::min(x0 + pool_factor, ypixel);
-
-            for (size_t yp = y0; yp < min_yp; yp++) {
-                for (size_t xp = x0; xp < min_xp; xp++) {
-                    int16_t pxl = image[yp * xpixel + xp];
-                    if (val < pxl)
-                        val = pxl;
-                }
-            }
-            float max_pool = floorf(sqrtf(val));
-            model_input[512 * y + x] = max_pool;
-        }
-    }
-}
-
-void NeuralNetResPredictor::Prepare(const DiffractionExperiment& experiment, const int16_t *image) {
-    PrepareInternal(experiment, image);
-}
-
-void NeuralNetResPredictor::Prepare(const DiffractionExperiment& experiment, const int32_t *image) {
-    PrepareInternal(experiment, image);
-}
-
-size_t NeuralNetResPredictor::GetMaxPoolFactor(const DiffractionExperiment& experiment) const {
-    float max_direction = std::max(experiment.GetXPixelsNum(), experiment.GetYPixelsNum()) / 2.0;
-    size_t pool_factor = std::lround(max_direction / 512.0f);
-    if (pool_factor <= 0)
-        throw JFJochException(JFJochExceptionCategory::InputParameterInvalid, "Detector size is too small");
-
-    if (pool_factor > 8)
-        throw JFJochException(JFJochExceptionCategory::InputParameterInvalid, "Detector size is too large");
-    return pool_factor;
-}
-
-std::optional<float> NeuralNetResPredictor::Inference(const DiffractionExperiment& experiment, const void *image) {
-    if (!enable)
-        return {};
-#ifdef JFJOCH_USE_TORCH
-    if (experiment.GetPixelDepth() == 2)
-        Prepare(experiment, (int16_t *) image);
-    else
-        Prepare(experiment, (int32_t *) image);
-
-    auto options = torch::TensorOptions().dtype(at::kFloat);
-    auto model_input_tensor = torch::from_blob(model_input.data(), {1,1,512,512}, options).to(device);
-
-    std::vector<torch::jit::IValue> pixels;
-    pixels.emplace_back(std::move(model_input_tensor));
-
-    auto output = module.forward(pixels).toTensor();
-    auto tensor_output = output[0].item<float>();
-    float two_theta = atanf(((2.0f * experiment.GetPixelSize_mm() / experiment.GetDetectorDistance_mm()) * tensor_output));
-    float stheta = sinf(two_theta * 0.5f);
-    float resolution = experiment.GetWavelength_A() / (2.0f * stheta);
-    return resolution;
-#else
-    return {};
-#endif
-}
-
-const std::vector<float> &NeuralNetResPredictor::GetModelInput() const {
-    return model_input;
-}

resonet/NeuralNetResPredictor.h

@@ -1,36 +0,0 @@
-// Copyright (2019-2024) Paul Scherrer Institute
-
-#ifndef JUNGFRAUJOCH_NEURALNETRESPREDICTOR_H
-#define JUNGFRAUJOCH_NEURALNETRESPREDICTOR_H
-
-#ifdef JFJOCH_USE_TORCH
-#include <torch/script.h>
-#endif
-
-#include "../common/DiffractionExperiment.h"
-
-// Based on model described in:
-// Mendez, D., Holton, J. M., Lyubimov, A. Y., Hollatz, S., Mathews, I. I., Cichosz, A., Martirosyan, V.,
-// Zeng, T., Stofer, R., Liu, R., Song, J., McPhillips, S., Soltis, M. & Cohen, A. E. (2024).
-// Acta Cryst. D80, 26-43.
-
-class NeuralNetResPredictor {
-    std::vector<float> model_input;
-    bool enable;
-#ifdef JFJOCH_USE_TORCH
-    torch::Device device;
-    torch::jit::script::Module module;
-#endif
-    template<class T>
-    void PrepareInternal(const DiffractionExperiment& experiment, const T* image);
-public:
-    explicit NeuralNetResPredictor(const std::string& model_path);
-    void Prepare(const DiffractionExperiment& experiment, const int16_t* image);
-    void Prepare(const DiffractionExperiment& experiment, const int32_t* image);
-    std::optional<float> Inference(const DiffractionExperiment& experiment, const void* image);
-    size_t GetMaxPoolFactor(const DiffractionExperiment& experiment) const;
-    const std::vector<float> &GetModelInput() const;
-};
-
-
-#endif //JUNGFRAUJOCH_NEURALNETRESPREDICTOR_H

resonet/resonet_test.cpp

@@ -1,51 +0,0 @@
-// Copyright (2019-2024) Paul Scherrer Institute
-
-#include <iostream>
-
-#include "NeuralNetResPredictor.h"
-#include "../writer/HDF5Objects.h"
-
-int main(int argc, char **argv) {
-    if (argc != 2) {
-        std::cerr << "Usage ./resonet_test <.pt file with traced model>" << std::endl;
-        exit(EXIT_FAILURE);
-    }
-
-    RegisterHDF5Filter();
-
-    DiffractionExperiment experiment(DetectorGeometry(8, 2, 8, 36));
-    experiment.DetectorDistance_mm(75).PhotonEnergy_keV(12.4).BeamY_pxl(1136).BeamX_pxl(1090);
-
-    NeuralNetResPredictor predictor(argv[1]);
-
-    HDF5ReadOnlyFile data("../../tests/test_data/compression_benchmark.h5");
-    HDF5DataSet dataset(data, "/entry/data/data");
-    HDF5DataSpace file_space(dataset);
-
-    std::vector<int16_t> image_conv (file_space.GetDimensions()[1] * file_space.GetDimensions()[2]);
-
-    std::vector<hsize_t> start = {4,0,0};
-    std::vector<hsize_t> file_size = {1, file_space.GetDimensions()[1], file_space.GetDimensions()[2]};
-    dataset.ReadVector(image_conv, start, file_size);
-
-    std::cout << "Max pooling " << predictor.GetMaxPoolFactor(experiment) << std::endl;
-
-    float x = 0;
-    size_t niterations = 25;
-    std::optional<float> result;
-
-    auto start_time = std::chrono::system_clock::now();
-    for (int i = 0; i < niterations; i++) {
-        result =   predictor.Inference(experiment, image_conv.data());
-        if (result)
-            x += result.value();
-    }
-    auto end_time = std::chrono::system_clock::now();
-    auto elapsed = std::chrono::duration_cast<std::chrono::microseconds>(end_time - start_time);
-    int64_t frequency_Hz = (niterations * 1e6) / (double) (elapsed.count());
-
-    std::cout << ((double) elapsed.count()) / (1e3 * niterations) << " ms" << std::endl;
-    std::cout << frequency_Hz << " Hz" << std::endl;
-    if (result)
-        std::cout << "Resolution " << result.value() << std::endl;
-}

tests/CMakeLists.txt

@@ -1,4 +1,4 @@
-ADD_EXECUTABLE(CatchTest
+ADD_EXECUTABLE(jfjoch_test
         DiffractionExperimentTest.cpp
         RawToConvertedGeometryTest.cpp
         ../common/RawToConvertedGeometry.h
@@ -30,7 +30,6 @@ ADD_EXECUTABLE(CatchTest
         TIFFTest.cpp
         JFJochReceiverProcessingTest.cpp
         JPEGTest.cpp
-        NeuralNetResPredictorTest.cpp
         HistogramTest.cpp
         FPGAEigerReorderTest.cpp
         ROIMapTest.cpp
@@ -40,7 +39,5 @@ ADD_EXECUTABLE(CatchTest
         RegressionTest.cpp
 )
 
-target_link_libraries(CatchTest Catch2WithMain JFJochBroker JFJochReceiver JFJochWriter JFJochImageAnalysis JFJochCommon JFJochHLSSimulation JFJochPreview JFJochResonet)
-target_include_directories(CatchTest PRIVATE .)
-
-INSTALL(TARGETS CatchTest RUNTIME)
+target_link_libraries(jfjoch_test Catch2WithMain JFJochBroker JFJochReceiver JFJochWriter JFJochImageAnalysis JFJochCommon JFJochHLSSimulation JFJochPreview)
+target_include_directories(jfjoch_test PRIVATE .)

tests/JFJochReceiverProcessingTest.cpp

@@ -188,73 +188,6 @@ TEST_CASE("GenerateResolutionMap") {
                     experiment.GetYPixelsNum(), 4);
 }
 
-TEST_CASE("JFJochIntegrationTest_ZMQ_lysozyme_resolution", "[JFJochReceiver]") {
-    Logger logger("JFJochIntegrationTest_ZMQ_lysozyme_resolution");
-
-    RegisterHDF5Filter();
-
-    const uint16_t nthreads = 4;
-
-    DiffractionExperiment experiment(DetectorGeometry(8,2,8,36));
-    experiment.ImagesPerTrigger(5).NumTriggers(1).UseInternalPacketGenerator(true)
-            .FilePrefix("lyso_test_resolution").ConversionOnFPGA(false)
-            .DetectorDistance_mm(75).BeamY_pxl(1136).BeamX_pxl(1090).PhotonEnergy_keV(12.4)
-            .SetUnitCell(UnitCell{.a = 36.9, .b = 78.95, .c = 78.95, .alpha =90, .beta = 90, .gamma = 90})
-            .NeuralNetModelPath("../../resonet/traced_resnet_model.pt");
-
-    // Load example image
-    HDF5ReadOnlyFile data("../../tests/test_data/compression_benchmark.h5");
-    HDF5DataSet dataset(data, "/entry/data/data");
-    HDF5DataSpace file_space(dataset);
-
-    REQUIRE(file_space.GetDimensions()[2] == experiment.GetXPixelsNum());
-    REQUIRE(file_space.GetDimensions()[1] == experiment.GetYPixelsNum());
-    std::vector<int16_t> image_conv (file_space.GetDimensions()[1] * file_space.GetDimensions()[2]);
-
-    std::vector<hsize_t> start = {4,0,0};
-    std::vector<hsize_t> file_size = {1, file_space.GetDimensions()[1], file_space.GetDimensions()[2]};
-    dataset.ReadVector(image_conv, start, file_size);
-
-    std::vector<int16_t> image_raw_geom(experiment.GetModulesNum() * RAW_MODULE_SIZE);
-    ConvertedToRawGeometry(experiment, image_raw_geom.data(), image_conv.data());
-    logger.Info("Loaded image");
-
-    // Setup acquisition device
-    AcquisitionDeviceGroup aq_devices;
-    std::unique_ptr<HLSSimulatedDevice> test = std::make_unique<HLSSimulatedDevice>(0, 64);
-    for (int m = 0; m < experiment.GetModulesNum(); m++)
-        test->SetInternalGeneratorFrame((uint16_t *) image_raw_geom.data() + m * RAW_MODULE_SIZE, m);
-    aq_devices.Add(std::move(test));
-
-    ZMQContext context;
-    ZMQStream2Pusher pusher(context, "ipc://*");
-    StreamWriter writer(context, logger, pusher.GetAddress());
-    auto writer_future = std::async(std::launch::async, &StreamWriter::Run, &writer);
-
-    context.NumThreads(4);
-
-    JFJochReceiverService service(aq_devices, logger, pusher);
-    service.NumThreads(nthreads);
-
-    SpotFindingSettings settings = DiffractionExperiment::DefaultDataProcessingSettings();
-    settings.signal_to_noise_threshold = 2.5;
-    settings.photon_count_threshold = 5;
-    settings.min_pix_per_spot = 1;
-    settings.max_pix_per_spot = 200;
-    service.SetSpotFindingSettings(settings);
-
-    service.Start(experiment, nullptr);
-    auto receiver_out = service.Stop();
-
-    CHECK(receiver_out.efficiency == 1.0);
-    CHECK(receiver_out.status.indexing_rate == 1.0);
-    CHECK(receiver_out.status.images_sent == experiment.GetImageNum());
-    CHECK(!receiver_out.status.cancelled);
-
-    REQUIRE_NOTHROW(writer_future.get());
-}
-
-
 TEST_CASE("JFJochIntegrationTest_ZMQ_ROI", "[JFJochReceiver]") {
     Logger logger("JFJochIntegrationTest_ZMQ_ROI");
 

tests/NeuralNetResPredictorTest.cpp

@@ -1,51 +0,0 @@
-// Copyright (2019-2024) Paul Scherrer Institute
-
-#include <catch2/catch_all.hpp>
-
-#include "../writer/HDF5Objects.h"
-#include "../resonet/NeuralNetResPredictor.h"
-
-TEST_CASE("NeuralNetResPredictor_Prepare", "[LinearAlgebra][Coord]") {
-    DiffractionExperiment experiment(DetectorGeometry(8, 2, 8, 36));
-    experiment.DetectorDistance_mm(75).PhotonEnergy_keV(12.4).BeamX_pxl(1000).BeamY_pxl(1000);
-
-    std::vector<int16_t> v(experiment.GetPixelsNum(),0);
-    v[1000 * experiment.GetXPixelsNum() + 1000] = 100;
-    v[1000 * experiment.GetXPixelsNum() + 1001] = 20;
-    v[1001 * experiment.GetXPixelsNum() + 1000] = 30;
-    v[1001 * experiment.GetXPixelsNum() + 1001] = INT16_MIN;
-
-    v[1050 * experiment.GetXPixelsNum() + 1050] = 52;
-    v[2000 * experiment.GetXPixelsNum() + 1500] = 160;
-    NeuralNetResPredictor predictor("../../resonet/traced_resnet_model.pt");
-
-    REQUIRE(predictor.GetMaxPoolFactor(experiment) == 2);
-
-    predictor.Prepare(experiment, v.data());
-    auto nn_input = predictor.GetModelInput();
-    REQUIRE(nn_input[0] == 10);
-    REQUIRE(nn_input[25 * 512 + 25] == 7);
-    REQUIRE(nn_input[500 * 512 + 250] == 12);
-}
-
-TEST_CASE("NeuralNetResPredictor_Inference", "[LinearAlgebra][Coord]") {
-    DiffractionExperiment experiment(DetectorGeometry(8, 2, 8, 36));
-    experiment.DetectorDistance_mm(75).PhotonEnergy_keV(12.4).BeamY_pxl(1136).BeamX_pxl(1090);
-
-    NeuralNetResPredictor predictor("../../resonet/traced_resnet_model.pt");
-
-    HDF5ReadOnlyFile data("../../tests/test_data/compression_benchmark.h5");
-    HDF5DataSet dataset(data, "/entry/data/data");
-    HDF5DataSpace file_space(dataset);
-
-    std::vector<int16_t> image_conv (file_space.GetDimensions()[1] * file_space.GetDimensions()[2]);
-
-    std::vector<hsize_t> start = {4,0,0};
-    std::vector<hsize_t> file_size = {1, file_space.GetDimensions()[1], file_space.GetDimensions()[2]};
-    dataset.ReadVector(image_conv, start, file_size);
-
-    auto res = predictor.Inference(experiment, image_conv.data());
-    REQUIRE(res);
-    REQUIRE(res.value() < 1.5);
-    REQUIRE(res.value() > 1.4);
-}

writer/CMakeLists.txt

@@ -21,8 +21,7 @@ ADD_LIBRARY(JFJochWriter STATIC
         HDF5DataFilePluginAzInt.h
         HDF5DataFilePluginJUNGFRAU.cpp
         HDF5DataFilePluginJUNGFRAU.h
-        HDF5DataFilePluginResEstimation.cpp
-        HDF5DataFilePluginResEstimation.h)
+)
 
 TARGET_LINK_LIBRARIES(JFJochWriter JFJochHDF5Wrappers JFJochCommon CBORStream2FrameSerialize)
 

writer/HDF5DataFile.cpp

@@ -8,7 +8,6 @@
 #include "HDF5DataFilePluginMX.h"
 #include "HDF5DataFilePluginXFEL.h"
 #include "HDF5DataFilePluginJUNGFRAU.h"
-#include "HDF5DataFilePluginResEstimation.h"
 #include "../include/spdlog/fmt/fmt.h"
 
 HDF5DataFile::HDF5DataFile(const std::string &in_filename,
@@ -27,7 +26,6 @@ HDF5DataFile::HDF5DataFile(const std::string &in_filename,
     plugins.emplace_back(std::make_unique<HDF5DataFilePluginJUNGFRAU>());
     plugins.emplace_back(std::make_unique<HDF5DataFilePluginAzInt>(in_rad_int_bin_to_q));
     plugins.emplace_back(std::make_unique<HDF5DataFilePluginXFEL>());
-    plugins.emplace_back(std::make_unique<HDF5DataFilePluginResEstimation>());
     plugins.emplace_back(std::make_unique<HDF5DataFilePluginMX>(in_max_spots));
 }
 

writer/HDF5DataFilePluginResEstimation.cpp

@@ -1,20 +0,0 @@
-// Copyright (2019-2024) Paul Scherrer Institute
-
-#include "HDF5DataFilePluginResEstimation.h"
-
-void HDF5DataFilePluginResEstimation::OpenFile(HDF5File &data_file, const DataMessage &msg) {}
-
-void HDF5DataFilePluginResEstimation::Write(const DataMessage &msg, uint64_t image_number) {
-    if (!msg.resolution_estimation)
-        return;
-
-    if (image_number >= res_estimation.size())
-        res_estimation.resize(image_number + 1);
-
-    res_estimation[image_number] = msg.resolution_estimation.value();
-}
-
-void HDF5DataFilePluginResEstimation::WriteFinal(HDF5File &data_file) {
-    if (!res_estimation.empty())
-        data_file.SaveVector("/entry/res_estimation", res_estimation);
-}

writer/HDF5DataFilePluginResEstimation.h

@@ -1,17 +0,0 @@
-// Copyright (2019-2024) Paul Scherrer Institute
-
-#ifndef JUNGFRAUJOCH_HDF5DATAFILEPLUGINRESESTIMATION_H
-#define JUNGFRAUJOCH_HDF5DATAFILEPLUGINRESESTIMATION_H
-
-#include "HDF5DataFilePlugin.h"
-
-class HDF5DataFilePluginResEstimation : public HDF5DataFilePlugin {
-    std::vector<float> res_estimation;
-public:
-    void OpenFile(HDF5File &data_file, const DataMessage &msg) override;
-    void Write(const DataMessage &msg, uint64_t image_number) override;
-    void WriteFinal(HDF5File &data_file) override;
-};
-
-
-#endif //JUNGFRAUJOCH_HDF5DATAFILEPLUGINRESESTIMATION_H