deploy docs on release only

- New binding code with macros to bind all cluster templates
- Simplified factory function on the python side
- 5x5, 7x7 and 9x9 bindings in python

.clang-tidy

@@ -0,0 +1,42 @@
+
+---
+Checks:          '*, 
+                    -altera-*,
+                    -android-cloexec-fopen,
+                    -cppcoreguidelines-pro-bounds-array-to-pointer-decay, 
+                    -cppcoreguidelines-pro-bounds-pointer-arithmetic, 
+                    -fuchsia*,
+                    -readability-else-after-return,
+                    -readability-avoid-const-params-in-decls,
+                    -readability-identifier-length,
+                    -cppcoreguidelines-pro-bounds-constant-array-index,
+                    -cppcoreguidelines-pro-type-reinterpret-cast,
+                    -llvm-header-guard,
+                    -modernize-use-nodiscard,
+                    -misc-non-private-member-variables-in-classes,
+                    -readability-static-accessed-through-instance,
+                    -readability-braces-around-statements,
+                    -readability-isolate-declaration,
+                    -readability-implicit-bool-conversion,
+                    -readability-identifier-length,
+                    -readability-identifier-naming,
+                    -hicpp-signed-bitwise,
+                    -hicpp-no-array-decay, 
+                    -hicpp-braces-around-statements,
+                    -google-runtime-references, 
+                    -google-readability-todo, 
+                    -google-readability-braces-around-statements,
+                    -modernize-use-trailing-return-type,
+                    -llvmlibc-*'
+
+HeaderFilterRegex: \.hpp
+FormatStyle:     none
+CheckOptions:
+  - { key: readability-identifier-naming.NamespaceCase,       value: lower_case }
+ # - { key: readability-identifier-naming.FunctionCase,        value: lower_case }
+  - { key: readability-identifier-naming.ClassCase,           value: CamelCase  }
+ # - { key: readability-identifier-naming.MethodCase,          value: CamelCase  }
+ # - { key: readability-identifier-naming.StructCase,          value: CamelCase  }
+ # - { key: readability-identifier-naming.VariableCase,        value: lower_case }
+  - { key: readability-identifier-naming.GlobalConstantCase,  value: UPPER_CASE }
+...

.gitea/workflows/cmake_build.yml

@@ -0,0 +1,58 @@
+name: Build the package using cmake then documentation
+
+on:
+  workflow_dispatch:
+
+    
+
+permissions:
+  contents: read
+  pages: write
+  id-token: write
+
+jobs:
+  build:
+    strategy:
+      fail-fast: false
+      matrix:
+        platform: [ubuntu-latest, ] 
+        python-version: ["3.12", ] 
+
+    runs-on: ${{ matrix.platform }}
+
+
+    defaults:
+      run:
+        shell: "bash -l {0}"
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Setup dev env
+        run: |
+            sudo apt-get update
+            sudo apt-get -y install cmake gcc g++
+
+      - name: Get conda
+        uses: conda-incubator/setup-miniconda@v3
+        with:
+          python-version: ${{ matrix.python-version }}
+          environment-file: etc/dev-env.yml
+          miniforge-version: latest
+          channels: conda-forge
+          conda-remove-defaults: "true"
+
+      - name: Build library 
+        run: |
+            mkdir build 
+            cd build 
+            cmake .. -DAARE_SYSTEM_LIBRARIES=ON -DAARE_DOCS=ON 
+            make -j 2 
+            make docs
+
+
+
+
+
+
+

.gitea/workflows/rh8-native.yml

@@ -0,0 +1,36 @@
+name: Build on RHEL8
+
+on:
+  push:
+  workflow_dispatch:
+
+permissions:
+  contents: read
+
+jobs:
+  build:
+    runs-on: "ubuntu-latest"
+    container:
+      image: gitea.psi.ch/images/rhel8-developer-gitea-actions
+    steps:
+      # workaround until actions/checkout@v4 is available for RH8
+      # - uses: actions/checkout@v4
+      - name: Clone repository
+        run: |
+          echo Cloning ${{ github.ref_name }}
+          git clone https://${{secrets.GITHUB_TOKEN}}@gitea.psi.ch/${{ github.repository }}.git --branch=${{ github.ref_name }} .
+
+
+      - name: Install dependencies
+        run: |
+          dnf install -y cmake python3.12 python3.12-devel python3.12-pip
+
+      - name: Build library 
+        run: |
+            mkdir build && cd build
+            cmake .. -DAARE_PYTHON_BINDINGS=ON -DAARE_TESTS=ON -DPython_FIND_VIRTUALENV=FIRST
+            make -j 2 
+
+      - name: C++ unit tests
+        working-directory: ${{gitea.workspace}}/build
+        run: ctest

.gitea/workflows/rh9-native.yml

@@ -0,0 +1,31 @@
+name: Build on RHEL9
+
+on:
+  push:
+  workflow_dispatch:
+
+permissions:
+  contents: read
+
+jobs:
+  build:
+    runs-on: "ubuntu-latest"
+    container:
+      image: gitea.psi.ch/images/rhel9-developer-gitea-actions
+    steps:
+      - uses: actions/checkout@v4
+
+
+      - name: Install dependencies
+        run: |
+          dnf install -y cmake python3.12 python3.12-devel python3.12-pip
+
+      - name: Build library 
+        run: |
+            mkdir build && cd build
+            cmake .. -DAARE_PYTHON_BINDINGS=ON -DAARE_TESTS=ON
+            make -j 2 
+
+      - name: C++ unit tests
+        working-directory: ${{gitea.workspace}}/build
+        run: ctest

.github/workflows/build_and_deploy_conda.yml

@@ -1,10 +1,9 @@
 name: Build pkgs and deploy if on main
 
 on:
-  push:
-    branches:
-     - main
-     - developer
+  release:
+      types:
+        - published
 
 jobs:
   build:
@@ -25,16 +24,15 @@ jobs:
       - uses: actions/checkout@v4
 
       - name: Get conda
-        uses: conda-incubator/setup-miniconda@v3.0.4
+        uses: conda-incubator/setup-miniconda@v3
         with:
           python-version: ${{ matrix.python-version }}
+          environment-file: etc/dev-env.yml
+          miniforge-version: latest
           channels: conda-forge
-
-      - name: Prepare
-        run: conda install conda-build=24.9 conda-verify pytest anaconda-client
+          conda-remove-defaults: "true"
 
       - name: Enable upload
-        if: github.ref == 'refs/heads/main'
         run: conda config --set anaconda_upload yes
 
       - name: Build

.github/workflows/build_conda.yml

@@ -0,0 +1,41 @@
+name: Build pkgs and deploy if on main
+
+on:
+  push:
+    branches:
+     - developer
+
+jobs:
+  build:
+    strategy:
+      fail-fast: false
+      matrix:
+        platform: [ubuntu-latest, ] # macos-12, windows-2019]
+        python-version: ["3.12",]
+
+    runs-on: ${{ matrix.platform }}
+
+    # The setup-miniconda action needs this to activate miniconda
+    defaults:
+      run:
+        shell: "bash -l {0}"
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Get conda
+        uses: conda-incubator/setup-miniconda@v3
+        with:
+          python-version: ${{ matrix.python-version }}
+          environment-file: etc/dev-env.yml
+          miniforge-version: latest
+          channels: conda-forge
+          conda-remove-defaults: "true"
+
+  
+      - name: Disable upload
+        run: conda config --set anaconda_upload no
+
+      - name: Build
+        run: conda build conda-recipe 
+

.github/workflows/build_docs.yml

@@ -5,7 +5,6 @@ on:
   push:
     
 
-
 permissions:
   contents: read
   pages: write
@@ -16,12 +15,11 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        platform: [ubuntu-latest, ] # macos-12, windows-2019]
+        platform: [ubuntu-latest, ] 
         python-version: ["3.12",]
 
     runs-on: ${{ matrix.platform }}
 
-    # The setup-miniconda action needs this to activate miniconda
     defaults:
       run:
         shell: "bash -l {0}"
@@ -30,13 +28,13 @@ jobs:
       - uses: actions/checkout@v4
 
       - name: Get conda
-        uses: conda-incubator/setup-miniconda@v3.0.4
+        uses: conda-incubator/setup-miniconda@v3
         with:
           python-version: ${{ matrix.python-version }}
+          environment-file: etc/dev-env.yml
+          miniforge-version: latest
           channels: conda-forge
-
-      - name: Prepare
-        run: conda install doxygen sphinx=7.1.2 breathe pybind11 sphinx_rtd_theme furo nlohmann_json zeromq fmt numpy
+          conda-remove-defaults: "true"
 
       - name: Build library 
         run: |
@@ -57,7 +55,7 @@ jobs:
       url: ${{ steps.deployment.outputs.page_url }}
     runs-on: ubuntu-latest
     needs: build
-    if: github.ref == 'refs/heads/main'
+    if: github.event_name == 'release' && github.event.action == 'published'
     steps:
       - name: Deploy to GitHub Pages
         id: deployment

.github/workflows/build_wheel.yml

@@ -0,0 +1,64 @@
+name: Build wheel
+
+on:
+    workflow_dispatch:
+    pull_request:
+    push:
+      branches:
+        - main
+    release:
+      types:
+        - published
+  
+
+jobs:
+  build_wheels:
+    name: Build wheels on ${{ matrix.os }}
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ubuntu-latest,]
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Build wheels
+        run: pipx run cibuildwheel==2.23.0
+
+      - uses: actions/upload-artifact@v4
+        with:
+          name: cibw-wheels-${{ matrix.os }}-${{ strategy.job-index }}
+          path: ./wheelhouse/*.whl
+    
+  build_sdist:
+    name: Build source distribution
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v4
+
+    - name: Build sdist
+      run: pipx run build --sdist
+
+    - uses: actions/upload-artifact@v4
+      with:
+        name: cibw-sdist
+        path: dist/*.tar.gz
+
+  upload_pypi:
+    needs: [build_wheels, build_sdist]
+    runs-on: ubuntu-latest
+    environment: pypi
+    permissions:
+        id-token: write
+    if: github.event_name == 'release' && github.event.action == 'published'
+    # or, alternatively, upload to PyPI on every tag starting with 'v' (remove on: release above to use this)
+    # if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags/v')
+    steps:
+        - uses: actions/download-artifact@v4
+          with:
+            # unpacks all CIBW artifacts into dist/
+            pattern: cibw-*
+            path: dist
+            merge-multiple: true
+
+        - uses: pypa/gh-action-pypi-publish@release/v1

.gitignore

@@ -17,7 +17,8 @@ Testing/
 ctbDict.cpp
 ctbDict.h
 
-
+wheelhouse/
+dist/
 
 *.pyc
 */__pycache__/*

CMakeLists.txt

@@ -1,16 +1,29 @@
-cmake_minimum_required(VERSION 3.14)
+cmake_minimum_required(VERSION 3.15)
 
 project(aare 
-    VERSION 1.0.0
     DESCRIPTION "Data processing library for PSI detectors"
     HOMEPAGE_URL "https://github.com/slsdetectorgroup/aare"
     LANGUAGES C CXX 
 )
 
+# Read VERSION file into project version
+set(VERSION_FILE "${CMAKE_CURRENT_SOURCE_DIR}/VERSION")
+file(READ "${VERSION_FILE}" VERSION_CONTENT)
+string(STRIP "${VERSION_CONTENT}" PROJECT_VERSION_STRING)
+set(PROJECT_VERSION ${PROJECT_VERSION_STRING})
+
 set(CMAKE_CXX_STANDARD 17) 
 set(CMAKE_CXX_STANDARD_REQUIRED ON)
 set(CMAKE_CXX_EXTENSIONS OFF)
 
+execute_process(
+        COMMAND git log -1 --format=%h
+        WORKING_DIRECTORY ${CMAKE_CURRENT_LIST_DIR}
+        OUTPUT_VARIABLE GIT_HASH
+        OUTPUT_STRIP_TRAILING_WHITESPACE
+        )
+message(STATUS "Building from git hash: ${GIT_HASH}")
+
 if (${CMAKE_VERSION} VERSION_GREATER "3.24")
     cmake_policy(SET CMP0135 NEW) #Fetch content download timestamp
 endif()
@@ -31,7 +44,7 @@ set(CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake" ${CMAKE_MODULE_PATH})
 
 
 # General options
-option(AARE_PYTHON_BINDINGS "Build python bindings" ON)
+option(AARE_PYTHON_BINDINGS "Build python bindings" OFF)
 option(AARE_TESTS "Build tests" OFF)
 option(AARE_BENCHMARKS "Build benchmarks" OFF)
 option(AARE_EXAMPLES "Build examples" OFF)
@@ -48,6 +61,7 @@ option(AARE_FETCH_PYBIND11 "Use FetchContent to download pybind11" ON)
 option(AARE_FETCH_CATCH "Use FetchContent to download catch2" ON)
 option(AARE_FETCH_JSON "Use FetchContent to download nlohmann::json" ON)
 option(AARE_FETCH_ZMQ "Use FetchContent to download libzmq" ON)
+option(AARE_FETCH_LMFIT "Use FetchContent to download lmfit" ON)
 
 
 #Convenience option to use system libraries only (no FetchContent)
@@ -59,10 +73,15 @@ if(AARE_SYSTEM_LIBRARIES)
     set(AARE_FETCH_CATCH OFF CACHE BOOL "Disabled FetchContent for catch2" FORCE)
     set(AARE_FETCH_JSON OFF CACHE BOOL "Disabled FetchContent for nlohmann::json" FORCE)
     set(AARE_FETCH_ZMQ OFF CACHE BOOL "Disabled FetchContent for libzmq" FORCE)
+    # Still fetch lmfit when setting AARE_SYSTEM_LIBRARIES since this is not available
+    # on conda-forge
 endif()
 
 if(AARE_VERBOSE)
     add_compile_definitions(AARE_VERBOSE)
+    add_compile_definitions(AARE_LOG_LEVEL=aare::logDEBUG5)
+else()
+    add_compile_definitions(AARE_LOG_LEVEL=aare::logERROR)
 endif()
 
 if(AARE_CUSTOM_ASSERT)
@@ -74,18 +93,70 @@ if(AARE_BENCHMARKS)
 endif()
 
 
+
 set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
 
+if(AARE_FETCH_LMFIT)
+    #TODO! Should we fetch lmfit from the web or inlcude a tar.gz in the repo?
+    set(LMFIT_PATCH_COMMAND git apply ${CMAKE_CURRENT_SOURCE_DIR}/patches/lmfit.patch)
+
+    # For cmake < 3.28 we can't supply EXCLUDE_FROM_ALL to FetchContent_Declare
+    # so we need this workaround
+    if (${CMAKE_VERSION} VERSION_LESS "3.28")
+        FetchContent_Declare(
+            lmfit
+            GIT_REPOSITORY https://jugit.fz-juelich.de/mlz/lmfit.git
+            GIT_TAG        main
+            PATCH_COMMAND ${LMFIT_PATCH_COMMAND}
+            UPDATE_DISCONNECTED 1
+        )
+    else()
+        FetchContent_Declare(
+            lmfit
+            GIT_REPOSITORY https://jugit.fz-juelich.de/mlz/lmfit.git
+            GIT_TAG        main
+            PATCH_COMMAND ${LMFIT_PATCH_COMMAND}
+            UPDATE_DISCONNECTED 1
+            EXCLUDE_FROM_ALL 1
+        )
+    endif()
+    
+
+    #Disable what we don't need from lmfit
+    set(BUILD_TESTING OFF CACHE BOOL "")
+    set(LMFIT_CPPTEST OFF CACHE BOOL "")
+    set(LIB_MAN OFF CACHE BOOL "")
+    set(LMFIT_CPPTEST OFF CACHE BOOL "")
+    set(BUILD_SHARED_LIBS OFF CACHE BOOL "")
+
+    if (${CMAKE_VERSION} VERSION_LESS "3.28")
+        if(NOT lmfit_POPULATED)
+            FetchContent_Populate(lmfit)
+            add_subdirectory(${lmfit_SOURCE_DIR} ${lmfit_BINARY_DIR} EXCLUDE_FROM_ALL)
+        endif()
+    else()
+        FetchContent_MakeAvailable(lmfit)
+    endif()
+    
+    set_property(TARGET lmfit PROPERTY POSITION_INDEPENDENT_CODE ON)
+else()
+    find_package(lmfit REQUIRED)
+endif()
+
+
 if(AARE_FETCH_ZMQ)
     # Fetchcontent_Declare is deprecated need to find a way to update this
     # for now setting the policy to old is enough
     if (${CMAKE_VERSION} VERSION_GREATER_EQUAL "3.30")
         cmake_policy(SET CMP0169 OLD)
     endif()
+    set(ZMQ_PATCH_COMMAND git apply ${CMAKE_CURRENT_SOURCE_DIR}/patches/libzmq_cmake_version.patch)
     FetchContent_Declare(
         libzmq
         GIT_REPOSITORY https://github.com/zeromq/libzmq.git
         GIT_TAG        v4.3.4
+        PATCH_COMMAND ${ZMQ_PATCH_COMMAND}
+        UPDATE_DISCONNECTED 1
     )
     # Disable unwanted options from libzmq
     set(BUILD_TESTS OFF CACHE BOOL "Switch off libzmq test build")
@@ -127,8 +198,8 @@ if (AARE_FETCH_FMT)
         LIBRARY DESTINATION ${CMAKE_INSTALL_LIBDIR}
         ARCHIVE DESTINATION ${CMAKE_INSTALL_LIBDIR}
         RUNTIME DESTINATION ${CMAKE_INSTALL_BINDIR}
-        INCLUDES DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}
-)
+        INCLUDES DESTINATION ${CMAKE_INSTALL_INCLUDEDIR}   
+    )  
 else()
       find_package(fmt 6 REQUIRED)
 endif()
@@ -146,7 +217,6 @@ if (AARE_FETCH_JSON)
     install(
         TARGETS nlohmann_json
         EXPORT "${TARGETS_EXPORT_NAME}"
-        
     )
     message(STATUS "target: ${NLOHMANN_JSON_TARGET_NAME}")
 else()
@@ -279,15 +349,23 @@ endif()
 
 set(PUBLICHEADERS
     include/aare/ArrayExpr.hpp
+    include/aare/CalculateEta.hpp
+    include/aare/Cluster.hpp
     include/aare/ClusterFinder.hpp
     include/aare/ClusterFile.hpp
     include/aare/CtbRawFile.hpp
     include/aare/ClusterVector.hpp
+    include/aare/decode.hpp
     include/aare/defs.hpp
     include/aare/Dtype.hpp
     include/aare/File.hpp
+    include/aare/Fit.hpp
     include/aare/FileInterface.hpp
+    include/aare/FilePtr.hpp
     include/aare/Frame.hpp
+    include/aare/GainMap.hpp
+    include/aare/geo_helpers.hpp
+    include/aare/JungfrauDataFile.hpp
     include/aare/NDArray.hpp
     include/aare/NDView.hpp
     include/aare/NumpyFile.hpp
@@ -298,33 +376,40 @@ set(PUBLICHEADERS
     include/aare/RawMasterFile.hpp
     include/aare/RawSubFile.hpp
     include/aare/VarClusterFinder.hpp
-
+    include/aare/utils/task.hpp
 )
 
 
 set(SourceFiles
     ${CMAKE_CURRENT_SOURCE_DIR}/src/CtbRawFile.cpp
-    ${CMAKE_CURRENT_SOURCE_DIR}/src/ClusterFile.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/src/defs.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/src/Dtype.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/src/decode.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/src/Frame.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/src/File.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/src/FilePtr.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/src/Fit.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/src/geo_helpers.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/src/JungfrauDataFile.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/src/NumpyFile.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/src/NumpyHelpers.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/src/Interpolator.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/src/PixelMap.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/src/RawFile.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/src/RawSubFile.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/src/RawMasterFile.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/src/utils/task.cpp
+    ${CMAKE_CURRENT_SOURCE_DIR}/src/utils/ifstream_helpers.cpp
 )
 
-
 add_library(aare_core STATIC ${SourceFiles})
 target_include_directories(aare_core PUBLIC 
     "$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>" 
-    "$<INSTALL_INTERFACE:${CMAKE_INSTALL_INCLUDEDIR}>"
+    "$<INSTALL_INTERFACE:${CMAKE_INSTALL_INCLUDEDIR}>" 
 )
 
-
+set(THREADS_PREFER_PTHREAD_FLAG ON)
+find_package(Threads REQUIRED)
 
 target_link_libraries(
     aare_core 
@@ -334,6 +419,9 @@ target_link_libraries(
     ${STD_FS_LIB} # from helpers.cmake
     PRIVATE 
     aare_compiler_flags 
+    Threads::Threads
+    $<BUILD_INTERFACE:lmfit>
+
 )
 
 set_target_properties(aare_core PROPERTIES
@@ -347,18 +435,28 @@ endif()
 
 if(AARE_TESTS)
     set(TestSources
+            ${CMAKE_CURRENT_SOURCE_DIR}/src/algorithm.test.cpp
             ${CMAKE_CURRENT_SOURCE_DIR}/src/defs.test.cpp
+            ${CMAKE_CURRENT_SOURCE_DIR}/src/decode.test.cpp
             ${CMAKE_CURRENT_SOURCE_DIR}/src/Dtype.test.cpp
             ${CMAKE_CURRENT_SOURCE_DIR}/src/Frame.test.cpp
+            ${CMAKE_CURRENT_SOURCE_DIR}/src/geo_helpers.test.cpp
             ${CMAKE_CURRENT_SOURCE_DIR}/src/RawMasterFile.test.cpp
             ${CMAKE_CURRENT_SOURCE_DIR}/src/NDArray.test.cpp
             ${CMAKE_CURRENT_SOURCE_DIR}/src/NDView.test.cpp
             ${CMAKE_CURRENT_SOURCE_DIR}/src/ClusterFinder.test.cpp
             ${CMAKE_CURRENT_SOURCE_DIR}/src/ClusterVector.test.cpp
+            ${CMAKE_CURRENT_SOURCE_DIR}/src/Cluster.test.cpp
+            ${CMAKE_CURRENT_SOURCE_DIR}/src/CalculateEta.test.cpp
+            ${CMAKE_CURRENT_SOURCE_DIR}/src/ClusterFile.test.cpp
+            ${CMAKE_CURRENT_SOURCE_DIR}/src/ClusterFinderMT.test.cpp
             ${CMAKE_CURRENT_SOURCE_DIR}/src/Pedestal.test.cpp
+            ${CMAKE_CURRENT_SOURCE_DIR}/src/JungfrauDataFile.test.cpp
             ${CMAKE_CURRENT_SOURCE_DIR}/src/NumpyFile.test.cpp
             ${CMAKE_CURRENT_SOURCE_DIR}/src/NumpyHelpers.test.cpp
             ${CMAKE_CURRENT_SOURCE_DIR}/src/RawFile.test.cpp
+            ${CMAKE_CURRENT_SOURCE_DIR}/src/RawSubFile.test.cpp
+            ${CMAKE_CURRENT_SOURCE_DIR}/src/utils/task.test.cpp
 
         )
     target_sources(tests PRIVATE ${TestSources} )
@@ -436,4 +534,4 @@ if(AARE_MASTER_PROJECT)
     set(CMAKE_INSTALL_DIR "share/cmake/${PROJECT_NAME}")
     set(PROJECT_LIBRARIES aare-core aare-compiler-flags )
     include(cmake/package_config.cmake)
-endif()
+endif()

VERSION

@@ -0,0 +1 @@
+2025.5.22

benchmarks/CMakeLists.txt

@@ -1,11 +1,27 @@
-find_package(benchmark REQUIRED)
 
-add_executable(ndarray_benchmark ndarray_benchmark.cpp)
+include(FetchContent)
 
-target_link_libraries(ndarray_benchmark benchmark::benchmark aare_core aare_compiler_flags)
-# target_link_libraries(tests PRIVATE aare_core aare_compiler_flags)
 
-set_target_properties(ndarray_benchmark PROPERTIES
-        RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}
-        # OUTPUT_NAME run_tests
+FetchContent_Declare(
+    benchmark
+    GIT_REPOSITORY https://github.com/google/benchmark.git
+    GIT_TAG        v1.8.3 # Change to the latest version if needed
+)
+
+# Ensure Google Benchmark is built correctly
+set(BENCHMARK_ENABLE_TESTING OFF CACHE BOOL "" FORCE)
+
+FetchContent_MakeAvailable(benchmark)
+
+add_executable(benchmarks)
+
+target_sources(benchmarks PRIVATE ndarray_benchmark.cpp calculateeta_benchmark.cpp)
+
+# Link Google Benchmark and other necessary libraries
+target_link_libraries(benchmarks PRIVATE benchmark::benchmark aare_core aare_compiler_flags)
+
+# Set output properties
+set_target_properties(benchmarks PROPERTIES
+    RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}
+    OUTPUT_NAME run_benchmarks
 )

benchmarks/calculateeta_benchmark.cpp

@@ -0,0 +1,70 @@
+#include "aare/CalculateEta.hpp"
+#include "aare/ClusterFile.hpp"
+#include <benchmark/benchmark.h>
+
+using namespace aare;
+
+class ClusterFixture : public benchmark::Fixture {
+  public:
+    Cluster<int, 2, 2> cluster_2x2{};
+    Cluster<int, 3, 3> cluster_3x3{};
+
+  private:
+    using benchmark::Fixture::SetUp;
+
+    void SetUp([[maybe_unused]] const benchmark::State &state) override {
+        int temp_data[4] = {1, 2, 3, 1};
+        std::copy(std::begin(temp_data), std::end(temp_data),
+                  std::begin(cluster_2x2.data));
+
+        cluster_2x2.x = 0;
+        cluster_2x2.y = 0;
+
+        int temp_data2[9] = {1, 2, 3, 1, 3, 4, 5, 1, 20};
+        std::copy(std::begin(temp_data2), std::end(temp_data2),
+                  std::begin(cluster_3x3.data));
+
+        cluster_3x3.x = 0;
+        cluster_3x3.y = 0;
+    }
+
+    // void TearDown(::benchmark::State& state) {
+    // }
+};
+
+BENCHMARK_F(ClusterFixture, Calculate2x2Eta)(benchmark::State &st) {
+    for (auto _ : st) {
+        // This code gets timed
+        Eta2 eta = calculate_eta2(cluster_2x2);
+        benchmark::DoNotOptimize(eta);
+    }
+}
+
+// almost takes double the time
+BENCHMARK_F(ClusterFixture,
+            CalculateGeneralEtaFor2x2Cluster)(benchmark::State &st) {
+    for (auto _ : st) {
+        // This code gets timed
+        Eta2 eta = calculate_eta2<int, 2, 2>(cluster_2x2);
+        benchmark::DoNotOptimize(eta);
+    }
+}
+
+BENCHMARK_F(ClusterFixture, Calculate3x3Eta)(benchmark::State &st) {
+    for (auto _ : st) {
+        // This code gets timed
+        Eta2 eta = calculate_eta2(cluster_3x3);
+        benchmark::DoNotOptimize(eta);
+    }
+}
+
+// almost takes double the time
+BENCHMARK_F(ClusterFixture,
+            CalculateGeneralEtaFor3x3Cluster)(benchmark::State &st) {
+    for (auto _ : st) {
+        // This code gets timed
+        Eta2 eta = calculate_eta2<int, 3, 3>(cluster_3x3);
+        benchmark::DoNotOptimize(eta);
+    }
+}
+// BENCHMARK_MAIN();

conda-recipe/conda_build_config.yaml

@@ -1,28 +1,5 @@
 python:
     - 3.11
-    - 3.11
-    - 3.11
-    - 3.12
-    - 3.12
     - 3.12
     - 3.13
 
-
-    
-numpy: 
-    - 1.26
-    - 2.0
-    - 2.1
-    - 1.26
-    - 2.0
-    - 2.1
-    - 2.1
-
-
-zip_keys:
-    - python
-    - numpy
-
-pin_run_as_build:
-  numpy: x.x
-  python: x.x

conda-recipe/meta.yaml

@@ -1,7 +1,10 @@
+source:
+  path: ../
+
+{% set version = load_file_regex(load_file = 'VERSION', regex_pattern = '(\d+(?:\.\d+)*(?:[\+\w\.]+))').group(1) %}
 package:
   name: aare
-  version: 2024.12.16.dev0 #TODO! how to not duplicate this?
-
+  version: {{version}}
 
 source:
   path: ..
@@ -9,44 +12,39 @@ source:
 build:
   number: 0
   script:
-    - unset CMAKE_GENERATOR && {{ PYTHON }} -m pip install . -vv  # [not win]
-    - {{ PYTHON }} -m pip install . -vv  # [win]
+    - unset CMAKE_GENERATOR && {{ PYTHON }} -m pip install . -vv 
 
 requirements:
   build:
-    - python {{python}}
-    - numpy {{ numpy }}
     - {{ compiler('cxx') }}
-
-
-  host:
     - cmake
     - ninja
-    - python {{python}}
-    - numpy {{ numpy }}
+
+  host:
+    - python
     - pip
+    - numpy=2.1
     - scikit-build-core
     - pybind11 >=2.13.0
-    - fmt
-    - zeromq
-    - nlohmann_json
-    - catch2
+    - matplotlib # needed in host to solve the environment for run
 
   run:
-    - python {{python}}
-    - numpy {{ numpy }}
+    - python
+    - {{ pin_compatible('numpy') }}
+    - matplotlib
+    
 
 
 test:
   imports:
     - aare
-  # requires:
-  #   - pytest
-  # source_files:
-  #   - tests
-  # commands:
-  #   - pytest tests
+  requires:
+    - pytest
+    - boost-histogram 
+  source_files:
+    - python/tests
+  commands:
+    - python -m pytest python/tests
 
 about:
-  summary: An example project built with pybind11 and scikit-build.
-  # license_file: LICENSE
+  summary: Data analysis library for hybrid pixel detectors from PSI 

docs/CMakeLists.txt

@@ -12,28 +12,7 @@ set(SPHINX_BUILD ${CMAKE_CURRENT_BINARY_DIR})
 
 
 file(GLOB SPHINX_SOURCE_FILES CONFIGURE_DEPENDS "src/*.rst")
-# set(SPHINX_SOURCE_FILES
-#   src/index.rst
-#   src/Installation.rst
-#   src/Requirements.rst
-#   src/NDArray.rst
-#   src/NDView.rst
-#   src/File.rst
-#   src/Frame.rst
-#   src/Dtype.rst
-#   src/ClusterFinder.rst
-#   src/ClusterFile.rst
-#   src/Pedestal.rst
-#   src/RawFile.rst
-#   src/RawSubFile.rst
-#   src/RawMasterFile.rst
-#   src/VarClusterFinder.rst
-#   src/pyVarClusterFinder.rst
-#   src/pyFile.rst
-#   src/pyCtbRawFile.rst
-#   src/pyRawFile.rst
-#   src/pyRawMasterFile.rst
-# )
+
 
 
 foreach(filename ${SPHINX_SOURCE_FILES})

docs/src/ClusterFinderMT.rst

@@ -0,0 +1,7 @@
+ClusterFinderMT
+==================
+
+
+.. doxygenclass:: aare::ClusterFinderMT
+   :members:
+   :undoc-members:

docs/src/JungfrauDataFile.rst

@@ -0,0 +1,25 @@
+JungfrauDataFile
+==================
+
+JungfrauDataFile is a class to read the .dat files that are produced by Aldo's receiver. 
+It is mostly used for calibration. 
+
+The structure of the file is:
+
+* JungfrauDataHeader
+* Binary data (256x256, 256x1024 or 512x1024)
+* JungfrauDataHeader
+* ...
+
+There is no metadata indicating number of frames or the size of the image, but this
+will be infered by this reader. 
+
+.. doxygenstruct:: aare::JungfrauDataHeader
+   :members:
+   :undoc-members:
+   :private-members:
+
+.. doxygenclass:: aare::JungfrauDataFile
+   :members:
+   :undoc-members:
+   :private-members:

docs/src/Tests.rst

@@ -0,0 +1,47 @@
+****************
+Tests
+****************
+
+We test the code both from the C++ and Python API. By default only tests that does not require image data is run. 
+
+C++
+~~~~~~~~~~~~~~~~~~
+
+.. code-block:: bash
+
+    mkdir build
+    cd build
+    cmake .. -DAARE_TESTS=ON
+    make -j 4
+
+    export AARE_TEST_DATA=/path/to/test/data
+    ./run_test [.files] #or using ctest, [.files] is the option to include tests needing data
+
+
+
+Python
+~~~~~~~~~~~~~~~~~~
+
+.. code-block:: bash
+
+    #From the root dir of the library
+    python -m pytest python/tests --files # passing --files will run the tests needing data
+
+
+
+Getting the test data
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. attention ::
+
+    The tests needing the test data are not run by default. To make the data available, you need to set the environment variable
+    AARE_TEST_DATA to the path of the test data directory. Then pass either [.files] for the C++ tests or --files for Python
+
+The image files needed for the test are large and are not included in the repository. They are stored
+using GIT LFS in a separate repository. To get the test data, you need to clone the repository.
+To do this, you need to have GIT LFS installed. You can find instructions on how to install it here: https://git-lfs.github.com/
+Once you have GIT LFS installed, you can clone the repository like any normal repo using:
+
+.. code-block:: bash
+    
+    git clone https://gitea.psi.ch/detectors/aare-test-data.git

docs/src/algorithm.rst

@@ -0,0 +1,5 @@
+algorithm
+=============
+
+.. doxygenfile:: algorithm.hpp
+

docs/src/index.rst

@@ -20,9 +20,6 @@ AARE
     Requirements
     Consume
 
-
-
-
 .. toctree::
     :caption: Python API
     :maxdepth: 1
@@ -30,23 +27,30 @@ AARE
     pyFile
     pyCtbRawFile
     pyClusterFile
+    pyClusterVector
+    pyJungfrauDataFile
     pyRawFile
     pyRawMasterFile
     pyVarClusterFinder
 
+    pyFit
+
 
 .. toctree::
     :caption: C++ API
     :maxdepth: 1
     
+    algorithm
     NDArray
     NDView
     Frame
     File
     Dtype
     ClusterFinder
+    ClusterFinderMT
     ClusterFile
     ClusterVector
+    JungfrauDataFile
     Pedestal
     RawFile
     RawSubFile
@@ -55,4 +59,8 @@ AARE
 
 
 
-
+.. toctree::
+    :caption: Developer
+    :maxdepth: 3
+    
+    Tests

docs/src/pyClusterVector.rst

@@ -0,0 +1,33 @@
+ClusterVector
+================
+
+The ClusterVector, holds clusters from the ClusterFinder. Since it is templated
+in C++  we use a suffix indicating the data type in python. The suffix is
+``_i`` for integer, ``_f`` for float, and ``_d`` for double.
+
+At the moment the functionality from python is limited and it is not supported
+to push_back clusters to the vector. The intended use case is to pass it to 
+C++ functions that support the ClusterVector or to view it as a numpy array.
+
+**View ClusterVector as numpy array**
+
+.. code:: python
+
+    from aare import ClusterFile
+    with ClusterFile("path/to/file") as f:
+        cluster_vector = f.read_frame()
+
+    # Create a copy of the cluster data in a numpy array
+    clusters = np.array(cluster_vector)
+
+    # Avoid copying the data by passing copy=False
+    clusters = np.array(cluster_vector, copy = False)
+
+
+.. py:currentmodule:: aare
+
+.. autoclass:: ClusterVector_i
+    :members:
+    :undoc-members:
+    :show-inheritance:
+    :inherited-members:

docs/src/pyFit.rst

@@ -0,0 +1,19 @@
+
+Fit
+========
+
+.. py:currentmodule:: aare
+
+
+**Functions**
+
+.. autofunction:: gaus
+
+.. autofunction:: pol1
+
+
+**Fitting**
+
+.. autofunction:: fit_gaus
+
+.. autofunction:: fit_pol1

docs/src/pyJungfrauDataFile.rst

@@ -0,0 +1,10 @@
+JungfrauDataFile
+===================
+
+.. py:currentmodule:: aare
+
+.. autoclass:: JungfrauDataFile
+    :members:
+    :undoc-members:
+    :show-inheritance:
+    :inherited-members:

etc/dev-env.yml

@@ -0,0 +1,13 @@
+name: dev-environment
+channels:
+  - conda-forge
+dependencies:
+  - anaconda-client
+  - conda-build
+  - doxygen 
+  - sphinx=7.1.2 
+  - breathe 
+  - sphinx_rtd_theme 
+  - furo 
+  - zeromq 
+

include/aare/ArrayExpr.hpp

@@ -1,22 +1,24 @@
 #pragma once
-#include <cstdint> //int64_t
-#include <cstddef> //size_t
+#include <cstdint> 
+#include <cstddef> 
 #include <array> 
-
 #include <cassert>
+#include "aare/defs.hpp"
+
+
 namespace aare {
 
-template <typename E, int64_t Ndim> class ArrayExpr {
+template <typename E, ssize_t Ndim> class ArrayExpr {
   public:
     static constexpr bool is_leaf = false;
 
     auto operator[](size_t i) const { return static_cast<E const &>(*this)[i]; }
     auto operator()(size_t i) const { return static_cast<E const &>(*this)[i]; }
     auto size() const { return static_cast<E const &>(*this).size(); }
-    std::array<int64_t, Ndim> shape() const { return static_cast<E const &>(*this).shape(); }
+    std::array<ssize_t, Ndim> shape() const { return static_cast<E const &>(*this).shape(); }
 };
 
-template <typename A, typename B, int64_t Ndim>
+template <typename A, typename B, ssize_t Ndim>
 class ArrayAdd : public ArrayExpr<ArrayAdd<A, B, Ndim>, Ndim> {
     const A &arr1_;
     const B &arr2_;
@@ -27,10 +29,10 @@ class ArrayAdd : public ArrayExpr<ArrayAdd<A, B, Ndim>, Ndim> {
     }
     auto operator[](int i) const { return arr1_[i] + arr2_[i]; }
     size_t size() const { return arr1_.size(); }
-    std::array<int64_t, Ndim> shape() const { return arr1_.shape(); }
+    std::array<ssize_t, Ndim> shape() const { return arr1_.shape(); }
 };
 
-template <typename A, typename B, int64_t Ndim>
+template <typename A, typename B, ssize_t Ndim>
 class ArraySub : public ArrayExpr<ArraySub<A, B, Ndim>, Ndim> {
     const A &arr1_;
     const B &arr2_;
@@ -41,10 +43,10 @@ class ArraySub : public ArrayExpr<ArraySub<A, B, Ndim>, Ndim> {
     }
     auto operator[](int i) const { return arr1_[i] - arr2_[i]; }
     size_t size() const { return arr1_.size(); }
-    std::array<int64_t, Ndim> shape() const { return arr1_.shape(); }
+    std::array<ssize_t, Ndim> shape() const { return arr1_.shape(); }
 };
 
-template <typename A, typename B, int64_t Ndim>
+template <typename A, typename B, ssize_t Ndim>
 class ArrayMul : public ArrayExpr<ArrayMul<A, B, Ndim>,Ndim> {
     const A &arr1_;
     const B &arr2_;
@@ -55,10 +57,10 @@ class ArrayMul : public ArrayExpr<ArrayMul<A, B, Ndim>,Ndim> {
     }
     auto operator[](int i) const { return arr1_[i] * arr2_[i]; }
     size_t size() const { return arr1_.size(); }
-    std::array<int64_t, Ndim> shape() const { return arr1_.shape(); }
+    std::array<ssize_t, Ndim> shape() const { return arr1_.shape(); }
 };
 
-template <typename A, typename B, int64_t Ndim>
+template <typename A, typename B, ssize_t Ndim>
 class ArrayDiv : public ArrayExpr<ArrayDiv<A, B, Ndim>, Ndim> {
     const A &arr1_;
     const B &arr2_;
@@ -69,27 +71,27 @@ class ArrayDiv : public ArrayExpr<ArrayDiv<A, B, Ndim>, Ndim> {
     }
     auto operator[](int i) const { return arr1_[i] / arr2_[i]; }
     size_t size() const { return arr1_.size(); }
-    std::array<int64_t, Ndim> shape() const { return arr1_.shape(); }
+    std::array<ssize_t, Ndim> shape() const { return arr1_.shape(); }
 };
 
 
 
-template <typename A, typename B, int64_t Ndim>
+template <typename A, typename B, ssize_t Ndim>
 auto operator+(const ArrayExpr<A, Ndim> &arr1, const ArrayExpr<B, Ndim> &arr2) {
     return ArrayAdd<ArrayExpr<A, Ndim>, ArrayExpr<B, Ndim>, Ndim>(arr1, arr2);
 }
 
-template <typename A, typename B, int64_t Ndim>
+template <typename A, typename B, ssize_t Ndim>
 auto operator-(const ArrayExpr<A,Ndim> &arr1, const ArrayExpr<B, Ndim> &arr2) {
     return ArraySub<ArrayExpr<A, Ndim>, ArrayExpr<B, Ndim>, Ndim>(arr1, arr2);
 }
 
-template <typename A, typename B, int64_t Ndim>
+template <typename A, typename B, ssize_t Ndim>
 auto operator*(const ArrayExpr<A, Ndim> &arr1, const ArrayExpr<B, Ndim> &arr2) {
     return ArrayMul<ArrayExpr<A, Ndim>, ArrayExpr<B, Ndim>, Ndim>(arr1, arr2);
 }
 
-template <typename A, typename B, int64_t Ndim>
+template <typename A, typename B, ssize_t Ndim>
 auto operator/(const ArrayExpr<A, Ndim> &arr1, const ArrayExpr<B, Ndim> &arr2) {
     return ArrayDiv<ArrayExpr<A, Ndim>, ArrayExpr<B, Ndim>, Ndim>(arr1, arr2);
 }

include/aare/CalculateEta.hpp

@@ -0,0 +1,170 @@
+#pragma once
+
+#include "aare/Cluster.hpp"
+#include "aare/ClusterVector.hpp"
+#include "aare/NDArray.hpp"
+
+namespace aare {
+
+enum class corner : int {
+    cBottomLeft = 0,
+    cBottomRight = 1,
+    cTopLeft = 2,
+    cTopRight = 3
+};
+
+enum class pixel : int {
+    pBottomLeft = 0,
+    pBottom = 1,
+    pBottomRight = 2,
+    pLeft = 3,
+    pCenter = 4,
+    pRight = 5,
+    pTopLeft = 6,
+    pTop = 7,
+    pTopRight = 8
+};
+
+template <typename T> struct Eta2 {
+    double x;
+    double y;
+    int c;
+    T sum;
+};
+
+/**
+ * @brief Calculate the eta2 values for all clusters in a Clustervector
+ */
+template <typename ClusterType,
+          typename = std::enable_if_t<is_cluster_v<ClusterType>>>
+NDArray<double, 2> calculate_eta2(const ClusterVector<ClusterType> &clusters) {
+    NDArray<double, 2> eta2({static_cast<int64_t>(clusters.size()), 2});
+
+    for (size_t i = 0; i < clusters.size(); i++) {
+        auto e = calculate_eta2(clusters[i]);
+        eta2(i, 0) = e.x;
+        eta2(i, 1) = e.y;
+    }
+
+    return eta2;
+}
+
+/**
+ * @brief Calculate the eta2 values for a generic sized cluster and return them
+ * in a Eta2 struct containing etay, etax and the index of the respective 2x2
+ * subcluster.
+ */
+template <typename T, uint8_t ClusterSizeX, uint8_t ClusterSizeY,
+          typename CoordType>
+Eta2<T>
+calculate_eta2(const Cluster<T, ClusterSizeX, ClusterSizeY, CoordType> &cl) {
+    Eta2<T> eta{};
+
+    auto max_sum = cl.max_sum_2x2();
+    eta.sum = max_sum.first;
+    auto c = max_sum.second;
+
+    size_t cluster_center_index =
+        (ClusterSizeX / 2) + (ClusterSizeY / 2) * ClusterSizeX;
+
+    size_t index_bottom_left_max_2x2_subcluster =
+        (int(c / (ClusterSizeX - 1))) * ClusterSizeX + c % (ClusterSizeX - 1);
+
+    // check that cluster center is in max subcluster
+    if (cluster_center_index != index_bottom_left_max_2x2_subcluster &&
+        cluster_center_index != index_bottom_left_max_2x2_subcluster + 1 &&
+        cluster_center_index !=
+            index_bottom_left_max_2x2_subcluster + ClusterSizeX &&
+        cluster_center_index !=
+            index_bottom_left_max_2x2_subcluster + ClusterSizeX + 1)
+        throw std::runtime_error("Photon center is not in max 2x2_subcluster");
+
+    if ((cluster_center_index - index_bottom_left_max_2x2_subcluster) %
+            ClusterSizeX ==
+        0) {
+        if ((cl.data[cluster_center_index + 1] +
+             cl.data[cluster_center_index]) != 0)
+
+            eta.x = static_cast<double>(cl.data[cluster_center_index + 1]) /
+                    static_cast<double>((cl.data[cluster_center_index + 1] +
+                                         cl.data[cluster_center_index]));
+    } else {
+        if ((cl.data[cluster_center_index] +
+             cl.data[cluster_center_index - 1]) != 0)
+
+            eta.x = static_cast<double>(cl.data[cluster_center_index]) /
+                    static_cast<double>((cl.data[cluster_center_index - 1] +
+                                         cl.data[cluster_center_index]));
+    }
+    if ((cluster_center_index - index_bottom_left_max_2x2_subcluster) /
+            ClusterSizeX <
+        1) {
+        assert(cluster_center_index + ClusterSizeX <
+               ClusterSizeX * ClusterSizeY); // suppress warning
+        if ((cl.data[cluster_center_index] +
+             cl.data[cluster_center_index + ClusterSizeX]) != 0)
+            eta.y = static_cast<double>(
+                        cl.data[cluster_center_index + ClusterSizeX]) /
+                    static_cast<double>(
+                        (cl.data[cluster_center_index] +
+                         cl.data[cluster_center_index + ClusterSizeX]));
+    } else {
+        if ((cl.data[cluster_center_index] +
+             cl.data[cluster_center_index - ClusterSizeX]) != 0)
+            eta.y = static_cast<double>(cl.data[cluster_center_index]) /
+                    static_cast<double>(
+                        (cl.data[cluster_center_index] +
+                         cl.data[cluster_center_index - ClusterSizeX]));
+    }
+
+    eta.c = c; // TODO only supported for 2x2 and 3x3 clusters -> at least no
+               // underyling enum class
+    return eta;
+}
+
+// TODO! Look up eta2 calculation - photon center should be top right corner
+template <typename T>
+Eta2<T> calculate_eta2(const Cluster<T, 2, 2, int16_t> &cl) {
+    Eta2<T> eta{};
+
+    if ((cl.data[0] + cl.data[1]) != 0)
+        eta.x = static_cast<double>(cl.data[1]) / (cl.data[0] + cl.data[1]);
+    if ((cl.data[0] + cl.data[2]) != 0)
+        eta.y = static_cast<double>(cl.data[2]) / (cl.data[0] + cl.data[2]);
+    eta.sum = cl.sum();
+    eta.c = static_cast<int>(corner::cBottomLeft); // TODO! This is not correct,
+                                                   // but need to put something
+    return eta;
+}
+
+// calculates Eta3 for 3x3 cluster based on code from analyze_cluster
+// TODO only supported for 3x3 Clusters
+template <typename T> Eta2<T> calculate_eta3(const Cluster<T, 3, 3> &cl) {
+
+    Eta2<T> eta{};
+
+    T sum = 0;
+
+    std::for_each(std::begin(cl.data), std::end(cl.data),
+                  [&sum](T x) { sum += x; });
+
+    eta.sum = sum;
+
+    eta.c = corner::cBottomLeft;
+
+    if ((cl.data[3] + cl.data[4] + cl.data[5]) != 0)
+
+        eta.x = static_cast<double>(-cl.data[3] + cl.data[3 + 2]) /
+
+                (cl.data[3] + cl.data[4] + cl.data[5]);
+
+    if ((cl.data[1] + cl.data[4] + cl.data[7]) != 0)
+
+        eta.y = static_cast<double>(-cl.data[1] + cl.data[2 * 3 + 1]) /
+
+                (cl.data[1] + cl.data[4] + cl.data[7]);
+
+    return eta;
+}
+
+} // namespace aare

include/aare/CircularFifo.hpp

@@ -0,0 +1,97 @@
+#pragma once
+
+#include <chrono>
+#include <fmt/color.h>
+#include <fmt/format.h>
+#include <memory>
+#include <thread>
+
+#include "aare/ProducerConsumerQueue.hpp"
+
+namespace aare {
+
+template <class ItemType> class CircularFifo {
+    uint32_t fifo_size;
+    aare::ProducerConsumerQueue<ItemType> free_slots;
+    aare::ProducerConsumerQueue<ItemType> filled_slots;
+
+  public:
+    CircularFifo() : CircularFifo(100){};
+    CircularFifo(uint32_t size) : fifo_size(size), free_slots(size + 1), filled_slots(size + 1) {
+
+        // TODO! how do we deal with alignment for writing? alignas???
+        // Do we give the user a chance to provide memory locations?
+        // Templated allocator?
+        for (size_t i = 0; i < fifo_size; ++i) {
+            free_slots.write(ItemType{});
+        }
+    }
+
+    bool next() {
+        // TODO! avoid default constructing ItemType
+        ItemType it;
+        if (!filled_slots.read(it))
+            return false;
+        if (!free_slots.write(std::move(it)))
+            return false;
+        return true;
+    }
+
+    ~CircularFifo() {}
+
+    using value_type = ItemType;
+
+    auto numFilledSlots() const noexcept { return filled_slots.sizeGuess(); }
+    auto numFreeSlots() const noexcept { return free_slots.sizeGuess(); }
+    auto isFull() const noexcept { return filled_slots.isFull(); }
+
+    ItemType pop_free() {
+        ItemType v;
+        while (!free_slots.read(v))
+            ;
+        return std::move(v);
+        // return v;
+    }
+
+    bool try_pop_free(ItemType &v) { return free_slots.read(v); }
+
+    ItemType pop_value(std::chrono::nanoseconds wait, std::atomic<bool> &stopped) {
+        ItemType v;
+        while (!filled_slots.read(v) && !stopped) {
+            std::this_thread::sleep_for(wait);
+        }
+        return std::move(v);
+    }
+
+    ItemType pop_value() {
+        ItemType v;
+        while (!filled_slots.read(v))
+            ;
+        return std::move(v);
+    }
+
+    ItemType *frontPtr() { return filled_slots.frontPtr(); }
+
+    // TODO! Add function to move item from filled to free to be used
+    // with the frontPtr function
+
+    template <class... Args> void push_value(Args &&...recordArgs) {
+        while (!filled_slots.write(std::forward<Args>(recordArgs)...))
+            ;
+    }
+
+    template <class... Args> bool try_push_value(Args &&...recordArgs) {
+        return filled_slots.write(std::forward<Args>(recordArgs)...);
+    }
+
+    template <class... Args> void push_free(Args &&...recordArgs) {
+        while (!free_slots.write(std::forward<Args>(recordArgs)...))
+            ;
+    }
+
+    template <class... Args> bool try_push_free(Args &&...recordArgs) {
+        return free_slots.write(std::forward<Args>(recordArgs)...);
+    }
+};
+
+} // namespace aare

include/aare/Cluster.hpp

@@ -0,0 +1,86 @@
+
+/************************************************
+ * @file Cluster.hpp
+ * @short definition of cluster, where CoordType (x,y) give
+ * the cluster center coordinates and data the actual cluster data
+ * cluster size is given as template parameters
+ ***********************************************/
+
+#pragma once
+
+#include <algorithm>
+#include <array>
+#include <cstdint>
+#include <numeric>
+#include <type_traits>
+
+namespace aare {
+
+// requires clause c++20 maybe update
+template <typename T, uint8_t ClusterSizeX, uint8_t ClusterSizeY,
+          typename CoordType = int16_t>
+struct Cluster {
+
+    static_assert(std::is_arithmetic_v<T>, "T needs to be an arithmetic type");
+    static_assert(std::is_integral_v<CoordType>,
+                  "CoordType needs to be an integral type");
+    static_assert(ClusterSizeX > 0 && ClusterSizeY > 0,
+                  "Cluster sizes must be bigger than zero");
+
+    CoordType x;
+    CoordType y;
+    std::array<T, ClusterSizeX * ClusterSizeY> data;
+
+    static constexpr uint8_t cluster_size_x = ClusterSizeX;
+    static constexpr uint8_t cluster_size_y = ClusterSizeY;
+    using value_type = T;
+    using coord_type = CoordType;
+
+    T sum() const { return std::accumulate(data.begin(), data.end(), T{}); }
+
+    std::pair<T, int> max_sum_2x2() const {
+
+        if constexpr (cluster_size_x == 3 && cluster_size_y == 3) {
+            std::array<T, 4> sum_2x2_subclusters;
+            sum_2x2_subclusters[0] = data[0] + data[1] + data[3] + data[4];
+            sum_2x2_subclusters[1] = data[1] + data[2] + data[4] + data[5];
+            sum_2x2_subclusters[2] = data[3] + data[4] + data[6] + data[7];
+            sum_2x2_subclusters[3] = data[4] + data[5] + data[7] + data[8];
+            int index = std::max_element(sum_2x2_subclusters.begin(),
+                                         sum_2x2_subclusters.end()) -
+                        sum_2x2_subclusters.begin();
+            return std::make_pair(sum_2x2_subclusters[index], index);
+        } else if constexpr (cluster_size_x == 2 && cluster_size_y == 2) {
+            return std::make_pair(data[0] + data[1] + data[2] + data[3], 0);
+        } else {
+            constexpr size_t num_2x2_subclusters =
+                (ClusterSizeX - 1) * (ClusterSizeY - 1);
+
+            std::array<T, num_2x2_subclusters> sum_2x2_subcluster;
+            for (size_t i = 0; i < ClusterSizeY - 1; ++i) {
+                for (size_t j = 0; j < ClusterSizeX - 1; ++j)
+                    sum_2x2_subcluster[i * (ClusterSizeX - 1) + j] =
+                        data[i * ClusterSizeX + j] +
+                        data[i * ClusterSizeX + j + 1] +
+                        data[(i + 1) * ClusterSizeX + j] +
+                        data[(i + 1) * ClusterSizeX + j + 1];
+            }
+
+            int index = std::max_element(sum_2x2_subcluster.begin(),
+                                         sum_2x2_subcluster.end()) -
+                        sum_2x2_subcluster.begin();
+            return std::make_pair(sum_2x2_subcluster[index], index);
+        }
+    }
+};
+
+// Type Traits for is_cluster_type
+template <typename T>
+struct is_cluster : std::false_type {}; // Default case: Not a Cluster
+
+template <typename T, uint8_t X, uint8_t Y, typename CoordType>
+struct is_cluster<Cluster<T, X, Y, CoordType>> : std::true_type {}; // Cluster
+
+template <typename T> constexpr bool is_cluster_v = is_cluster<T>::value;
+
+} // namespace aare

include/aare/ClusterCollector.hpp

@@ -0,0 +1,58 @@
+#pragma once
+#include <atomic>
+#include <thread>
+
+#include "aare/ClusterFinderMT.hpp"
+#include "aare/ClusterVector.hpp"
+#include "aare/ProducerConsumerQueue.hpp"
+
+namespace aare {
+
+template <typename ClusterType,
+          typename = std::enable_if_t<is_cluster_v<ClusterType>>>
+class ClusterCollector {
+    ProducerConsumerQueue<ClusterVector<ClusterType>> *m_source;
+    std::atomic<bool> m_stop_requested{false};
+    std::atomic<bool> m_stopped{true};
+    std::chrono::milliseconds m_default_wait{1};
+    std::thread m_thread;
+    std::vector<ClusterVector<ClusterType>> m_clusters;
+
+    void process() {
+        m_stopped = false;
+        fmt::print("ClusterCollector started\n");
+        while (!m_stop_requested || !m_source->isEmpty()) {
+            if (ClusterVector<ClusterType> *clusters = m_source->frontPtr();
+                clusters != nullptr) {
+                m_clusters.push_back(std::move(*clusters));
+                m_source->popFront();
+            } else {
+                std::this_thread::sleep_for(m_default_wait);
+            }
+        }
+        fmt::print("ClusterCollector stopped\n");
+        m_stopped = true;
+    }
+
+  public:
+    ClusterCollector(ClusterFinderMT<ClusterType, uint16_t, double> *source) {
+        m_source = source->sink();
+        m_thread =
+            std::thread(&ClusterCollector::process,
+                        this); // only one process does that so why isnt it
+                               // automatically written to m_cluster in collect
+                               // - instead of writing first to m_sink?
+    }
+    void stop() {
+        m_stop_requested = true;
+        m_thread.join();
+    }
+    std::vector<ClusterVector<ClusterType>> steal_clusters() {
+        if (!m_stopped) {
+            throw std::runtime_error("ClusterCollector is still running");
+        }
+        return std::move(m_clusters);
+    }
+};
+
+} // namespace aare

include/aare/ClusterFile.hpp

@@ -1,45 +1,18 @@
 #pragma once
 
+#include "aare/Cluster.hpp"
 #include "aare/ClusterVector.hpp"
+#include "aare/GainMap.hpp"
 #include "aare/NDArray.hpp"
 #include "aare/defs.hpp"
+#include "aare/logger.hpp"
+
 #include <filesystem>
 #include <fstream>
+#include <optional>
 
 namespace aare {
 
-struct Cluster3x3 {
-    int16_t x;
-    int16_t y;
-    int32_t data[9];
-};
-
-typedef enum {
-    cBottomLeft = 0,
-    cBottomRight = 1,
-    cTopLeft = 2,
-    cTopRight = 3
-} corner;
-
-typedef enum {
-    pBottomLeft = 0,
-    pBottom = 1,
-    pBottomRight = 2,
-    pLeft = 3,
-    pCenter = 4,
-    pRight = 5,
-    pTopLeft = 6,
-    pTop = 7,
-    pTopRight = 8
-} pixel;
-
-struct ClusterAnalysis {
-    uint32_t c;
-    int32_t tot;
-    double etax;
-    double etay;
-};
-
 /*
 Binary cluster file. Expects data to be layed out as:
 int32_t frame_number
@@ -49,33 +22,434 @@ int32_t frame_number
 uint32_t number_of_clusters
 ....
 */
+
+// TODO: change to support any type of clusters, e.g. header line with
+// clsuter_size_x, cluster_size_y,
+/**
+ * @brief Class to read and write cluster files
+ * Expects data to be laid out as:
+ *
+ *
+ *       int32_t frame_number
+ *       uint32_t number_of_clusters
+ *       int16_t x, int16_t y, int32_t data[9] * number_of_clusters
+ *       int32_t frame_number
+ *       uint32_t number_of_clusters
+ *       etc.
+ */
+template <typename ClusterType,
+          typename Enable = std::enable_if_t<is_cluster_v<ClusterType>>>
 class ClusterFile {
     FILE *fp{};
-    uint32_t m_num_left{};
-    size_t m_chunk_size{};
-    const std::string m_mode;
+    const std::string m_filename{};
+    uint32_t m_num_left{};    /*Number of photons left in frame*/
+    size_t m_chunk_size{};    /*Number of clusters to read at a time*/
+    std::string m_mode;       /*Mode to open the file in*/
+    std::optional<ROI> m_roi; /*Region of interest, will be applied if set*/
+    std::optional<NDArray<int32_t, 2>>
+        m_noise_map; /*Noise map to cut photons, will be applied if set*/
+    std::optional<InvertedGainMap> m_gain_map; /*Gain map to apply to the
+                                          clusters, will be applied if set*/
 
   public:
+    /**
+     * @brief Construct a new Cluster File object
+     * @param fname path to the file
+     * @param chunk_size number of clusters to read at a time when iterating
+     * over the file
+     * @param mode mode to open the file in. "r" for reading, "w" for writing,
+     * "a" for appending
+     * @throws std::runtime_error if the file could not be opened
+     */
     ClusterFile(const std::filesystem::path &fname, size_t chunk_size = 1000,
-                const std::string &mode = "r");
-    ~ClusterFile();
-    std::vector<Cluster3x3> read_clusters(size_t n_clusters);
-    std::vector<Cluster3x3> read_frame(int32_t &out_fnum);
-    void write_frame(int32_t frame_number,
-                     const ClusterVector<int32_t> &clusters);
-    std::vector<Cluster3x3>
-    read_cluster_with_cut(size_t n_clusters, double *noise_map, int nx, int ny);
+                const std::string &mode = "r")
 
+        : m_filename(fname.string()), m_chunk_size(chunk_size), m_mode(mode) {
+
+        if (mode == "r") {
+            fp = fopen(m_filename.c_str(), "rb");
+            if (!fp) {
+                throw std::runtime_error("Could not open file for reading: " +
+                                         m_filename);
+            }
+        } else if (mode == "w") {
+            fp = fopen(m_filename.c_str(), "wb");
+            if (!fp) {
+                throw std::runtime_error("Could not open file for writing: " +
+                                         m_filename);
+            }
+        } else if (mode == "a") {
+            fp = fopen(m_filename.c_str(), "ab");
+            if (!fp) {
+                throw std::runtime_error("Could not open file for appending: " +
+                                         m_filename);
+            }
+        } else {
+            throw std::runtime_error("Unsupported mode: " + mode);
+        }
+    }
+
+    ~ClusterFile() { close(); }
+
+    /**
+     * @brief Read n_clusters clusters from the file discarding
+     * frame numbers. If EOF is reached the returned vector will
+     * have less than n_clusters clusters
+     */
+    ClusterVector<ClusterType> read_clusters(size_t n_clusters) {
+        if (m_mode != "r") {
+            throw std::runtime_error("File not opened for reading");
+        }
+        if (m_noise_map || m_roi) {
+            return read_clusters_with_cut(n_clusters);
+        } else {
+            return read_clusters_without_cut(n_clusters);
+        }
+    }
+
+    /**
+     * @brief Read a single frame from the file and return the
+     * clusters. The cluster vector will have the frame number
+     * set.
+     * @throws std::runtime_error if the file is not opened for
+     * reading or the file pointer not at the beginning of a
+     * frame
+     */
+    ClusterVector<ClusterType> read_frame() {
+        if (m_mode != "r") {
+            throw std::runtime_error(LOCATION + "File not opened for reading");
+        }
+        if (m_noise_map || m_roi) {
+            return read_frame_with_cut();
+        } else {
+            return read_frame_without_cut();
+        }
+    }
+
+    void write_frame(const ClusterVector<ClusterType> &clusters) {
+        if (m_mode != "w" && m_mode != "a") {
+            throw std::runtime_error("File not opened for writing");
+        }
+
+        int32_t frame_number = clusters.frame_number();
+        fwrite(&frame_number, sizeof(frame_number), 1, fp);
+        uint32_t n_clusters = clusters.size();
+        fwrite(&n_clusters, sizeof(n_clusters), 1, fp);
+        fwrite(clusters.data(), clusters.item_size(), clusters.size(), fp);
+    }
+
+    /**
+     * @brief Return the chunk size
+     */
     size_t chunk_size() const { return m_chunk_size; }
-    void close();
+
+    /**
+     * @brief Set the region of interest to use when reading
+     * clusters. If set only clusters within the ROI will be
+     * read.
+     */
+    void set_roi(ROI roi) { m_roi = roi; }
+
+    /**
+     * @brief Set the noise map to use when reading clusters. If
+     * set clusters below the noise level will be discarded.
+     * Selection criteria one of: Central pixel above noise,
+     * highest 2x2 sum above 2 * noise, total sum above 3 *
+     * noise.
+     */
+    void set_noise_map(const NDView<int32_t, 2> noise_map) {
+        m_noise_map = NDArray<int32_t, 2>(noise_map);
+    }
+
+    /**
+     * @brief Set the gain map to use when reading clusters. If set the gain map
+     * will be applied to the clusters that pass ROI and noise_map selection.
+     * The gain map is expected to be in ADU/energy.
+     */
+    void set_gain_map(const NDView<double, 2> gain_map) {
+        m_gain_map = InvertedGainMap(gain_map);
+    }
+
+    void set_gain_map(const InvertedGainMap &gain_map) {
+        m_gain_map = gain_map;
+    }
+
+    void set_gain_map(const InvertedGainMap &&gain_map) {
+        m_gain_map = gain_map;
+    }
+
+    /**
+     * @brief Close the file. If not closed the file will be
+     * closed in the destructor
+     */
+    void close() {
+        if (fp) {
+            fclose(fp);
+            fp = nullptr;
+        }
+    }
+
+    /** @brief Open the file in specific mode
+     *
+     */
+    void open(const std::string &mode) {
+        if (fp) {
+            close();
+        }
+
+        if (mode == "r") {
+            fp = fopen(m_filename.c_str(), "rb");
+            if (!fp) {
+                throw std::runtime_error("Could not open file for reading: " +
+                                         m_filename);
+            }
+            m_mode = "r";
+        } else if (mode == "w") {
+            fp = fopen(m_filename.c_str(), "wb");
+            if (!fp) {
+                throw std::runtime_error("Could not open file for writing: " +
+                                         m_filename);
+            }
+            m_mode = "w";
+        } else if (mode == "a") {
+            fp = fopen(m_filename.c_str(), "ab");
+            if (!fp) {
+                throw std::runtime_error("Could not open file for appending: " +
+                                         m_filename);
+            }
+            m_mode = "a";
+        } else {
+            throw std::runtime_error("Unsupported mode: " + mode);
+        }
+    }
+
+  private:
+    ClusterVector<ClusterType> read_clusters_with_cut(size_t n_clusters);
+    ClusterVector<ClusterType> read_clusters_without_cut(size_t n_clusters);
+    ClusterVector<ClusterType> read_frame_with_cut();
+    ClusterVector<ClusterType> read_frame_without_cut();
+    bool is_selected(ClusterType &cl);
+    ClusterType read_one_cluster();
 };
 
-int analyze_data(int32_t *data, int32_t *t2, int32_t *t3, char *quad,
-                 double *eta2x, double *eta2y, double *eta3x, double *eta3y);
-int analyze_cluster(Cluster3x3& cl, int32_t *t2, int32_t *t3, char *quad,
-                    double *eta2x, double *eta2y, double *eta3x, double *eta3y);
+template <typename ClusterType, typename Enable>
+ClusterVector<ClusterType>
+ClusterFile<ClusterType, Enable>::read_clusters_without_cut(size_t n_clusters) {
+    if (m_mode != "r") {
+        throw std::runtime_error("File not opened for reading");
+    }
 
-NDArray<double, 2> calculate_eta2( ClusterVector<int>& clusters);
-std::array<double,2> calculate_eta2( Cluster3x3& cl);
+    ClusterVector<ClusterType> clusters(n_clusters);
+    clusters.resize(n_clusters);
+
+    int32_t iframe = 0; // frame number needs to be 4 bytes!
+    size_t nph_read = 0;
+    uint32_t nn = m_num_left;
+    uint32_t nph = m_num_left; // number of clusters in frame needs to be 4
+
+    auto buf = clusters.data();
+    // if there are photons left from previous frame read them first
+    if (nph) {
+        if (nph > n_clusters) {
+            // if we have more photons left in the frame then photons to
+            // read we read directly the requested number
+            nn = n_clusters;
+        } else {
+            nn = nph;
+        }
+        nph_read += fread((buf + nph_read), clusters.item_size(), nn, fp);
+        m_num_left = nph - nn; // write back the number of photons left
+    }
+
+    if (nph_read < n_clusters) {
+        // keep on reading frames and photons until reaching n_clusters
+        while (fread(&iframe, sizeof(iframe), 1, fp)) {
+            clusters.set_frame_number(iframe);
+            // read number of clusters in frame
+            if (fread(&nph, sizeof(nph), 1, fp)) {
+                if (nph > (n_clusters - nph_read))
+                    nn = n_clusters - nph_read;
+                else
+                    nn = nph;
+
+                nph_read +=
+                    fread((buf + nph_read), clusters.item_size(), nn, fp);
+                m_num_left = nph - nn;
+            }
+            if (nph_read >= n_clusters)
+                break;
+        }
+    }
+
+    // Resize the vector to the number o f clusters.
+    // No new allocation, only change bounds.
+    clusters.resize(nph_read);
+    if (m_gain_map)
+        m_gain_map->apply_gain_map(clusters);
+    return clusters;
+}
+
+template <typename ClusterType, typename Enable>
+ClusterVector<ClusterType>
+ClusterFile<ClusterType, Enable>::read_clusters_with_cut(size_t n_clusters) {
+    ClusterVector<ClusterType> clusters;
+    clusters.reserve(n_clusters);
+
+    // if there are photons left from previous frame read them first
+    if (m_num_left) {
+        while (m_num_left && clusters.size() < n_clusters) {
+            ClusterType c = read_one_cluster();
+            if (is_selected(c)) {
+                clusters.push_back(c);
+            }
+        }
+    }
+
+    // we did not have enough clusters left in the previous frame
+    // keep on reading frames until reaching n_clusters
+    if (clusters.size() < n_clusters) {
+        // sanity check
+        if (m_num_left) {
+            throw std::runtime_error(
+                LOCATION + "Entered second loop with clusters left\n");
+        }
+
+        int32_t frame_number = 0; // frame number needs to be 4 bytes!
+        while (fread(&frame_number, sizeof(frame_number), 1, fp)) {
+            if (fread(&m_num_left, sizeof(m_num_left), 1, fp)) {
+                clusters.set_frame_number(
+                    frame_number); // cluster vector will hold the last
+                                   // frame number
+                while (m_num_left && clusters.size() < n_clusters) {
+                    ClusterType c = read_one_cluster();
+                    if (is_selected(c)) {
+                        clusters.push_back(c);
+                    }
+                }
+            }
+
+            // we have enough clusters, break out of the outer while loop
+            if (clusters.size() >= n_clusters)
+                break;
+        }
+    }
+    if (m_gain_map)
+        m_gain_map->apply_gain_map(clusters);
+
+    return clusters;
+}
+
+template <typename ClusterType, typename Enable>
+ClusterType ClusterFile<ClusterType, Enable>::read_one_cluster() {
+    ClusterType c;
+    auto rc = fread(&c, sizeof(c), 1, fp);
+    if (rc != 1) {
+        throw std::runtime_error(LOCATION + "Could not read cluster");
+    }
+    --m_num_left;
+    return c;
+}
+
+template <typename ClusterType, typename Enable>
+ClusterVector<ClusterType>
+ClusterFile<ClusterType, Enable>::read_frame_without_cut() {
+    if (m_mode != "r") {
+        throw std::runtime_error("File not opened for reading");
+    }
+    if (m_num_left) {
+        throw std::runtime_error(
+            "There are still photons left in the last frame");
+    }
+    int32_t frame_number;
+    if (fread(&frame_number, sizeof(frame_number), 1, fp) != 1) {
+        throw std::runtime_error(LOCATION + "Could not read frame number");
+    }
+
+    int32_t n_clusters; // Saved as 32bit integer in the cluster file
+    if (fread(&n_clusters, sizeof(n_clusters), 1, fp) != 1) {
+        throw std::runtime_error(LOCATION +
+                                 "Could not read number of clusters");
+    }
+
+    LOG(logDEBUG1) << "Reading " << n_clusters
+                  << " clusters from frame " << frame_number;
+
+    ClusterVector<ClusterType> clusters(n_clusters);
+    clusters.set_frame_number(frame_number);
+    clusters.resize(n_clusters);
+
+    LOG(logDEBUG1) << "clusters.item_size(): " << clusters.item_size();
+
+    if (fread(clusters.data(), clusters.item_size(), n_clusters, fp) !=
+        static_cast<size_t>(n_clusters)) {
+        throw std::runtime_error(LOCATION + "Could not read clusters");
+    }
+
+    if (m_gain_map)
+        m_gain_map->apply_gain_map(clusters);
+    return clusters;
+}
+
+template <typename ClusterType, typename Enable>
+ClusterVector<ClusterType>
+ClusterFile<ClusterType, Enable>::read_frame_with_cut() {
+    if (m_mode != "r") {
+        throw std::runtime_error("File not opened for reading");
+    }
+    if (m_num_left) {
+        throw std::runtime_error(
+            "There are still photons left in the last frame");
+    }
+    int32_t frame_number;
+    if (fread(&frame_number, sizeof(frame_number), 1, fp) != 1) {
+        throw std::runtime_error("Could not read frame number");
+    }
+
+    if (fread(&m_num_left, sizeof(m_num_left), 1, fp) != 1) {
+        throw std::runtime_error("Could not read number of clusters");
+    }
+
+    ClusterVector<ClusterType> clusters;
+    clusters.reserve(m_num_left);
+    clusters.set_frame_number(frame_number);
+    while (m_num_left) {
+        ClusterType c = read_one_cluster();
+        if (is_selected(c)) {
+            clusters.push_back(c);
+        }
+    }
+    if (m_gain_map)
+        m_gain_map->apply_gain_map(clusters);
+    return clusters;
+}
+
+template <typename ClusterType, typename Enable>
+bool ClusterFile<ClusterType, Enable>::is_selected(ClusterType &cl) {
+    // Should fail fast
+    if (m_roi) {
+        if (!(m_roi->contains(cl.x, cl.y))) {
+            return false;
+        }
+    }
+
+    size_t cluster_center_index =
+        (ClusterType::cluster_size_x / 2) +
+        (ClusterType::cluster_size_y / 2) * ClusterType::cluster_size_x;
+
+    if (m_noise_map) {
+        auto sum_1x1 = cl.data[cluster_center_index]; // central pixel
+        auto sum_2x2 = cl.max_sum_2x2().first; // highest sum of 2x2 subclusters
+        auto total_sum = cl.sum();             // sum of all pixels
+
+        auto noise =
+            (*m_noise_map)(cl.y, cl.x); // TODO! check if this is correct
+        if (sum_1x1 <= noise || sum_2x2 <= 2 * noise ||
+            total_sum <= 3 * noise) {
+            return false;
+        }
+    }
+    // we passed all checks
+    return true;
+}
 
 } // namespace aare

include/aare/ClusterFileSink.hpp

@@ -0,0 +1,65 @@
+#pragma once
+#include <atomic>
+#include <filesystem>
+#include <thread>
+
+#include "aare/ClusterFinderMT.hpp"
+#include "aare/ClusterVector.hpp"
+#include "aare/ProducerConsumerQueue.hpp"
+
+namespace aare {
+
+template <typename ClusterType,
+          typename = std::enable_if_t<is_cluster_v<ClusterType>>>
+class ClusterFileSink {
+    ProducerConsumerQueue<ClusterVector<ClusterType>> *m_source;
+    std::atomic<bool> m_stop_requested{false};
+    std::atomic<bool> m_stopped{true};
+    std::chrono::milliseconds m_default_wait{1};
+    std::thread m_thread;
+    std::ofstream m_file;
+
+    void process() {
+        m_stopped = false;
+        LOG(logDEBUG) << "ClusterFileSink started";
+        while (!m_stop_requested || !m_source->isEmpty()) {
+            if (ClusterVector<ClusterType> *clusters = m_source->frontPtr();
+                clusters != nullptr) {
+                // Write clusters to file
+                int32_t frame_number =
+                    clusters->frame_number(); // TODO! Should we store frame
+                                              // number already as int?
+                uint32_t num_clusters = clusters->size();
+                m_file.write(reinterpret_cast<const char *>(&frame_number),
+                             sizeof(frame_number));
+                m_file.write(reinterpret_cast<const char *>(&num_clusters),
+                             sizeof(num_clusters));
+                m_file.write(reinterpret_cast<const char *>(clusters->data()),
+                             clusters->size() * clusters->item_size());
+                m_source->popFront();
+            } else {
+                std::this_thread::sleep_for(m_default_wait);
+            }
+        }
+        LOG(logDEBUG) << "ClusterFileSink stopped";
+        m_stopped = true;
+    }
+
+  public:
+    ClusterFileSink(ClusterFinderMT<ClusterType, uint16_t, double> *source,
+                    const std::filesystem::path &fname) {
+        LOG(logDEBUG) << "ClusterFileSink: "
+                       << "source: " << source->sink()
+                       << ", file: " << fname.string();
+        m_source = source->sink();
+        m_thread = std::thread(&ClusterFileSink::process, this);
+        m_file.open(fname, std::ios::binary);
+    }
+    void stop() {
+        m_stop_requested = true;
+        m_thread.join();
+        m_file.close();
+    }
+};
+
+} // namespace aare

include/aare/ClusterFileV2.hpp

@@ -1,148 +0,0 @@
-#pragma once
-#include "aare/core/defs.hpp"
-#include <filesystem>
-#include <string>
-#include <fmt/format.h>
-
-namespace aare {
-struct ClusterHeader {
-    int32_t frame_number;
-    int32_t n_clusters;
-    std::string to_string() const {
-        return "frame_number: " + std::to_string(frame_number) + ", n_clusters: " + std::to_string(n_clusters);
-    }
-};
-
-struct ClusterV2_ {
-    int16_t x;
-    int16_t y;
-    std::array<int32_t, 9> data;
-    std::string to_string(bool detailed = false) const {
-        if (detailed) {
-            std::string data_str = "[";
-            for (auto &d : data) {
-                data_str += std::to_string(d) + ", ";
-            }
-            data_str += "]";
-            return "x: " + std::to_string(x) + ", y: " + std::to_string(y) + ", data: " + data_str;
-        }
-        return "x: " + std::to_string(x) + ", y: " + std::to_string(y);
-    }
-};
-
-struct ClusterV2 {
-    ClusterV2_ cluster;
-    int32_t frame_number;
-    std::string to_string() const {
-        return "frame_number: " + std::to_string(frame_number) + ", " + cluster.to_string();
-    }
-};
-
-/**
- * @brief
- * important not: fp always points to the clusters header and does not point to individual clusters
- *
- */
-class ClusterFileV2 {
-    std::filesystem::path m_fpath; 
-    std::string m_mode;
-    FILE *fp{nullptr};
-
-    void check_open(){
-        if (!fp)
-            throw std::runtime_error(fmt::format("File: {} not open", m_fpath.string()));
-    }
-
-  public:
-    ClusterFileV2(std::filesystem::path const &fpath, std::string const &mode): m_fpath(fpath), m_mode(mode) {
-        if (m_mode != "r" && m_mode != "w")
-            throw std::invalid_argument("mode must be 'r' or 'w'");
-        if (m_mode == "r" && !std::filesystem::exists(m_fpath))
-            throw std::invalid_argument("File does not exist");
-        if (mode == "r") {
-            fp = fopen(fpath.string().c_str(), "rb");
-        } else if (mode == "w") {
-            if (std::filesystem::exists(fpath)) {
-                fp = fopen(fpath.string().c_str(), "r+b");
-            } else {
-                fp = fopen(fpath.string().c_str(), "wb");
-            }
-        }
-        if (fp == nullptr) {
-            throw std::runtime_error("Failed to open file");
-        }
-    }
-    ~ClusterFileV2() { close(); }
-    std::vector<ClusterV2> read() {
-        check_open();
-
-        ClusterHeader header;
-        fread(&header, sizeof(ClusterHeader), 1, fp); 
-        std::vector<ClusterV2_> clusters_(header.n_clusters);
-        fread(clusters_.data(), sizeof(ClusterV2_), header.n_clusters, fp);
-        std::vector<ClusterV2> clusters;
-        for (auto &c : clusters_) {
-            ClusterV2 cluster;
-            cluster.cluster = std::move(c);
-            cluster.frame_number = header.frame_number;
-            clusters.push_back(cluster);
-        }
-
-        return clusters;
-    }
-    std::vector<std::vector<ClusterV2>> read(int n_frames) {
-        std::vector<std::vector<ClusterV2>> clusters;
-        for (int i = 0; i < n_frames; i++) {
-            clusters.push_back(read());
-        }
-        return clusters;
-    }
-
-    size_t write(std::vector<ClusterV2> const &clusters) {
-        check_open();
-        if (m_mode != "w")
-            throw std::runtime_error("File not opened in write mode");
-        if (clusters.empty())
-            return 0;
-
-        ClusterHeader header;
-        header.frame_number = clusters[0].frame_number;
-        header.n_clusters = clusters.size();
-        fwrite(&header, sizeof(ClusterHeader), 1, fp);
-        for (auto &c : clusters) {
-            fwrite(&c.cluster, sizeof(ClusterV2_), 1, fp);
-        }
-        return clusters.size();
-    }
-
-    size_t write(std::vector<std::vector<ClusterV2>> const &clusters) {
-        check_open();
-        if (m_mode != "w") 
-            throw std::runtime_error("File not opened in write mode");
-
-        size_t n_clusters = 0;
-        for (auto &c : clusters) {
-            n_clusters += write(c);
-        }
-        return n_clusters;
-    }
-
-    int seek_to_begin() { return fseek(fp, 0, SEEK_SET); }
-    int seek_to_end() { return fseek(fp, 0, SEEK_END); }
-
-    int32_t frame_number() {
-        auto pos = ftell(fp);
-        ClusterHeader header;
-        fread(&header, sizeof(ClusterHeader), 1, fp);
-        fseek(fp, pos, SEEK_SET);
-        return header.frame_number;
-    }
-
-    void close() {
-        if (fp) {
-            fclose(fp);
-            fp = nullptr;
-        }
-    }
-};
-} // namespace aare

include/aare/ClusterFinder.hpp

@@ -10,31 +10,19 @@
 
 namespace aare {
 
-/** enum to define the event types */
-enum class eventType {
-    PEDESTAL,   /** pedestal */
-    NEIGHBOUR,  /** neighbour i.e. below threshold, but in the cluster of a
-                   photon */
-    PHOTON,     /** photon i.e. above threshold */
-    PHOTON_MAX, /** maximum of a cluster satisfying the photon conditions */
-    NEGATIVE_PEDESTAL, /** negative value, will not be accounted for as pedestal
-                          in order to avoid drift of the pedestal towards
-                          negative values */
-    UNDEFINED_EVENT = -1 /** undefined */
-};
-
-template <typename FRAME_TYPE = uint16_t, typename PEDESTAL_TYPE = double,
-          typename CT = int32_t>
+template <typename ClusterType = Cluster<int32_t, 3, 3>,
+          typename FRAME_TYPE = uint16_t, typename PEDESTAL_TYPE = double>
 class ClusterFinder {
     Shape<2> m_image_size;
-    const int m_cluster_sizeX;
-    const int m_cluster_sizeY;
-    // const PEDESTAL_TYPE m_threshold;
     const PEDESTAL_TYPE m_nSigma;
     const PEDESTAL_TYPE c2;
     const PEDESTAL_TYPE c3;
     Pedestal<PEDESTAL_TYPE> m_pedestal;
-    ClusterVector<CT> m_clusters;
+    ClusterVector<ClusterType> m_clusters;
+
+    static const uint8_t ClusterSizeX = ClusterType::cluster_size_x;
+    static const uint8_t ClusterSizeY = ClusterType::cluster_size_y;
+    using CT = typename ClusterType::value_type;
 
   public:
     /**
@@ -45,15 +33,17 @@ class ClusterFinder {
      * @param capacity initial capacity of the cluster vector
      *
      */
-    ClusterFinder(Shape<2> image_size, Shape<2> cluster_size,
-                  PEDESTAL_TYPE nSigma = 5.0, size_t capacity = 1000000)
-        : m_image_size(image_size), m_cluster_sizeX(cluster_size[0]),
-          m_cluster_sizeY(cluster_size[1]),
-          m_nSigma(nSigma),
-          c2(sqrt((m_cluster_sizeY + 1) / 2 * (m_cluster_sizeX + 1) / 2)),
-          c3(sqrt(m_cluster_sizeX * m_cluster_sizeY)),
-          m_pedestal(image_size[0], image_size[1]),
-          m_clusters(m_cluster_sizeX, m_cluster_sizeY, capacity) {};
+    ClusterFinder(Shape<2> image_size, PEDESTAL_TYPE nSigma = 5.0,
+                  size_t capacity = 1000000)
+        : m_image_size(image_size), m_nSigma(nSigma),
+          c2(sqrt((ClusterSizeY + 1) / 2 * (ClusterSizeX + 1) / 2)),
+          c3(sqrt(ClusterSizeX * ClusterSizeY)),
+          m_pedestal(image_size[0], image_size[1]), m_clusters(capacity) {
+        LOG(logDEBUG ) << "ClusterFinder: "
+                       << "image_size: " << image_size[0] << "x" << image_size[1]
+                       << ", nSigma: " << nSigma
+                       << ", capacity: " << capacity;
+    }
 
     void push_pedestal_frame(NDView<FRAME_TYPE, 2> frame) {
         m_pedestal.push(frame);
@@ -61,6 +51,7 @@ class ClusterFinder {
 
     NDArray<PEDESTAL_TYPE, 2> pedestal() { return m_pedestal.mean(); }
     NDArray<PEDESTAL_TYPE, 2> noise() { return m_pedestal.std(); }
+    void clear_pedestal() { m_pedestal.clear(); }
 
     /**
      * @brief Move the clusters from the ClusterVector in the ClusterFinder to a
@@ -69,23 +60,28 @@ class ClusterFinder {
      * same capacity as the old one
      *
      */
-    ClusterVector<CT> steal_clusters(bool realloc_same_capacity = false) {
-        ClusterVector<CT> tmp = std::move(m_clusters);
+    ClusterVector<ClusterType>
+    steal_clusters(bool realloc_same_capacity = false) {
+        ClusterVector<ClusterType> tmp = std::move(m_clusters);
         if (realloc_same_capacity)
-            m_clusters = ClusterVector<CT>(m_cluster_sizeX, m_cluster_sizeY,
-                                           tmp.capacity());
+            m_clusters = ClusterVector<ClusterType>(tmp.capacity());
         else
-            m_clusters = ClusterVector<CT>(m_cluster_sizeX, m_cluster_sizeY);
+            m_clusters = ClusterVector<ClusterType>{};
         return tmp;
     }
-    void find_clusters(NDView<FRAME_TYPE, 2> frame) {
+    void find_clusters(NDView<FRAME_TYPE, 2> frame, uint64_t frame_number = 0) {
         // // TODO! deal with even size clusters
         // // currently 3,3 -> +/- 1
         // //  4,4 -> +/- 2
-        int dy = m_cluster_sizeY / 2;
-        int dx = m_cluster_sizeX / 2;
+        int dy = ClusterSizeY / 2;
+        int dx = ClusterSizeX / 2;
+        int has_center_pixel_x =
+            ClusterSizeX %
+            2; // for even sized clusters there is no proper cluster center and
+               // even amount of pixels around the center
+        int has_center_pixel_y = ClusterSizeY % 2;
 
-        std::vector<CT> cluster_data(m_cluster_sizeX * m_cluster_sizeY);
+        m_clusters.set_frame_number(frame_number);
         for (int iy = 0; iy < frame.shape(0); iy++) {
             for (int ix = 0; ix < frame.shape(1); ix++) {
 
@@ -100,8 +96,8 @@ class ClusterFinder {
                     continue; // NEGATIVE_PEDESTAL go to next pixel
                               // TODO! No pedestal update???
 
-                for (int ir = -dy; ir < dy + 1; ir++) {
-                    for (int ic = -dx; ic < dx + 1; ic++) {
+                for (int ir = -dy; ir < dy + has_center_pixel_y; ir++) {
+                    for (int ic = -dx; ic < dx + has_center_pixel_x; ic++) {
                         if (ix + ic >= 0 && ix + ic < frame.shape(1) &&
                             iy + ir >= 0 && iy + ir < frame.shape(0)) {
                             PEDESTAL_TYPE val =
@@ -121,28 +117,34 @@ class ClusterFinder {
                 } else if (total > c3 * m_nSigma * rms) {
                     // pass
                 } else {
-                    // m_pedestal.push(iy, ix, frame(iy, ix));
-                    m_pedestal.push_fast(iy, ix, frame(iy, ix));
-                    continue; // It was a pedestal value nothing to store
+                    // m_pedestal.push(iy, ix, frame(iy, ix));   // Safe option
+                    m_pedestal.push_fast(
+                        iy, ix,
+                        frame(iy,
+                              ix)); // Assume we have reached n_samples in the
+                                    // pedestal, slight performance improvement
+                    continue;       // It was a pedestal value nothing to store
                 }
 
                 // Store cluster
                 if (value == max) {
-                    // Zero out the cluster data
-                    std::fill(cluster_data.begin(), cluster_data.end(), 0);
+                    ClusterType cluster{};
+                    cluster.x = ix;
+                    cluster.y = iy;
 
                     // Fill the cluster data since we have a photon to store
                     // It's worth redoing the look since most of the time we
                     // don't have a photon
                     int i = 0;
-                    for (int ir = -dy; ir < dy + 1; ir++) {
-                        for (int ic = -dx; ic < dx + 1; ic++) {
+                    for (int ir = -dy; ir < dy + has_center_pixel_y; ir++) {
+                        for (int ic = -dx; ic < dx + has_center_pixel_y; ic++) {
                             if (ix + ic >= 0 && ix + ic < frame.shape(1) &&
                                 iy + ir >= 0 && iy + ir < frame.shape(0)) {
                                 CT tmp =
                                     static_cast<CT>(frame(iy + ir, ix + ic)) -
-                                    m_pedestal.mean(iy + ir, ix + ic);
-                                cluster_data[i] =
+                                    static_cast<CT>(
+                                        m_pedestal.mean(iy + ir, ix + ic));
+                                cluster.data[i] =
                                     tmp; // Watch for out of bounds access
                                 i++;
                             }
@@ -150,121 +152,11 @@ class ClusterFinder {
                     }
 
                     // Add the cluster to the output ClusterVector
-                    m_clusters.push_back(
-                        ix, iy,
-                        reinterpret_cast<std::byte *>(cluster_data.data()));
+                    m_clusters.push_back(cluster);
                 }
             }
         }
     }
-
-    // // template <typename FRAME_TYPE, typename PEDESTAL_TYPE>
-    // std::vector<DynamicCluster>
-    // find_clusters_with_threshold(NDView<FRAME_TYPE, 2> frame,
-    //                              Pedestal<PEDESTAL_TYPE> &pedestal) {
-    //     assert(m_threshold > 0);
-    //     std::vector<DynamicCluster> clusters;
-    //     std::vector<std::vector<eventType>> eventMask;
-    //     for (int i = 0; i < frame.shape(0); i++) {
-    //         eventMask.push_back(std::vector<eventType>(frame.shape(1)));
-    //     }
-    //     double tthr, tthr1, tthr2;
-
-    //     NDArray<FRAME_TYPE, 2> rest({frame.shape(0), frame.shape(1)});
-    //     NDArray<int, 2> nph({frame.shape(0), frame.shape(1)});
-    //     // convert to n photons
-    //     // nph = (frame-pedestal.mean()+0.5*m_threshold)/m_threshold; // can
-    //     be
-    //     // optimized with expression templates?
-    //     for (int iy = 0; iy < frame.shape(0); iy++) {
-    //         for (int ix = 0; ix < frame.shape(1); ix++) {
-    //             auto val = frame(iy, ix) - pedestal.mean(iy, ix);
-    //             nph(iy, ix) = (val + 0.5 * m_threshold) / m_threshold;
-    //             nph(iy, ix) = nph(iy, ix) < 0 ? 0 : nph(iy, ix);
-    //             rest(iy, ix) = val - nph(iy, ix) * m_threshold;
-    //         }
-    //     }
-    //     // iterate over frame pixels
-    //     for (int iy = 0; iy < frame.shape(0); iy++) {
-    //         for (int ix = 0; ix < frame.shape(1); ix++) {
-    //             eventMask[iy][ix] = eventType::PEDESTAL;
-    //             // initialize max and total
-    //             FRAME_TYPE max = std::numeric_limits<FRAME_TYPE>::min();
-    //             long double total = 0;
-    //             if (rest(iy, ix) <= 0.25 * m_threshold) {
-    //                 pedestal.push(iy, ix, frame(iy, ix));
-    //                 continue;
-    //             }
-    //             eventMask[iy][ix] = eventType::NEIGHBOUR;
-    //             // iterate over cluster pixels around the current pixel
-    //             (ix,iy) for (short ir = -(m_cluster_sizeY / 2);
-    //                  ir < (m_cluster_sizeY / 2) + 1; ir++) {
-    //                 for (short ic = -(m_cluster_sizeX / 2);
-    //                      ic < (m_cluster_sizeX / 2) + 1; ic++) {
-    //                     if (ix + ic >= 0 && ix + ic < frame.shape(1) &&
-    //                         iy + ir >= 0 && iy + ir < frame.shape(0)) {
-    //                         auto val = frame(iy + ir, ix + ic) -
-    //                                    pedestal.mean(iy + ir, ix + ic);
-    //                         total += val;
-    //                         if (val > max) {
-    //                             max = val;
-    //                         }
-    //                     }
-    //                 }
-    //             }
-
-    //             auto rms = pedestal.std(iy, ix);
-    //             if (m_nSigma == 0) {
-    //                 tthr = m_threshold;
-    //                 tthr1 = m_threshold;
-    //                 tthr2 = m_threshold;
-    //             } else {
-    //                 tthr = m_nSigma * rms;
-    //                 tthr1 = m_nSigma * rms * c3;
-    //                 tthr2 = m_nSigma * rms * c2;
-
-    //                 if (m_threshold > 2 * tthr)
-    //                     tthr = m_threshold - tthr;
-    //                 if (m_threshold > 2 * tthr1)
-    //                     tthr1 = tthr - tthr1;
-    //                 if (m_threshold > 2 * tthr2)
-    //                     tthr2 = tthr - tthr2;
-    //             }
-    //             if (total > tthr1 || max > tthr) {
-    //                 eventMask[iy][ix] = eventType::PHOTON;
-    //                 nph(iy, ix) += 1;
-    //                 rest(iy, ix) -= m_threshold;
-    //             } else {
-    //                 pedestal.push(iy, ix, frame(iy, ix));
-    //                 continue;
-    //             }
-    //             if (eventMask[iy][ix] == eventType::PHOTON &&
-    //                 frame(iy, ix) - pedestal.mean(iy, ix) >= max) {
-    //                 eventMask[iy][ix] = eventType::PHOTON_MAX;
-    //                 DynamicCluster cluster(m_cluster_sizeX, m_cluster_sizeY,
-    //                                        Dtype(typeid(FRAME_TYPE)));
-    //                 cluster.x = ix;
-    //                 cluster.y = iy;
-    //                 short i = 0;
-    //                 for (short ir = -(m_cluster_sizeY / 2);
-    //                      ir < (m_cluster_sizeY / 2) + 1; ir++) {
-    //                     for (short ic = -(m_cluster_sizeX / 2);
-    //                          ic < (m_cluster_sizeX / 2) + 1; ic++) {
-    //                         if (ix + ic >= 0 && ix + ic < frame.shape(1) &&
-    //                             iy + ir >= 0 && iy + ir < frame.shape(0)) {
-    //                             auto tmp = frame(iy + ir, ix + ic) -
-    //                                        pedestal.mean(iy + ir, ix + ic);
-    //                             cluster.set<FRAME_TYPE>(i, tmp);
-    //                             i++;
-    //                         }
-    //                     }
-    //                 }
-    //                 clusters.push_back(cluster);
-    //             }
-    //         }
-    //     }
-    //     return clusters;
-    // }
 };
 
 } // namespace aare

include/aare/ClusterFinderMT.hpp

@@ -0,0 +1,284 @@
+#pragma once
+#include <atomic>
+#include <cstdint>
+#include <memory>
+#include <thread>
+#include <vector>
+
+#include "aare/ClusterFinder.hpp"
+#include "aare/NDArray.hpp"
+#include "aare/logger.hpp"
+#include "aare/ProducerConsumerQueue.hpp"
+
+namespace aare {
+
+enum class FrameType {
+    DATA,
+    PEDESTAL,
+};
+
+struct FrameWrapper {
+    FrameType type;
+    uint64_t frame_number;
+    NDArray<uint16_t, 2> data;
+};
+
+/**
+ * @brief ClusterFinderMT is a multi-threaded version of ClusterFinder. It uses
+ * a producer-consumer queue to distribute the frames to the threads. The
+ * clusters are collected in a single output queue.
+ * @tparam FRAME_TYPE type of the frame data
+ * @tparam PEDESTAL_TYPE type of the pedestal data
+ * @tparam CT type of the cluster data
+ */
+template <typename ClusterType = Cluster<int32_t, 3, 3>,
+          typename FRAME_TYPE = uint16_t, typename PEDESTAL_TYPE = double>
+class ClusterFinderMT {
+
+  protected:
+    using CT = typename ClusterType::value_type;
+    size_t m_current_thread{0};
+    size_t m_n_threads{0};
+    using Finder = ClusterFinder<ClusterType, FRAME_TYPE, PEDESTAL_TYPE>;
+    using InputQueue = ProducerConsumerQueue<FrameWrapper>;
+    using OutputQueue = ProducerConsumerQueue<ClusterVector<ClusterType>>;
+    std::vector<std::unique_ptr<InputQueue>> m_input_queues;
+    std::vector<std::unique_ptr<OutputQueue>> m_output_queues;
+
+    OutputQueue m_sink{1000}; // All clusters go into this queue
+
+    std::vector<std::unique_ptr<Finder>> m_cluster_finders;
+    std::vector<std::thread> m_threads;
+    std::thread m_collect_thread;
+    std::chrono::milliseconds m_default_wait{1};
+
+  private:
+    std::atomic<bool> m_stop_requested{false};
+    std::atomic<bool> m_processing_threads_stopped{true};
+
+    /**
+     * @brief Function called by the processing threads. It reads the frames
+     * from the input queue and processes them.
+     */
+    void process(int thread_id) {
+        auto cf = m_cluster_finders[thread_id].get();
+        auto q = m_input_queues[thread_id].get();
+        bool realloc_same_capacity = true;
+
+        while (!m_stop_requested || !q->isEmpty()) {
+            if (FrameWrapper *frame = q->frontPtr(); frame != nullptr) {
+
+                switch (frame->type) {
+                case FrameType::DATA:
+                    cf->find_clusters(frame->data.view(), frame->frame_number);
+                    m_output_queues[thread_id]->write(
+                        cf->steal_clusters(realloc_same_capacity));
+                    break;
+
+                case FrameType::PEDESTAL:
+                    m_cluster_finders[thread_id]->push_pedestal_frame(
+                        frame->data.view());
+                    break;
+                }
+
+                // frame is processed now discard it
+                m_input_queues[thread_id]->popFront();
+            } else {
+                std::this_thread::sleep_for(m_default_wait);
+            }
+        }
+    }
+
+    /**
+     * @brief Collect all the clusters from the output queues and write them to
+     * the sink
+     */
+    void collect() {
+        bool empty = true;
+        while (!m_stop_requested || !empty || !m_processing_threads_stopped) {
+            empty = true;
+            for (auto &queue : m_output_queues) {
+                if (!queue->isEmpty()) {
+
+                    while (!m_sink.write(std::move(*queue->frontPtr()))) {
+                        std::this_thread::sleep_for(m_default_wait);
+                    }
+                    queue->popFront();
+                    empty = false;
+                }
+            }
+        }
+    }
+
+  public:
+    /**
+     * @brief Construct a new ClusterFinderMT object
+     * @param image_size size of the image
+     * @param cluster_size size of the cluster
+     * @param nSigma number of sigma above the pedestal to consider a photon
+     * @param capacity initial capacity of the cluster vector. Should match
+     * expected number of clusters in a frame per frame.
+     * @param n_threads number of threads to use
+     */
+    ClusterFinderMT(Shape<2> image_size, PEDESTAL_TYPE nSigma = 5.0,
+                    size_t capacity = 2000, size_t n_threads = 3)
+        : m_n_threads(n_threads) {
+
+        LOG(logDEBUG1) << "ClusterFinderMT: "
+            << "image_size: " << image_size[0] << "x" << image_size[1]
+            << ", nSigma: " << nSigma
+            << ", capacity: " << capacity
+            << ", n_threads: " << n_threads;
+
+        for (size_t i = 0; i < n_threads; i++) {
+            m_cluster_finders.push_back(
+                std::make_unique<
+                    ClusterFinder<ClusterType, FRAME_TYPE, PEDESTAL_TYPE>>(
+                    image_size, nSigma, capacity));
+        }
+        for (size_t i = 0; i < n_threads; i++) {
+            m_input_queues.emplace_back(std::make_unique<InputQueue>(200));
+            m_output_queues.emplace_back(std::make_unique<OutputQueue>(200));
+        }
+        // TODO! Should we start automatically?
+        start();
+    }
+
+    /**
+     * @brief Return the sink queue where all the clusters are collected
+     * @warning You need to empty this queue otherwise the cluster finder will
+     * wait forever
+     */
+    ProducerConsumerQueue<ClusterVector<ClusterType>> *sink() {
+        return &m_sink;
+    }
+
+    /**
+     * @brief Start all processing threads
+     */
+    void start() {
+        m_processing_threads_stopped = false;
+        m_stop_requested = false;
+
+        for (size_t i = 0; i < m_n_threads; i++) {
+            m_threads.push_back(
+                std::thread(&ClusterFinderMT::process, this, i));
+        }
+
+        m_collect_thread = std::thread(&ClusterFinderMT::collect, this);
+    }
+
+    /**
+     * @brief Stop all processing threads
+     */
+    void stop() {
+        m_stop_requested = true;
+
+        for (auto &thread : m_threads) {
+            thread.join();
+        }
+        m_threads.clear();
+
+        m_processing_threads_stopped = true;
+        m_collect_thread.join();
+    }
+
+    /**
+     * @brief Wait for all the queues to be empty. Mostly used for timing tests.
+     */
+    void sync() {
+        for (auto &q : m_input_queues) {
+            while (!q->isEmpty()) {
+                std::this_thread::sleep_for(m_default_wait);
+            }
+        }
+        for (auto &q : m_output_queues) {
+            while (!q->isEmpty()) {
+                std::this_thread::sleep_for(m_default_wait);
+            }
+        }
+        while (!m_sink.isEmpty()) {
+            std::this_thread::sleep_for(m_default_wait);
+        }
+    }
+
+    /**
+     * @brief Push a pedestal frame to all the cluster finders. The frames is
+     * expected to be dark. No photon finding is done. Just pedestal update.
+     */
+    void push_pedestal_frame(NDView<FRAME_TYPE, 2> frame) {
+        FrameWrapper fw{FrameType::PEDESTAL, 0,
+                        NDArray(frame)}; // TODO! copies the data!
+
+        for (auto &queue : m_input_queues) {
+            while (!queue->write(fw)) {
+                std::this_thread::sleep_for(m_default_wait);
+            }
+        }
+    }
+
+    /**
+     * @brief Push the frame to the queue of the next available thread. Function
+     * returns once the frame is in a queue.
+     * @note Spin locks with a default wait if the queue is full.
+     */
+    void find_clusters(NDView<FRAME_TYPE, 2> frame, uint64_t frame_number = 0) {
+        FrameWrapper fw{FrameType::DATA, frame_number,
+                        NDArray(frame)}; // TODO! copies the data!
+        while (!m_input_queues[m_current_thread % m_n_threads]->write(fw)) {
+            std::this_thread::sleep_for(m_default_wait);
+        }
+        m_current_thread++;
+    }
+
+    void clear_pedestal() {
+        if (!m_processing_threads_stopped) {
+            throw std::runtime_error("ClusterFinderMT is still running");
+        }
+        for (auto &cf : m_cluster_finders) {
+            cf->clear_pedestal();
+        }
+    }
+
+    /**
+     * @brief Return the pedestal currently used by the cluster finder
+     * @param thread_index index of the thread
+     */
+    auto pedestal(size_t thread_index = 0) {
+        if (m_cluster_finders.empty()) {
+            throw std::runtime_error("No cluster finders available");
+        }
+        if (!m_processing_threads_stopped) {
+            throw std::runtime_error("ClusterFinderMT is still running");
+        }
+        if (thread_index >= m_cluster_finders.size()) {
+            throw std::runtime_error("Thread index out of range");
+        }
+        return m_cluster_finders[thread_index]->pedestal();
+    }
+
+    /**
+     * @brief Return the noise currently used by the cluster finder
+     * @param thread_index index of the thread
+     */
+    auto noise(size_t thread_index = 0) {
+        if (m_cluster_finders.empty()) {
+            throw std::runtime_error("No cluster finders available");
+        }
+        if (!m_processing_threads_stopped) {
+            throw std::runtime_error("ClusterFinderMT is still running");
+        }
+        if (thread_index >= m_cluster_finders.size()) {
+            throw std::runtime_error("Thread index out of range");
+        }
+        return m_cluster_finders[thread_index]->noise();
+    }
+
+    // void push(FrameWrapper&& frame) {
+    //     //TODO! need to loop until we are successful
+    //     auto rc = m_input_queue.write(std::move(frame));
+    //     fmt::print("pushed frame {}\n", rc);
+    // }
+};
+
+} // namespace aare

include/aare/ClusterVector.hpp

@@ -1,4 +1,7 @@
 #pragma once
+#include "aare/Cluster.hpp" //TODO maybe store in seperate file !!!
+#include <algorithm>
+#include <array>
 #include <cstddef>
 #include <cstdint>
 #include <numeric>
@@ -6,174 +9,161 @@
 
 #include <fmt/core.h>
 
+#include "aare/Cluster.hpp"
+#include "aare/NDView.hpp"
+
 namespace aare {
 
+template <typename ClusterType,
+          typename = std::enable_if_t<is_cluster_v<ClusterType>>>
+class ClusterVector; // Forward declaration
+
 /**
- * @brief ClusterVector is a container for clusters of various sizes. It uses a 
- * contiguous memory buffer to store the clusters. 
+ * @brief ClusterVector is a container for clusters of various sizes. It
+ * uses a contiguous memory buffer to store the clusters. It is templated on
+ * the data type and the coordinate type of the clusters.
  * @note push_back can invalidate pointers to elements in the container
+ * @warning ClusterVector is currently move only to catch unintended copies,
+ * but this might change since there are probably use cases where copying is
+ * needed.
  * @tparam T data type of the pixels in the cluster
- * @tparam CoordType data type of the x and y coordinates of the cluster (normally int16_t)
+ * @tparam CoordType data type of the x and y coordinates of the cluster
+ * (normally int16_t)
  */
-template <typename T, typename CoordType=int16_t> class ClusterVector {
-    using value_type = T;
-    size_t m_cluster_size_x;
-    size_t m_cluster_size_y;
-    std::byte *m_data{};
-    size_t m_size{0};
-    size_t m_capacity;
-    /*
-    Format string used in the python bindings to create a numpy
-    array from the buffer
-    = - native byte order
-    h - short
-    d - double
-    i - int
-    */
-    constexpr static char m_fmt_base[] = "=h:x:\nh:y:\n({},{}){}:data:" ;
+template <typename T, uint8_t ClusterSizeX, uint8_t ClusterSizeY,
+          typename CoordType>
+class ClusterVector<Cluster<T, ClusterSizeX, ClusterSizeY, CoordType>> {
+
+    std::vector<Cluster<T, ClusterSizeX, ClusterSizeY, CoordType>> m_data{};
+    int32_t m_frame_number{0}; // TODO! Check frame number size and type
 
   public:
+    using value_type = T;
+    using ClusterType = Cluster<T, ClusterSizeX, ClusterSizeY, CoordType>;
+
     /**
      * @brief Construct a new ClusterVector object
-     * @param cluster_size_x size of the cluster in x direction
-     * @param cluster_size_y size of the cluster in y direction
      * @param capacity initial capacity of the buffer in number of clusters
+     * @param frame_number frame number of the clusters. Default is 0, which is
+     * also used to indicate that the clusters come from many frames
      */
-    ClusterVector(size_t cluster_size_x, size_t cluster_size_y,
-                  size_t capacity = 1024)
-        : m_cluster_size_x(cluster_size_x), m_cluster_size_y(cluster_size_y),
-          m_capacity(capacity) {
-        allocate_buffer(capacity);
-    }
-    ~ClusterVector() {
-        delete[] m_data;
+    ClusterVector(size_t capacity = 1024, uint64_t frame_number = 0)
+        : m_frame_number(frame_number) {
+        m_data.reserve(capacity);
     }
 
-   
-    //Move constructor
+    // Move constructor
     ClusterVector(ClusterVector &&other) noexcept
-        : m_cluster_size_x(other.m_cluster_size_x),
-          m_cluster_size_y(other.m_cluster_size_y), m_data(other.m_data),
-          m_size(other.m_size), m_capacity(other.m_capacity) {
-        other.m_data = nullptr;
-        other.m_size = 0;
-        other.m_capacity = 0;
+        : m_data(other.m_data), m_frame_number(other.m_frame_number) {
+        other.m_data.clear();
     }
 
-    //Move assignment operator
-    ClusterVector& operator=(ClusterVector &&other) noexcept {
+    // Move assignment operator
+    ClusterVector &operator=(ClusterVector &&other) noexcept {
         if (this != &other) {
-            delete[] m_data;
-            m_cluster_size_x = other.m_cluster_size_x;
-            m_cluster_size_y = other.m_cluster_size_y;
             m_data = other.m_data;
-            m_size = other.m_size;
-            m_capacity = other.m_capacity;
-            other.m_data = nullptr;
-            other.m_size = 0;
-            other.m_capacity = 0;
+            m_frame_number = other.m_frame_number;
+            other.m_data.clear();
+            other.m_frame_number = 0;
         }
         return *this;
     }
 
-    /**
-     * @brief Reserve space for at least capacity clusters
-     * @param capacity number of clusters to reserve space for
-     * @note If capacity is less than the current capacity, the function does nothing. 
-     */
-    void reserve(size_t capacity) {
-        if (capacity > m_capacity) {
-            allocate_buffer(capacity);
-        }
-    }
-
-    /**
-     * @brief Add a cluster to the vector
-     * @param x x-coordinate of the cluster
-     * @param y y-coordinate of the cluster
-     * @param data pointer to the data of the cluster
-     * @warning The data pointer must point to a buffer of size cluster_size_x * cluster_size_y * sizeof(T)
-     */
-    void push_back(CoordType x, CoordType y, const std::byte *data) {
-        if (m_size == m_capacity) {
-            allocate_buffer(m_capacity * 2);
-        }
-        std::byte *ptr = element_ptr(m_size);
-        *reinterpret_cast<CoordType *>(ptr) = x;
-        ptr += sizeof(CoordType);
-        *reinterpret_cast<CoordType *>(ptr) = y;
-        ptr += sizeof(CoordType);
-
-        std::copy(data, data + m_cluster_size_x * m_cluster_size_y * sizeof(T),
-                  ptr);
-        m_size++;
-    }
-
-
     /**
      * @brief Sum the pixels in each cluster
      * @return std::vector<T> vector of sums for each cluster
      */
     std::vector<T> sum() {
-        std::vector<T> sums(m_size);
-        const size_t stride = element_offset();
-        const size_t n_pixels = m_cluster_size_x * m_cluster_size_y;
-        std::byte *ptr = m_data + 2 * sizeof(CoordType); // skip x and y
+        std::vector<T> sums(m_data.size());
+
+        std::transform(
+            m_data.begin(), m_data.end(), sums.begin(),
+            [](const ClusterType &cluster) { return cluster.sum(); });
 
-        for (size_t i = 0; i < m_size; i++) {
-            sums[i] =
-                std::accumulate(reinterpret_cast<T *>(ptr),
-                                reinterpret_cast<T *>(ptr) + n_pixels, T{});
-            ptr += stride;
-        }
         return sums;
     }
 
-    size_t size() const { return m_size; }
-    size_t capacity() const { return m_capacity; }
-    
     /**
-     * @brief Return the offset in bytes for a single cluster
+     * @brief Sum the pixels in the 2x2 subcluster with the biggest pixel sum in
+     * each cluster
+     * @return std::vector<T> vector of sums for each cluster
      */
-    size_t element_offset() const {
-        return 2*sizeof(CoordType)  +
-               m_cluster_size_x * m_cluster_size_y * sizeof(T);
-    }
-    /**
-     * @brief Return the offset in bytes for the i-th cluster
-     */
-    size_t element_offset(size_t i) const { return element_offset() * i; }
+    std::vector<T> sum_2x2() {
+        std::vector<T> sums_2x2(m_data.size());
 
-    /**
-     * @brief Return a pointer to the i-th cluster
-     */
-    std::byte *element_ptr(size_t i) { return m_data + element_offset(i); }
-     const std::byte *  element_ptr(size_t i) const { return m_data + element_offset(i); }
+        std::transform(m_data.begin(), m_data.end(), sums_2x2.begin(),
+                       [](const ClusterType &cluster) {
+                           return cluster.max_sum_2x2().first;
+                       });
 
-    size_t cluster_size_x() const { return m_cluster_size_x; }
-    size_t cluster_size_y() const { return m_cluster_size_y; }
-
-    std::byte *data() { return m_data; }
-    std::byte const *data() const { return m_data; }
-
-    template<typename V>
-    V& at(size_t i) {
-        return *reinterpret_cast<V*>(element_ptr(i));
+        return sums_2x2;
     }
 
-    const std::string_view fmt_base() const {
-        //TODO! how do we match on coord_t?
-        return m_fmt_base;
+    /**
+     * @brief Reserve space for at least capacity clusters
+     * @param capacity number of clusters to reserve space for
+     * @note If capacity is less than the current capacity, the function does
+     * nothing.
+     */
+    void reserve(size_t capacity) { m_data.reserve(capacity); }
+
+    void resize(size_t size) { m_data.resize(size); }
+
+    void push_back(const ClusterType &cluster) { m_data.push_back(cluster); }
+
+    ClusterVector &operator+=(const ClusterVector &other) {
+        m_data.insert(m_data.end(), other.begin(), other.end());
+
+        return *this;
     }
 
-  private:
-    void allocate_buffer(size_t new_capacity) {
-        size_t num_bytes = element_offset() * new_capacity;
-        std::byte *new_data = new std::byte[num_bytes]{};
-        std::copy(m_data, m_data + element_offset() * m_size, new_data);
-        delete[] m_data;
-        m_data = new_data;
-        m_capacity = new_capacity;
+    /**
+     * @brief Return the number of clusters in the vector
+     */
+    size_t size() const { return m_data.size(); }
+
+    uint8_t cluster_size_x() const { return ClusterSizeX; }
+
+    uint8_t cluster_size_y() const { return ClusterSizeY; }
+
+    /**
+     * @brief Return the capacity of the buffer in number of clusters. This is
+     * the number of clusters that can be stored in the current buffer without
+     * reallocation.
+     */
+    size_t capacity() const { return m_data.capacity(); }
+
+    auto begin() const { return m_data.begin(); }
+
+    auto end() const { return m_data.end(); }
+
+    /**
+     * @brief Return the size in bytes of a single cluster
+     */
+    size_t item_size() const {
+        return sizeof(ClusterType); // 2 * sizeof(CoordType) + ClusterSizeX *
+                                    // ClusterSizeY * sizeof(T);
+    }
+
+    ClusterType *data() { return m_data.data(); }
+    ClusterType const *data() const { return m_data.data(); }
+
+    /**
+     * @brief Return a reference to the i-th cluster casted to type V
+     * @tparam V type of the cluster
+     */
+    ClusterType &operator[](size_t i) { return m_data[i]; }
+
+    const ClusterType &operator[](size_t i) const { return m_data[i]; }
+
+    /**
+     * @brief Return the frame number of the clusters. 0 is used to indicate
+     * that the clusters come from many frames
+     */
+    int32_t frame_number() const { return m_frame_number; }
+
+    void set_frame_number(int32_t frame_number) {
+        m_frame_number = frame_number;
     }
 };
 

include/aare/File.hpp

@@ -36,6 +36,8 @@ class File {
     File(File &&other) noexcept;
     File& operator=(File &&other) noexcept;
     ~File() = default;
+
+    // void close();                             //!< close the file
       
     Frame read_frame();                       //!< read one frame from the file at the current position
     Frame read_frame(size_t frame_index);     //!< read one frame at the position given by frame number

include/aare/FilePtr.hpp

@@ -0,0 +1,30 @@
+#pragma once
+#include <cstdio>
+#include <filesystem>
+
+namespace aare {
+
+/**
+ * \brief RAII wrapper for FILE pointer
+ */
+class FilePtr {
+    FILE *fp_{nullptr};
+
+  public:
+    FilePtr() = default;
+    FilePtr(const std::filesystem::path& fname, const std::string& mode);
+    FilePtr(const FilePtr &) = delete;            // we don't want a copy
+    FilePtr &operator=(const FilePtr &) = delete; // since we handle a resource
+    FilePtr(FilePtr &&other);
+    FilePtr &operator=(FilePtr &&other);
+    FILE *get();
+    ssize_t tell();
+    void seek(ssize_t offset, int whence = SEEK_SET) {
+        if (fseek(fp_, offset, whence) != 0)
+            throw std::runtime_error("Error seeking in file");
+    }
+    std::string error_msg();
+    ~FilePtr();
+};
+
+} // namespace aare

include/aare/Fit.hpp

@@ -0,0 +1,115 @@
+#pragma once
+
+#include <cmath>
+#include <fmt/core.h>
+#include <vector>
+
+#include "aare/NDArray.hpp"
+
+namespace aare {
+
+namespace func {
+double gaus(const double x, const double *par);
+NDArray<double, 1> gaus(NDView<double, 1> x, NDView<double, 1> par);
+
+double pol1(const double x, const double *par);
+NDArray<double, 1> pol1(NDView<double, 1> x, NDView<double, 1> par);
+
+double scurve(const double x, const double *par);
+NDArray<double, 1> scurve(NDView<double, 1> x, NDView<double, 1> par);
+
+double scurve2(const double x, const double *par);
+NDArray<double, 1> scurve2(NDView<double, 1> x, NDView<double, 1> par);
+
+} // namespace func
+
+
+/**
+ * @brief Estimate the initial parameters for a Gaussian fit
+ */
+std::array<double, 3> gaus_init_par(const NDView<double, 1> x, const NDView<double, 1> y);
+
+std::array<double, 2> pol1_init_par(const NDView<double, 1> x, const NDView<double, 1> y);
+
+std::array<double, 6> scurve_init_par(const NDView<double, 1> x, const NDView<double, 1> y); 
+std::array<double, 6> scurve2_init_par(const NDView<double, 1> x, const NDView<double, 1> y);
+
+static constexpr int DEFAULT_NUM_THREADS = 4;
+
+/**
+ * @brief Fit a 1D Gaussian to data.
+ * @param data data to fit
+ * @param x x values
+ */
+NDArray<double, 1> fit_gaus(NDView<double, 1> x, NDView<double, 1> y);
+
+
+/**
+ * @brief Fit a 1D Gaussian to each pixel. Data layout [row, col, values]
+ * @param x x values
+ * @param y y values, layout [row, col, values]
+ * @param n_threads number of threads to use
+ */
+
+NDArray<double, 3> fit_gaus(NDView<double, 1> x, NDView<double, 3> y,
+                            int n_threads = DEFAULT_NUM_THREADS);
+
+
+
+
+/**
+ * @brief Fit a 1D Gaussian with error estimates
+ * @param x x values
+ * @param y y values, layout [row, col, values]
+ * @param y_err error in y, layout [row, col, values]
+ * @param par_out output parameters
+ * @param par_err_out output error parameters
+ */
+void fit_gaus(NDView<double, 1> x, NDView<double, 1> y, NDView<double, 1> y_err,
+              NDView<double, 1> par_out, NDView<double, 1> par_err_out,
+              double& chi2);
+
+/**
+ * @brief Fit a 1D Gaussian to each pixel with error estimates. Data layout
+ * [row, col, values]
+ * @param x x values
+ * @param y y values, layout [row, col, values]
+ * @param y_err error in y, layout [row, col, values]
+ * @param par_out output parameters, layout [row, col, values]
+ * @param par_err_out output parameter errors, layout [row, col, values]
+ * @param n_threads number of threads to use
+ */
+void fit_gaus(NDView<double, 1> x, NDView<double, 3> y, NDView<double, 3> y_err,
+              NDView<double, 3> par_out, NDView<double, 3> par_err_out, NDView<double, 2> chi2_out,
+              int n_threads = DEFAULT_NUM_THREADS
+              );
+
+NDArray<double, 1> fit_pol1(NDView<double, 1> x, NDView<double, 1> y);
+
+NDArray<double, 3> fit_pol1(NDView<double, 1> x, NDView<double, 3> y,
+                            int n_threads = DEFAULT_NUM_THREADS);
+
+void fit_pol1(NDView<double, 1> x, NDView<double, 1> y, NDView<double, 1> y_err,
+              NDView<double, 1> par_out, NDView<double, 1> par_err_out, double& chi2);
+
+// TODO! not sure we need to offer the different version in C++
+void fit_pol1(NDView<double, 1> x, NDView<double, 3> y, NDView<double, 3> y_err,
+              NDView<double, 3> par_out, NDView<double, 3> par_err_out,NDView<double, 2> chi2_out,
+              int n_threads = DEFAULT_NUM_THREADS);
+
+NDArray<double, 1> fit_scurve(NDView<double, 1> x, NDView<double, 1> y); 
+NDArray<double, 3> fit_scurve(NDView<double, 1> x, NDView<double, 3> y, int n_threads); 
+void fit_scurve(NDView<double, 1> x, NDView<double, 1> y, NDView<double, 1> y_err,
+              NDView<double, 1> par_out, NDView<double, 1> par_err_out, double& chi2); 
+void fit_scurve(NDView<double, 1> x, NDView<double, 3> y, NDView<double, 3> y_err,
+              NDView<double, 3> par_out, NDView<double, 3> par_err_out, NDView<double, 2> chi2_out,
+              int n_threads); 
+
+NDArray<double, 1> fit_scurve2(NDView<double, 1> x, NDView<double, 1> y); 
+NDArray<double, 3> fit_scurve2(NDView<double, 1> x, NDView<double, 3> y, int n_threads); 
+void fit_scurve2(NDView<double, 1> x, NDView<double, 1> y, NDView<double, 1> y_err,
+              NDView<double, 1> par_out, NDView<double, 1> par_err_out, double& chi2); 
+void fit_scurve2(NDView<double, 1> x, NDView<double, 3> y, NDView<double, 3> y_err,
+              NDView<double, 3> par_out, NDView<double, 3> par_err_out, NDView<double, 2> chi2_out,
+              int n_threads); 
+} // namespace aare

include/aare/Frame.hpp

@@ -107,8 +107,8 @@ class Frame {
      * @return NDView<T, 2> 
      */
     template <typename T> NDView<T, 2> view() {
-        std::array<int64_t, 2> shape = {static_cast<int64_t>(m_rows),
-                                        static_cast<int64_t>(m_cols)};
+        std::array<ssize_t, 2> shape = {static_cast<ssize_t>(m_rows),
+                                        static_cast<ssize_t>(m_cols)};
         T *data = reinterpret_cast<T *>(m_data);
         return NDView<T, 2>(data, shape);
     }

include/aare/GainMap.hpp

@@ -0,0 +1,68 @@
+/************************************************
+ * @file GainMap.hpp
+ * @short function to apply gain map of image size to a vector of clusters -
+ *note stored gainmap is inverted for efficient aaplication to images
+ ***********************************************/
+
+#pragma once
+#include "aare/Cluster.hpp"
+#include "aare/ClusterVector.hpp"
+#include "aare/NDArray.hpp"
+#include "aare/NDView.hpp"
+#include <memory>
+
+namespace aare {
+
+class InvertedGainMap {
+
+  public:
+    explicit InvertedGainMap(const NDArray<double, 2> &gain_map)
+        : m_gain_map(gain_map) {
+        for (auto &item : m_gain_map) {
+            item = 1.0 / item;
+        }
+    };
+
+    explicit InvertedGainMap(const NDView<double, 2> gain_map) {
+        m_gain_map = NDArray<double, 2>(gain_map);
+        for (auto &item : m_gain_map) {
+            item = 1.0 / item;
+        }
+    }
+
+    template <typename ClusterType,
+              typename = std::enable_if_t<is_cluster_v<ClusterType>>>
+    void apply_gain_map(ClusterVector<ClusterType> &clustervec) {
+        // in principle we need to know the size of the image for this lookup
+        size_t ClusterSizeX = clustervec.cluster_size_x();
+        size_t ClusterSizeY = clustervec.cluster_size_y();
+
+        using T = typename ClusterVector<ClusterType>::value_type;
+
+        int64_t index_cluster_center_x = ClusterSizeX / 2;
+        int64_t index_cluster_center_y = ClusterSizeY / 2;
+        for (size_t i = 0; i < clustervec.size(); i++) {
+            auto &cl = clustervec[i];
+
+            if (cl.x > 0 && cl.y > 0 && cl.x < m_gain_map.shape(1) - 1 &&
+                cl.y < m_gain_map.shape(0) - 1) {
+                for (size_t j = 0; j < ClusterSizeX * ClusterSizeY; j++) {
+                    size_t x = cl.x + j % ClusterSizeX - index_cluster_center_x;
+                    size_t y = cl.y + j / ClusterSizeX - index_cluster_center_y;
+                    cl.data[j] = static_cast<T>(
+                        static_cast<double>(cl.data[j]) *
+                        m_gain_map(
+                            y, x)); // cast after conversion to keep precision
+                }
+            } else {
+                // clear edge clusters
+                cl.data.fill(0);
+            }
+        }
+    }
+
+  private:
+    NDArray<double, 2> m_gain_map{};
+};
+
+} // end of namespace aare

include/aare/Interpolator.hpp

@@ -0,0 +1,130 @@
+#pragma once
+
+#include "aare/CalculateEta.hpp"
+#include "aare/Cluster.hpp"
+#include "aare/ClusterFile.hpp" //Cluster_3x3
+#include "aare/ClusterVector.hpp"
+#include "aare/NDArray.hpp"
+#include "aare/NDView.hpp"
+#include "aare/algorithm.hpp"
+
+namespace aare {
+
+struct Photon {
+    double x;
+    double y;
+    double energy;
+};
+
+class Interpolator {
+    NDArray<double, 3> m_ietax;
+    NDArray<double, 3> m_ietay;
+
+    NDArray<double, 1> m_etabinsx;
+    NDArray<double, 1> m_etabinsy;
+    NDArray<double, 1> m_energy_bins;
+
+  public:
+    Interpolator(NDView<double, 3> etacube, NDView<double, 1> xbins,
+                 NDView<double, 1> ybins, NDView<double, 1> ebins);
+    NDArray<double, 3> get_ietax() { return m_ietax; }
+    NDArray<double, 3> get_ietay() { return m_ietay; }
+
+    template <typename ClusterType,
+              typename Eanble = std::enable_if_t<is_cluster_v<ClusterType>>>
+    std::vector<Photon> interpolate(const ClusterVector<ClusterType> &clusters);
+};
+
+// TODO: generalize to support any clustertype!!! otherwise add std::enable_if_t
+// to only take Cluster2x2 and Cluster3x3
+template <typename ClusterType, typename Enable>
+std::vector<Photon>
+Interpolator::interpolate(const ClusterVector<ClusterType> &clusters) {
+    std::vector<Photon> photons;
+    photons.reserve(clusters.size());
+
+    if (clusters.cluster_size_x() == 3 || clusters.cluster_size_y() == 3) {
+        for (const ClusterType &cluster : clusters) {
+
+            auto eta = calculate_eta2(cluster);
+
+            Photon photon;
+            photon.x = cluster.x;
+            photon.y = cluster.y;
+            photon.energy = static_cast<decltype(photon.energy)>(eta.sum);
+
+            // auto ie = nearest_index(m_energy_bins, photon.energy)-1;
+            // auto ix = nearest_index(m_etabinsx, eta.x)-1;
+            // auto iy = nearest_index(m_etabinsy, eta.y)-1;
+            // Finding the index of the last element that is smaller
+            // should work fine as long as we have many bins
+            auto ie = last_smaller(m_energy_bins, photon.energy);
+            auto ix = last_smaller(m_etabinsx, eta.x);
+            auto iy = last_smaller(m_etabinsy, eta.y);
+
+            // fmt::print("ex: {}, ix: {}, iy: {}\n", ie, ix, iy);
+
+            double dX, dY;
+            // cBottomLeft = 0,
+            // cBottomRight = 1,
+            // cTopLeft = 2,
+            // cTopRight = 3
+            switch (static_cast<corner>(eta.c)) {
+            case corner::cTopLeft:
+                dX = -1.;
+                dY = 0;
+                break;
+            case corner::cTopRight:;
+                dX = 0;
+                dY = 0;
+                break;
+            case corner::cBottomLeft:
+                dX = -1.;
+                dY = -1.;
+                break;
+            case corner::cBottomRight:
+                dX = 0.;
+                dY = -1.;
+                break;
+            }
+            photon.x += m_ietax(ix, iy, ie) * 2 + dX;
+            photon.y += m_ietay(ix, iy, ie) * 2 + dY;
+            photons.push_back(photon);
+        }
+    } else if (clusters.cluster_size_x() == 2 ||
+               clusters.cluster_size_y() == 2) {
+        for (const ClusterType &cluster : clusters) {
+            auto eta = calculate_eta2(cluster);
+
+            Photon photon;
+            photon.x = cluster.x;
+            photon.y = cluster.y;
+            photon.energy = static_cast<decltype(photon.energy)>(eta.sum);
+
+            // Now do some actual interpolation.
+            // Find which energy bin the cluster is in
+            //  auto ie = nearest_index(m_energy_bins, photon.energy)-1;
+            //  auto ix = nearest_index(m_etabinsx, eta.x)-1;
+            //  auto iy = nearest_index(m_etabinsy, eta.y)-1;
+            // Finding the index of the last element that is smaller
+            // should work fine as long as we have many bins
+            auto ie = last_smaller(m_energy_bins, photon.energy);
+            auto ix = last_smaller(m_etabinsx, eta.x);
+            auto iy = last_smaller(m_etabinsy, eta.y);
+
+            photon.x += m_ietax(ix, iy, ie) *
+                        2; // eta goes between 0 and 1 but we could move the hit
+                           // anywhere in the 2x2
+            photon.y += m_ietay(ix, iy, ie) * 2;
+            photons.push_back(photon);
+        }
+
+    } else {
+        throw std::runtime_error(
+            "Only 3x3 and 2x2 clusters are supported for interpolation");
+    }
+
+    return photons;
+}
+
+} // namespace aare

include/aare/JungfrauDataFile.hpp

@@ -0,0 +1,106 @@
+#pragma once
+#include <cstdint>
+#include <filesystem>
+#include <vector>
+
+#include "aare/FilePtr.hpp"
+#include "aare/defs.hpp"
+#include "aare/NDArray.hpp"
+#include "aare/FileInterface.hpp"
+namespace aare {
+
+
+struct JungfrauDataHeader{
+    uint64_t framenum;
+    uint64_t bunchid;
+};
+
+class JungfrauDataFile : public FileInterface {
+
+    size_t m_rows{};     //!< number of rows in the image, from find_frame_size();
+    size_t m_cols{};     //!< number of columns in the image, from find_frame_size();
+    size_t m_bytes_per_frame{}; //!< number of bytes per frame excluding header
+    size_t m_total_frames{};    //!< total number of frames in the series of files
+    size_t m_offset{}; //!< file index of the first file, allow starting at non zero file
+    size_t m_current_file_index{};   //!< The index of the open file
+    size_t m_current_frame_index{};  //!< The index of the current frame (with reference to all files)
+
+    std::vector<size_t> m_last_frame_in_file{}; //!< Used for seeking to the correct file
+    std::filesystem::path m_path; //!< path to the files
+    std::string m_base_name;      //!< base name used for formatting file names
+
+    FilePtr m_fp; //!< RAII wrapper for a FILE* 
+
+    
+    using pixel_type = uint16_t;
+    static constexpr size_t header_size = sizeof(JungfrauDataHeader); 
+    static constexpr size_t n_digits_in_file_index = 6; //!< to format file names
+
+  public:
+    JungfrauDataFile(const std::filesystem::path &fname);
+
+    std::string base_name() const;      //!< get the base name of the file (without path and extension)
+    size_t bytes_per_frame() override;           
+    size_t pixels_per_frame() override;    
+    size_t bytes_per_pixel() const;  
+    size_t bitdepth() const override;
+    void seek(size_t frame_index) override;  //!< seek to the given frame index (note not byte offset)
+    size_t tell() override;            //!< get the frame index of the file pointer
+    size_t total_frames() const override;
+    size_t rows() const override;
+    size_t cols() const override;
+    std::array<ssize_t,2> shape() const;
+    size_t n_files() const; //!< get the number of files in the series. 
+
+    // Extra functions needed for FileInterface
+    Frame read_frame() override;
+    Frame read_frame(size_t frame_number) override;
+    std::vector<Frame> read_n(size_t n_frames=0) override;
+    void read_into(std::byte *image_buf) override;
+    void read_into(std::byte *image_buf, size_t n_frames) override;
+    size_t frame_number(size_t frame_index) override;
+    DetectorType detector_type() const override;
+
+    /**
+     * @brief Read a single frame from the file into the given buffer.  
+     * @param image_buf buffer to read the frame into. (Note the caller is responsible for allocating the buffer)
+     * @param header pointer to a JungfrauDataHeader or nullptr to skip header)
+     */
+    void read_into(std::byte *image_buf, JungfrauDataHeader *header = nullptr);
+
+    /**
+     * @brief Read a multiple frames from the file into the given buffer.  
+     * @param image_buf buffer to read the frame into. (Note the caller is responsible for allocating the buffer)
+     * @param n_frames number of frames to read
+     * @param header pointer to a JungfrauDataHeader or nullptr to skip header)
+     */
+    void read_into(std::byte *image_buf, size_t n_frames, JungfrauDataHeader *header = nullptr);
+    
+    /** 
+     * @brief Read a single frame from the file into the given NDArray
+     * @param image NDArray to read the frame into.
+     */
+    void read_into(NDArray<uint16_t>* image, JungfrauDataHeader* header = nullptr);
+
+    JungfrauDataHeader read_header();
+    std::filesystem::path current_file() const { return fpath(m_current_file_index+m_offset); }
+
+    
+    private:
+    /**
+     * @brief Find the size of the frame in the file. (256x256, 256x1024, 512x1024)
+     * @param fname path to the file
+     * @throws std::runtime_error if the file is empty or the size cannot be determined
+     */
+     void find_frame_size(const std::filesystem::path &fname);
+
+
+     void parse_fname(const std::filesystem::path &fname);
+     void scan_files();
+     void open_file(size_t file_index);
+     std::filesystem::path fpath(size_t frame_index) const;
+
+
+  };
+
+} // namespace aare

include/aare/NDArray.hpp

@@ -22,10 +22,10 @@ TODO! Add expression templates for operators
 namespace aare {
 
 
-template <typename T, int64_t Ndim = 2>
+template <typename T, ssize_t Ndim = 2>
 class NDArray : public ArrayExpr<NDArray<T, Ndim>, Ndim> {
-    std::array<int64_t, Ndim> shape_;
-    std::array<int64_t, Ndim> strides_;
+    std::array<ssize_t, Ndim> shape_;
+    std::array<ssize_t, Ndim> strides_;
     size_t size_{};
     T *data_;
 
@@ -42,7 +42,7 @@ class NDArray : public ArrayExpr<NDArray<T, Ndim>, Ndim> {
      *
      * @param shape shape of the new NDArray
      */
-    explicit NDArray(std::array<int64_t, Ndim> shape)
+    explicit NDArray(std::array<ssize_t, Ndim> shape)
         : shape_(shape), strides_(c_strides<Ndim>(shape_)),
           size_(std::accumulate(shape_.begin(), shape_.end(), 1,
                                 std::multiplies<>())),
@@ -55,7 +55,7 @@ class NDArray : public ArrayExpr<NDArray<T, Ndim>, Ndim> {
      * @param shape shape of the new array
      * @param value value to initialize the array with
      */
-    NDArray(std::array<int64_t, Ndim> shape, T value) : NDArray(shape) {
+    NDArray(std::array<ssize_t, Ndim> shape, T value) : NDArray(shape) {
         this->operator=(value);
     }
 
@@ -69,6 +69,11 @@ class NDArray : public ArrayExpr<NDArray<T, Ndim>, Ndim> {
         std::copy(v.begin(), v.end(), begin());
     }
 
+    template<size_t Size>
+    NDArray(const std::array<T, Size>& arr) : NDArray<T,1>({Size}) {
+        std::copy(arr.begin(), arr.end(), begin());
+    }
+
     // Move constructor
     NDArray(NDArray &&other) noexcept
         : shape_(other.shape_), strides_(c_strides<Ndim>(shape_)),
@@ -97,6 +102,9 @@ class NDArray : public ArrayExpr<NDArray<T, Ndim>, Ndim> {
     auto begin() { return data_; }
     auto end() { return data_ + size_; }
 
+    auto begin() const { return data_; }
+    auto end() const { return data_ + size_; }
+
     using value_type = T;
 
     NDArray &operator=(NDArray &&other) noexcept; // Move assign
@@ -105,6 +113,20 @@ class NDArray : public ArrayExpr<NDArray<T, Ndim>, Ndim> {
     NDArray &operator-=(const NDArray &other);
     NDArray &operator*=(const NDArray &other);
 
+    //Write directly to the data array, or create a new one
+    template<size_t Size>
+    NDArray<T,1>& operator=(const std::array<T,Size> &other){
+        if(Size != size_){
+            delete[] data_;
+            size_ = Size;
+            data_ = new T[size_];
+        }
+        for (size_t i = 0; i < Size; ++i) {
+            data_[i] = other[i];
+        }
+        return *this;
+    }
+
     // NDArray& operator/=(const NDArray& other);
 
     template <typename V> NDArray &operator/=(const NDArray<V, Ndim> &other) {
@@ -135,6 +157,11 @@ class NDArray : public ArrayExpr<NDArray<T, Ndim>, Ndim> {
 
     NDArray &operator&=(const T & /*mask*/);
 
+  
+
+
+    
+
     void sqrt() {
         for (int i = 0; i < size_; ++i) {
             data_[i] = std::sqrt(data_[i]);
@@ -159,22 +186,22 @@ class NDArray : public ArrayExpr<NDArray<T, Ndim>, Ndim> {
     }
 
     // TODO! is int the right type for index?
-    T &operator()(int64_t i) { return data_[i]; }
-    const T &operator()(int64_t i) const { return data_[i]; }
+    T &operator()(ssize_t i) { return data_[i]; }
+    const T &operator()(ssize_t i) const { return data_[i]; }
 
-    T &operator[](int64_t i) { return data_[i]; }
-    const T &operator[](int64_t i) const { return data_[i]; }
+    T &operator[](ssize_t i) { return data_[i]; }
+    const T &operator[](ssize_t i) const { return data_[i]; }
 
     T *data() { return data_; }
     std::byte *buffer() { return reinterpret_cast<std::byte *>(data_); }
-    size_t size() const { return size_; }
+    ssize_t size() const { return static_cast<ssize_t>(size_); }
     size_t total_bytes() const { return size_ * sizeof(T); }
-    std::array<int64_t, Ndim> shape() const noexcept { return shape_; }
-    int64_t shape(int64_t i) const noexcept { return shape_[i]; }
-    std::array<int64_t, Ndim> strides() const noexcept { return strides_; }
+    std::array<ssize_t, Ndim> shape() const noexcept { return shape_; }
+    ssize_t shape(ssize_t i) const noexcept { return shape_[i]; }
+    std::array<ssize_t, Ndim> strides() const noexcept { return strides_; }
     size_t bitdepth() const noexcept { return sizeof(T) * 8; }
 
-    std::array<int64_t, Ndim> byte_strides() const noexcept {
+    std::array<ssize_t, Ndim> byte_strides() const noexcept {
         auto byte_strides = strides_;
         for (auto &val : byte_strides)
             val *= sizeof(T);
@@ -201,7 +228,7 @@ class NDArray : public ArrayExpr<NDArray<T, Ndim>, Ndim> {
 };
 
 // Move assign
-template <typename T, int64_t Ndim>
+template <typename T, ssize_t Ndim>
 NDArray<T, Ndim> &
 NDArray<T, Ndim>::operator=(NDArray<T, Ndim> &&other) noexcept {
     if (this != &other) {
@@ -215,7 +242,7 @@ NDArray<T, Ndim>::operator=(NDArray<T, Ndim> &&other) noexcept {
     return *this;
 }
 
-template <typename T, int64_t Ndim>
+template <typename T, ssize_t Ndim>
 NDArray<T, Ndim> &NDArray<T, Ndim>::operator+=(const NDArray<T, Ndim> &other) {
     // check shape
     if (shape_ == other.shape_) {
@@ -227,7 +254,7 @@ NDArray<T, Ndim> &NDArray<T, Ndim>::operator+=(const NDArray<T, Ndim> &other) {
     throw(std::runtime_error("Shape of ImageDatas must match"));
 }
 
-template <typename T, int64_t Ndim>
+template <typename T, ssize_t Ndim>
 NDArray<T, Ndim> &NDArray<T, Ndim>::operator-=(const NDArray<T, Ndim> &other) {
     // check shape
     if (shape_ == other.shape_) {
@@ -239,7 +266,7 @@ NDArray<T, Ndim> &NDArray<T, Ndim>::operator-=(const NDArray<T, Ndim> &other) {
     throw(std::runtime_error("Shape of ImageDatas must match"));
 }
 
-template <typename T, int64_t Ndim>
+template <typename T, ssize_t Ndim>
 NDArray<T, Ndim> &NDArray<T, Ndim>::operator*=(const NDArray<T, Ndim> &other) {
     // check shape
     if (shape_ == other.shape_) {
@@ -251,14 +278,14 @@ NDArray<T, Ndim> &NDArray<T, Ndim>::operator*=(const NDArray<T, Ndim> &other) {
     throw(std::runtime_error("Shape of ImageDatas must match"));
 }
 
-template <typename T, int64_t Ndim>
+template <typename T, ssize_t Ndim>
 NDArray<T, Ndim> &NDArray<T, Ndim>::operator&=(const T &mask) {
     for (auto it = begin(); it != end(); ++it)
         *it &= mask;
     return *this;
 }
 
-template <typename T, int64_t Ndim>
+template <typename T, ssize_t Ndim>
 NDArray<bool, Ndim> NDArray<T, Ndim>::operator>(const NDArray &other) {
     if (shape_ == other.shape_) {
         NDArray<bool, Ndim> result{shape_};
@@ -270,7 +297,7 @@ NDArray<bool, Ndim> NDArray<T, Ndim>::operator>(const NDArray &other) {
     throw(std::runtime_error("Shape of ImageDatas must match"));
 }
 
-template <typename T, int64_t Ndim>
+template <typename T, ssize_t Ndim>
 NDArray<T, Ndim> &NDArray<T, Ndim>::operator=(const NDArray<T, Ndim> &other) {
     if (this != &other) {
         delete[] data_;
@@ -283,7 +310,7 @@ NDArray<T, Ndim> &NDArray<T, Ndim>::operator=(const NDArray<T, Ndim> &other) {
     return *this;
 }
 
-template <typename T, int64_t Ndim>
+template <typename T, ssize_t Ndim>
 bool NDArray<T, Ndim>::operator==(const NDArray<T, Ndim> &other) const {
     if (shape_ != other.shape_)
         return false;
@@ -295,80 +322,83 @@ bool NDArray<T, Ndim>::operator==(const NDArray<T, Ndim> &other) const {
     return true;
 }
 
-template <typename T, int64_t Ndim>
+template <typename T, ssize_t Ndim>
 bool NDArray<T, Ndim>::operator!=(const NDArray<T, Ndim> &other) const {
     return !((*this) == other);
 }
-template <typename T, int64_t Ndim>
+template <typename T, ssize_t Ndim>
 NDArray<T, Ndim> &NDArray<T, Ndim>::operator++() {
     for (uint32_t i = 0; i < size_; ++i)
         data_[i] += 1;
     return *this;
 }
-template <typename T, int64_t Ndim>
+template <typename T, ssize_t Ndim>
 NDArray<T, Ndim> &NDArray<T, Ndim>::operator=(const T &value) {
     std::fill_n(data_, size_, value);
     return *this;
 }
 
-template <typename T, int64_t Ndim>
+template <typename T, ssize_t Ndim>
 NDArray<T, Ndim> &NDArray<T, Ndim>::operator+=(const T &value) {
     for (uint32_t i = 0; i < size_; ++i)
         data_[i] += value;
     return *this;
 }
 
-template <typename T, int64_t Ndim>
+
+
+
+template <typename T, ssize_t Ndim>
 NDArray<T, Ndim> NDArray<T, Ndim>::operator+(const T &value) {
     NDArray result = *this;
     result += value;
     return result;
 }
-template <typename T, int64_t Ndim>
+template <typename T, ssize_t Ndim>
 NDArray<T, Ndim> &NDArray<T, Ndim>::operator-=(const T &value) {
     for (uint32_t i = 0; i < size_; ++i)
         data_[i] -= value;
     return *this;
 }
-template <typename T, int64_t Ndim>
+template <typename T, ssize_t Ndim>
 NDArray<T, Ndim> NDArray<T, Ndim>::operator-(const T &value) {
     NDArray result = *this;
     result -= value;
     return result;
 }
 
-template <typename T, int64_t Ndim>
+template <typename T, ssize_t Ndim>
 NDArray<T, Ndim> &NDArray<T, Ndim>::operator/=(const T &value) {
     for (uint32_t i = 0; i < size_; ++i)
         data_[i] /= value;
     return *this;
 }
-template <typename T, int64_t Ndim>
+template <typename T, ssize_t Ndim>
 NDArray<T, Ndim> NDArray<T, Ndim>::operator/(const T &value) {
     NDArray result = *this;
     result /= value;
     return result;
 }
-template <typename T, int64_t Ndim>
+template <typename T, ssize_t Ndim>
 NDArray<T, Ndim> &NDArray<T, Ndim>::operator*=(const T &value) {
     for (uint32_t i = 0; i < size_; ++i)
         data_[i] *= value;
     return *this;
 }
-template <typename T, int64_t Ndim>
+template <typename T, ssize_t Ndim>
 NDArray<T, Ndim> NDArray<T, Ndim>::operator*(const T &value) {
     NDArray result = *this;
     result *= value;
     return result;
 }
-template <typename T, int64_t Ndim> void NDArray<T, Ndim>::Print() {
-    if (shape_[0] < 20 && shape_[1] < 20)
-        Print_all();
-    else
-        Print_some();
-}
+// template <typename T, ssize_t Ndim> void NDArray<T, Ndim>::Print() {
+//     if (shape_[0] < 20 && shape_[1] < 20)
+//         Print_all();
+//     else
+//         Print_some();
+// }
 
-template <typename T, int64_t Ndim>
+template <typename T, ssize_t Ndim>
 std::ostream &operator<<(std::ostream &os, const NDArray<T, Ndim> &arr) {
     for (auto row = 0; row < arr.shape(0); ++row) {
         for (auto col = 0; col < arr.shape(1); ++col) {
@@ -380,7 +410,7 @@ std::ostream &operator<<(std::ostream &os, const NDArray<T, Ndim> &arr) {
     return os;
 }
 
-template <typename T, int64_t Ndim> void NDArray<T, Ndim>::Print_all() {
+template <typename T, ssize_t Ndim> void NDArray<T, Ndim>::Print_all() {
     for (auto row = 0; row < shape_[0]; ++row) {
         for (auto col = 0; col < shape_[1]; ++col) {
             std::cout << std::setw(3);
@@ -389,7 +419,7 @@ template <typename T, int64_t Ndim> void NDArray<T, Ndim>::Print_all() {
         std::cout << "\n";
     }
 }
-template <typename T, int64_t Ndim> void NDArray<T, Ndim>::Print_some() {
+template <typename T, ssize_t Ndim> void NDArray<T, Ndim>::Print_some() {
     for (auto row = 0; row < 5; ++row) {
         for (auto col = 0; col < 5; ++col) {
             std::cout << std::setw(7);
@@ -399,7 +429,7 @@ template <typename T, int64_t Ndim> void NDArray<T, Ndim>::Print_some() {
     }
 }
 
-template <typename T, int64_t Ndim>
+template <typename T, ssize_t Ndim>
 void save(NDArray<T, Ndim> &img, std::string &pathname) {
     std::ofstream f;
     f.open(pathname, std::ios::binary);
@@ -407,9 +437,9 @@ void save(NDArray<T, Ndim> &img, std::string &pathname) {
     f.close();
 }
 
-template <typename T, int64_t Ndim>
+template <typename T, ssize_t Ndim>
 NDArray<T, Ndim> load(const std::string &pathname,
-                      std::array<int64_t, Ndim> shape) {
+                      std::array<ssize_t, Ndim> shape) {
     NDArray<T, Ndim> img{shape};
     std::ifstream f;
     f.open(pathname, std::ios::binary);
@@ -418,4 +448,6 @@ NDArray<T, Ndim> load(const std::string &pathname,
     return img;
 }
 
+
+
 } // namespace aare

include/aare/NDView.hpp

@@ -1,5 +1,5 @@
 #pragma once
-
+#include "aare/defs.hpp"
 #include "aare/ArrayExpr.hpp"
 
 #include <algorithm>
@@ -14,10 +14,10 @@
 #include <vector>
 namespace aare {
 
-template <int64_t Ndim> using Shape = std::array<int64_t, Ndim>;
+template <ssize_t Ndim> using Shape = std::array<ssize_t, Ndim>;
 
 // TODO! fix mismatch between signed and unsigned
-template <int64_t Ndim> Shape<Ndim> make_shape(const std::vector<size_t> &shape) {
+template <ssize_t Ndim> Shape<Ndim> make_shape(const std::vector<size_t> &shape) {
     if (shape.size() != Ndim)
         throw std::runtime_error("Shape size mismatch");
     Shape<Ndim> arr;
@@ -25,41 +25,41 @@ template <int64_t Ndim> Shape<Ndim> make_shape(const std::vector<size_t> &shape)
     return arr;
 }
 
-template <int64_t Dim = 0, typename Strides> int64_t element_offset(const Strides & /*unused*/) { return 0; }
+template <ssize_t Dim = 0, typename Strides> ssize_t element_offset(const Strides & /*unused*/) { return 0; }
 
-template <int64_t Dim = 0, typename Strides, typename... Ix>
-int64_t element_offset(const Strides &strides, int64_t i, Ix... index) {
+template <ssize_t Dim = 0, typename Strides, typename... Ix>
+ssize_t element_offset(const Strides &strides, ssize_t i, Ix... index) {
     return i * strides[Dim] + element_offset<Dim + 1>(strides, index...);
 }
 
-template <int64_t Ndim> std::array<int64_t, Ndim> c_strides(const std::array<int64_t, Ndim> &shape) {
-    std::array<int64_t, Ndim> strides{};
+template <ssize_t Ndim> std::array<ssize_t, Ndim> c_strides(const std::array<ssize_t, Ndim> &shape) {
+    std::array<ssize_t, Ndim> strides{};
     std::fill(strides.begin(), strides.end(), 1);
-    for (int64_t i = Ndim - 1; i > 0; --i) {
+    for (ssize_t i = Ndim - 1; i > 0; --i) {
         strides[i - 1] = strides[i] * shape[i];
     }
     return strides;
 }
 
-template <int64_t Ndim> std::array<int64_t, Ndim> make_array(const std::vector<int64_t> &vec) {
+template <ssize_t Ndim> std::array<ssize_t, Ndim> make_array(const std::vector<ssize_t> &vec) {
     assert(vec.size() == Ndim);
-    std::array<int64_t, Ndim> arr{};
+    std::array<ssize_t, Ndim> arr{};
     std::copy_n(vec.begin(), Ndim, arr.begin());
     return arr;
 }
 
-template <typename T, int64_t Ndim = 2> class NDView : public ArrayExpr<NDView<T, Ndim>, Ndim> {
+template <typename T, ssize_t Ndim = 2> class NDView : public ArrayExpr<NDView<T, Ndim>, Ndim> {
   public:
     NDView() = default;
     ~NDView() = default;
     NDView(const NDView &) = default;
     NDView(NDView &&) = default;
 
-    NDView(T *buffer, std::array<int64_t, Ndim> shape)
+    NDView(T *buffer, std::array<ssize_t, Ndim> shape)
         : buffer_(buffer), strides_(c_strides<Ndim>(shape)), shape_(shape),
           size_(std::accumulate(std::begin(shape), std::end(shape), 1, std::multiplies<>())) {}
 
-    // NDView(T *buffer, const std::vector<int64_t> &shape)
+    // NDView(T *buffer, const std::vector<ssize_t> &shape)
     //     : buffer_(buffer), strides_(c_strides<Ndim>(make_array<Ndim>(shape))), shape_(make_array<Ndim>(shape)),
     //       size_(std::accumulate(std::begin(shape), std::end(shape), 1, std::multiplies<>())) {}
 
@@ -71,16 +71,16 @@ template <typename T, int64_t Ndim = 2> class NDView : public ArrayExpr<NDView<T
         return buffer_[element_offset(strides_, index...)];
     }
 
-    size_t size() const { return size_; }
+    ssize_t size() const { return static_cast<ssize_t>(size_); }
     size_t total_bytes() const { return size_ * sizeof(T); }
-    std::array<int64_t, Ndim> strides() const noexcept { return strides_; }
+    std::array<ssize_t, Ndim> strides() const noexcept { return strides_; }
 
     T *begin() { return buffer_; }
     T *end() { return buffer_ + size_; }
     T const *begin() const { return buffer_; }
     T const *end() const { return buffer_ + size_; }
-    T &operator()(int64_t i) const { return buffer_[i]; }
-    T &operator[](int64_t i) const { return buffer_[i]; }
+    T &operator()(ssize_t i) const { return buffer_[i]; }
+    T &operator[](ssize_t i) const { return buffer_[i]; }
 
     bool operator==(const NDView &other) const {
         if (size_ != other.size_)
@@ -99,6 +99,15 @@ template <typename T, int64_t Ndim = 2> class NDView : public ArrayExpr<NDView<T
 
     NDView &operator/=(const NDView &other) { return elemenwise(other, std::divides<T>()); }
 
+
+    template<size_t Size>
+    NDView& operator=(const std::array<T, Size> &arr) {
+        if(size() != static_cast<ssize_t>(arr.size()))
+            throw std::runtime_error(LOCATION + "Array and NDView size mismatch");
+        std::copy(arr.begin(), arr.end(), begin());
+        return *this;
+    }
+
     NDView &operator=(const T val) {
         for (auto it = begin(); it != end(); ++it)
             *it = val;
@@ -127,15 +136,15 @@ template <typename T, int64_t Ndim = 2> class NDView : public ArrayExpr<NDView<T
     }
 
     auto &shape() const { return shape_; }
-    auto shape(int64_t i) const { return shape_[i]; }
+    auto shape(ssize_t i) const { return shape_[i]; }
 
     T *data() { return buffer_; }
     void print_all() const;
 
   private:
     T *buffer_{nullptr};
-    std::array<int64_t, Ndim> strides_{};
-    std::array<int64_t, Ndim> shape_{};
+    std::array<ssize_t, Ndim> strides_{};
+    std::array<ssize_t, Ndim> shape_{};
     uint64_t size_{};
 
     template <class BinaryOperation> NDView &elemenwise(T val, BinaryOperation op) {
@@ -151,7 +160,7 @@ template <typename T, int64_t Ndim = 2> class NDView : public ArrayExpr<NDView<T
         return *this;
     }
 };
-template <typename T, int64_t Ndim> void NDView<T, Ndim>::print_all() const {
+template <typename T, ssize_t Ndim> void NDView<T, Ndim>::print_all() const {
     for (auto row = 0; row < shape_[0]; ++row) {
         for (auto col = 0; col < shape_[1]; ++col) {
             std::cout << std::setw(3);
@@ -162,7 +171,7 @@ template <typename T, int64_t Ndim> void NDView<T, Ndim>::print_all() const {
 }
 
 
-template <typename T, int64_t Ndim>
+template <typename T, ssize_t Ndim>
 std::ostream& operator <<(std::ostream& os, const NDView<T, Ndim>& arr){
     for (auto row = 0; row < arr.shape(0); ++row) {
         for (auto col = 0; col < arr.shape(1); ++col) {
@@ -175,4 +184,9 @@ std::ostream& operator <<(std::ostream& os, const NDView<T, Ndim>& arr){
 }
 
 
+template <typename T>
+NDView<T,1> make_view(std::vector<T>& vec){
+    return NDView<T,1>(vec.data(), {static_cast<ssize_t>(vec.size())});
+}
+
 } // namespace aare

include/aare/NumpyFile.hpp

@@ -69,7 +69,7 @@ class NumpyFile : public FileInterface {
      */
     template <typename T, size_t NDim> NDArray<T, NDim> load() {
         NDArray<T, NDim> arr(make_shape<NDim>(m_header.shape));
-        if (fseek(fp, static_cast<int64_t>(header_size), SEEK_SET)) {
+        if (fseek(fp, static_cast<long>(header_size), SEEK_SET)) {
             throw std::runtime_error(LOCATION + "Error seeking to the start of the data");
         }
         size_t rc = fread(arr.data(), sizeof(T), arr.size(), fp);

include/aare/Pedestal.hpp

@@ -89,6 +89,7 @@ template <typename SUM_TYPE = double> class Pedestal {
         m_sum = 0;
         m_sum2 = 0;
         m_cur_samples = 0;
+        m_mean = 0;
     }
 
     
@@ -97,6 +98,7 @@ template <typename SUM_TYPE = double> class Pedestal {
         m_sum(row, col) = 0;
         m_sum2(row, col) = 0;
         m_cur_samples(row, col) = 0;
+        m_mean(row, col) = 0;
     }
     
 
@@ -105,7 +107,7 @@ template <typename SUM_TYPE = double> class Pedestal {
         assert(frame.size() == m_rows * m_cols);
 
         // TODO! move away from m_rows, m_cols
-        if (frame.shape() != std::array<int64_t, 2>{m_rows, m_cols}) {
+        if (frame.shape() != std::array<ssize_t, 2>{m_rows, m_cols}) {
             throw std::runtime_error(
                 "Frame shape does not match pedestal shape");
         }
@@ -119,14 +121,14 @@ template <typename SUM_TYPE = double> class Pedestal {
 
     /**
      * Push but don't update the cached mean. Speeds up the process
-     * when intitializing the pedestal.
+     * when initializing the pedestal.
      * 
      */
     template <typename T> void push_no_update(NDView<T, 2> frame) {
         assert(frame.size() == m_rows * m_cols);
 
         // TODO! move away from m_rows, m_cols
-        if (frame.shape() != std::array<int64_t, 2>{m_rows, m_cols}) {
+        if (frame.shape() != std::array<ssize_t, 2>{m_rows, m_cols}) {
             throw std::runtime_error(
                 "Frame shape does not match pedestal shape");
         }
@@ -165,8 +167,8 @@ template <typename SUM_TYPE = double> class Pedestal {
             m_sum2(row, col) += val * val;
             m_cur_samples(row, col)++;
         } else {
-            m_sum(row, col) += val - m_sum(row, col) / m_cur_samples(row, col);
-            m_sum2(row, col) += val * val - m_sum2(row, col) / m_cur_samples(row, col);
+            m_sum(row, col) += val - m_sum(row, col) / m_samples;
+            m_sum2(row, col) += val * val - m_sum2(row, col) / m_samples;
         }
         //Since we just did a push we know that m_cur_samples(row, col) is at least 1
         m_mean(row, col) = m_sum(row, col) / m_cur_samples(row, col);

include/aare/ProducerConsumerQueue.hpp

@@ -0,0 +1,203 @@
+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// @author Bo Hu (bhu@fb.com)
+// @author Jordan DeLong (delong.j@fb.com)
+
+// Changes made by PSD Detector Group:
+// Copied: Line 34 constexpr std::size_t hardware_destructive_interference_size = 128; from folly/lang/Align.h
+// Changed extension to .hpp
+// Changed namespace to aare
+
+#pragma once
+
+#include <atomic>
+#include <cassert>
+#include <cstdlib>
+#include <memory>
+#include <stdexcept>
+#include <type_traits>
+#include <utility>
+
+constexpr std::size_t hardware_destructive_interference_size = 128;
+namespace aare {
+
+/*
+ * ProducerConsumerQueue is a one producer and one consumer queue
+ * without locks.
+ */
+template <class T> struct ProducerConsumerQueue {
+    typedef T value_type;
+
+    ProducerConsumerQueue(const ProducerConsumerQueue &) = delete;
+    ProducerConsumerQueue &operator=(const ProducerConsumerQueue &) = delete;
+
+    
+    ProducerConsumerQueue(ProducerConsumerQueue &&other){
+        size_ = other.size_;
+        records_ = other.records_;
+        other.records_ = nullptr;
+        readIndex_ = other.readIndex_.load(std::memory_order_acquire);
+        writeIndex_ = other.writeIndex_.load(std::memory_order_acquire);
+    }
+    ProducerConsumerQueue &operator=(ProducerConsumerQueue &&other){
+        size_ = other.size_;
+        records_ = other.records_;
+        other.records_ = nullptr;
+        readIndex_ = other.readIndex_.load(std::memory_order_acquire);
+        writeIndex_ = other.writeIndex_.load(std::memory_order_acquire);
+        return *this;
+    }
+    
+    
+    ProducerConsumerQueue():ProducerConsumerQueue(2){};
+    // size must be >= 2.
+    //
+    // Also, note that the number of usable slots in the queue at any
+    // given time is actually (size-1), so if you start with an empty queue,
+    // isFull() will return true after size-1 insertions.
+    explicit ProducerConsumerQueue(uint32_t size)
+        : size_(size), records_(static_cast<T *>(std::malloc(sizeof(T) * size))), readIndex_(0), writeIndex_(0) {
+        assert(size >= 2);
+        if (!records_) {
+            throw std::bad_alloc();
+        }
+    }
+
+    ~ProducerConsumerQueue() {
+        // We need to destruct anything that may still exist in our queue.
+        // (No real synchronization needed at destructor time: only one
+        // thread can be doing this.)
+        if (!std::is_trivially_destructible<T>::value) {
+            size_t readIndex = readIndex_;
+            size_t endIndex = writeIndex_;
+            while (readIndex != endIndex) {
+                records_[readIndex].~T();
+                if (++readIndex == size_) {
+                    readIndex = 0;
+                }
+            }
+        }
+
+        std::free(records_);
+    }
+
+    template <class... Args> bool write(Args &&...recordArgs) {
+        auto const currentWrite = writeIndex_.load(std::memory_order_relaxed);
+        auto nextRecord = currentWrite + 1;
+        if (nextRecord == size_) {
+            nextRecord = 0;
+        }
+        if (nextRecord != readIndex_.load(std::memory_order_acquire)) {
+            new (&records_[currentWrite]) T(std::forward<Args>(recordArgs)...);
+            writeIndex_.store(nextRecord, std::memory_order_release);
+            return true;
+        }
+
+        // queue is full
+        return false;
+    }
+
+    // move (or copy) the value at the front of the queue to given variable
+    bool read(T &record) {
+        auto const currentRead = readIndex_.load(std::memory_order_relaxed);
+        if (currentRead == writeIndex_.load(std::memory_order_acquire)) {
+            // queue is empty
+            return false;
+        }
+
+        auto nextRecord = currentRead + 1;
+        if (nextRecord == size_) {
+            nextRecord = 0;
+        }
+        record = std::move(records_[currentRead]);
+        records_[currentRead].~T();
+        readIndex_.store(nextRecord, std::memory_order_release);
+        return true;
+    }
+
+    // pointer to the value at the front of the queue (for use in-place) or
+    // nullptr if empty.
+    T *frontPtr() {
+        auto const currentRead = readIndex_.load(std::memory_order_relaxed);
+        if (currentRead == writeIndex_.load(std::memory_order_acquire)) {
+            // queue is empty
+            return nullptr;
+        }
+        return &records_[currentRead];
+    }
+
+    // queue must not be empty
+    void popFront() {
+        auto const currentRead = readIndex_.load(std::memory_order_relaxed);
+        assert(currentRead != writeIndex_.load(std::memory_order_acquire));
+
+        auto nextRecord = currentRead + 1;
+        if (nextRecord == size_) {
+            nextRecord = 0;
+        }
+        records_[currentRead].~T();
+        readIndex_.store(nextRecord, std::memory_order_release);
+    }
+
+    bool isEmpty() const {
+        return readIndex_.load(std::memory_order_acquire) == writeIndex_.load(std::memory_order_acquire);
+    }
+
+    bool isFull() const {
+        auto nextRecord = writeIndex_.load(std::memory_order_acquire) + 1;
+        if (nextRecord == size_) {
+            nextRecord = 0;
+        }
+        if (nextRecord != readIndex_.load(std::memory_order_acquire)) {
+            return false;
+        }
+        // queue is full
+        return true;
+    }
+
+    // * If called by consumer, then true size may be more (because producer may
+    //   be adding items concurrently).
+    // * If called by producer, then true size may be less (because consumer may
+    //   be removing items concurrently).
+    // * It is undefined to call this from any other thread.
+    size_t sizeGuess() const {
+        int ret = writeIndex_.load(std::memory_order_acquire) - readIndex_.load(std::memory_order_acquire);
+        if (ret < 0) {
+            ret += size_;
+        }
+        return ret;
+    }
+
+    // maximum number of items in the queue.
+    size_t capacity() const { return size_ - 1; }
+
+  private:
+    using AtomicIndex = std::atomic<unsigned int>;
+
+    char pad0_[hardware_destructive_interference_size];
+    // const uint32_t size_;
+    uint32_t size_;
+    // T *const records_;
+    T* records_;
+
+    alignas(hardware_destructive_interference_size) AtomicIndex readIndex_;
+    alignas(hardware_destructive_interference_size) AtomicIndex writeIndex_;
+
+    char pad1_[hardware_destructive_interference_size - sizeof(AtomicIndex)];
+};
+
+} // namespace aare

include/aare/RawFile.hpp

@@ -30,19 +30,12 @@ struct ModuleConfig {
  * Consider using that unless you need raw file specific functionality.
  */
 class RawFile : public FileInterface {
-    size_t n_subfiles{}; //f0,f1...fn
-    size_t n_subfile_parts{}; // d0,d1...dn
-    //TODO! move to vector of SubFile instead of pointers
-    std::vector<std::vector<RawSubFile *>> subfiles; //subfiles[f0,f1...fn][d0,d1...dn]
-    std::vector<xy> positions;
-    std::vector<ModuleGeometry> m_module_pixel_0;
+    std::vector<std::unique_ptr<RawSubFile>> m_subfiles;
     ModuleConfig cfg{0, 0};
-
     RawMasterFile m_master;
-
     size_t m_current_frame{};
-    size_t m_rows{};
-    size_t m_cols{};
+    size_t m_current_subfile{};
+    DetectorGeometry m_geometry;
 
   public:
     /**
@@ -52,7 +45,7 @@ class RawFile : public FileInterface {
 
      */
     RawFile(const std::filesystem::path &fname, const std::string &mode = "r");
-    virtual ~RawFile() override;
+    virtual ~RawFile() override = default;
 
     Frame read_frame() override;
     Frame read_frame(size_t frame_number) override;
@@ -76,7 +69,7 @@ class RawFile : public FileInterface {
     size_t cols() const override;
     size_t bitdepth() const override;
     xy geometry();
-    size_t n_mod() const;
+    size_t n_modules() const;
     
     RawMasterFile master() const;
 
@@ -111,11 +104,9 @@ class RawFile : public FileInterface {
      */
     static DetectorHeader read_header(const std::filesystem::path &fname);
 
-    void update_geometry_with_roi();
-    int find_number_of_subfiles();
-
     void open_subfiles();
     void find_geometry();
 };
 
+
 } // namespace aare

include/aare/RawMasterFile.hpp

@@ -62,17 +62,6 @@ class ScanParameters {
 };
 
 
-struct ROI{
-  int64_t xmin{};
-  int64_t xmax{};
-  int64_t ymin{};
-  int64_t ymax{};
-
-  int64_t height() const { return ymax - ymin; }
-  int64_t width() const { return xmax - xmin; }
-};
-
-
 /**
  * @brief Class for parsing a master file either in our .json format or the old
  * .raw format
@@ -132,6 +121,7 @@ class RawMasterFile {
 
     size_t total_frames_expected() const;
     xy geometry() const;
+    size_t n_modules() const;
 
     std::optional<size_t> analog_samples() const;
     std::optional<size_t> digital_samples() const;

include/aare/RawSubFile.hpp

@@ -18,11 +18,20 @@ class RawSubFile {
     std::ifstream m_file;
     DetectorType m_detector_type;
     size_t m_bitdepth;
-    std::filesystem::path m_fname;
+    std::filesystem::path m_path;     //!< path to the subfile
+    std::string m_base_name;          //!< base name used for formatting file names
+    size_t m_offset{}; //!< file index of the first file, allow starting at non zero file
+    size_t m_total_frames{};    //!< total number of frames in the series of files
     size_t m_rows{};
     size_t m_cols{};
     size_t m_bytes_per_frame{};
-    size_t n_frames{};
+
+
+    int m_module_index{};
+    size_t m_current_file_index{};   //!< The index of the open file
+    size_t m_current_frame_index{};  //!< The index of the current frame (with reference to all files)
+    std::vector<size_t> m_last_frame_in_file{}; //!< Used for seeking to the correct file
+
     uint32_t m_pos_row{};
     uint32_t m_pos_col{};
  
@@ -53,6 +62,7 @@ class RawSubFile {
     size_t tell();
 
     void read_into(std::byte *image_buf, DetectorHeader *header = nullptr);
+    void read_into(std::byte *image_buf, size_t n_frames, DetectorHeader *header= nullptr);
     void get_part(std::byte *buffer, size_t frame_index);
     
     void read_header(DetectorHeader *header);
@@ -64,8 +74,18 @@ class RawSubFile {
 
     size_t bytes_per_frame() const { return m_bytes_per_frame; }
     size_t pixels_per_frame() const { return m_rows * m_cols; }
-    size_t bytes_per_pixel() const { return m_bitdepth / 8; }
+    size_t bytes_per_pixel() const { return m_bitdepth / bits_per_byte; }
 
+    size_t frames_in_file() const { return m_total_frames; }
+
+private:
+  template <typename T>
+  void read_with_map(std::byte *image_buf);
+
+  void parse_fname(const std::filesystem::path &fname);
+  void scan_files();
+  void open_file(size_t file_index);
+  std::filesystem::path fpath(size_t file_index) const;
 
 };
 

include/aare/VarClusterFinder.hpp

@@ -7,7 +7,7 @@
 
 #include "aare/NDArray.hpp"
 
-const int MAX_CLUSTER_SIZE = 200;
+const int MAX_CLUSTER_SIZE = 50;
 namespace aare {
 
 template <typename T> class VarClusterFinder {
@@ -28,7 +28,7 @@ template <typename T> class VarClusterFinder {
     };
 
   private:
-    const std::array<int64_t, 2> shape_;
+    const std::array<ssize_t, 2> shape_;
     NDView<T, 2> original_;
     NDArray<int, 2> labeled_;
     NDArray<int, 2> peripheral_labeled_;
@@ -226,7 +226,7 @@ template <typename T> void VarClusterFinder<T>::single_pass(NDView<T, 2> img) {
 
 template <typename T> void VarClusterFinder<T>::first_pass() {
 
-    for (size_t i = 0; i < original_.size(); ++i) {
+    for (ssize_t i = 0; i < original_.size(); ++i) {
         if (use_noise_map)
             threshold_ = 5 * noiseMap(i);
         binary_(i) = (original_(i) > threshold_);
@@ -250,7 +250,7 @@ template <typename T> void VarClusterFinder<T>::first_pass() {
 
 template <typename T> void VarClusterFinder<T>::second_pass() {
 
-    for (size_t i = 0; i != labeled_.size(); ++i) {
+    for (ssize_t i = 0; i != labeled_.size(); ++i) {
         auto cl = labeled_(i);
         if (cl != 0) {
             auto it = child.find(cl);

include/aare/algorithm.hpp

@@ -0,0 +1,122 @@
+
+#pragma once
+#include <algorithm>
+#include <array>
+#include <vector>
+#include <aare/NDArray.hpp>
+
+namespace aare {
+/**
+ * @brief Index of the last element that is smaller than val.
+ * Requires a sorted array. Uses >= for ordering. If all elements
+ * are smaller it returns the last element and if all elements are
+ * larger it returns the first element.
+ * @param first iterator to the first element
+ * @param last iterator to the last element
+ * @param val value to compare
+ * @return index of the last element that is smaller than val
+ * 
+ */
+template <typename T>
+size_t last_smaller(const T* first, const T* last, T val) {
+    for (auto iter = first+1; iter != last; ++iter) {
+        if (*iter >= val) {
+            return std::distance(first, iter-1);
+        }
+    }
+    return std::distance(first, last-1);
+}
+
+template <typename T>
+size_t last_smaller(const NDArray<T, 1>& arr, T val) {
+    return last_smaller(arr.begin(), arr.end(), val);
+}
+
+template <typename T>
+size_t last_smaller(const std::vector<T>& vec, T val) {
+    return last_smaller(vec.data(), vec.data()+vec.size(), val);
+}
+
+/**
+ * @brief Index of the first element that is larger than val.
+ * Requires a sorted array. Uses > for ordering. If all elements
+ * are larger it returns the first element and if all elements are
+ * smaller it returns the last element.
+ * @param first iterator to the first element
+ * @param last iterator to the last element
+ * @param val value to compare
+ * @return index of the first element that is larger than val
+ */
+template <typename T>
+size_t first_larger(const T* first, const T* last, T val) {
+    for (auto iter = first; iter != last; ++iter) {
+        if (*iter > val) {
+            return std::distance(first, iter);
+        }
+    }
+    return std::distance(first, last-1);
+}
+
+template <typename T>
+size_t first_larger(const NDArray<T, 1>& arr, T val) {
+    return first_larger(arr.begin(), arr.end(), val);
+}
+
+template <typename T>
+size_t first_larger(const std::vector<T>& vec, T val) {
+    return first_larger(vec.data(), vec.data()+vec.size(), val);
+}
+
+/**
+ * @brief Index of the nearest element to val.
+ * Requires a sorted array. If there is no difference it takes the first element.
+ * @param first iterator to the first element
+ * @param last iterator to the last element
+ * @param val value to compare
+ * @return index of the nearest element
+ */
+template <typename T>
+size_t nearest_index(const T* first, const T* last, T val) {
+    auto iter = std::min_element(first, last,
+    [val](T a, T b) {
+        return std::abs(a - val) < std::abs(b - val);
+    });
+    return std::distance(first, iter);
+}
+
+template <typename T>
+size_t nearest_index(const NDArray<T, 1>& arr, T val) {
+    return nearest_index(arr.begin(), arr.end(), val);
+}
+
+template <typename T>
+size_t nearest_index(const std::vector<T>& vec, T val) {
+    return nearest_index(vec.data(), vec.data()+vec.size(), val);
+}
+
+template <typename T, size_t N>
+size_t nearest_index(const std::array<T,N>& arr, T val) {
+    return nearest_index(arr.data(), arr.data()+arr.size(), val);
+}
+
+template <typename T>
+std::vector<T> cumsum(const std::vector<T>& vec) {
+    std::vector<T> result(vec.size());
+    std::partial_sum(vec.begin(), vec.end(), result.begin());
+    return result;
+}
+
+
+template <typename Container> bool all_equal(const Container &c) {
+    if (!c.empty() &&
+        std::all_of(begin(c), end(c),
+                    [c](const typename Container::value_type &element) {
+                        return element == c.front();
+                    }))
+        return true;
+    return false;
+}
+
+
+
+} // namespace aare

include/aare/decode.hpp

@@ -0,0 +1,26 @@
+#pragma once
+
+#include <cstdint>
+#include <vector>
+#include <aare/NDView.hpp>
+namespace aare {
+
+
+uint16_t adc_sar_05_decode64to16(uint64_t input);
+uint16_t adc_sar_04_decode64to16(uint64_t input);
+void adc_sar_05_decode64to16(NDView<uint64_t, 2> input, NDView<uint16_t,2> output);
+void adc_sar_04_decode64to16(NDView<uint64_t, 2> input, NDView<uint16_t,2> output);
+
+
+/**
+ * @brief Apply custom weights to a 16-bit input value. Will sum up weights[i]**i
+ * for each bit i that is set in the input value.
+ * @throws std::out_of_range if weights.size() < 16
+ * @param input 16-bit input value
+ * @param weights vector of weights, size must be less than or equal to 16
+ */
+double apply_custom_weights(uint16_t input, const NDView<double, 1> weights);
+
+void apply_custom_weights(NDView<uint16_t, 1> input, NDView<double, 1> output, const NDView<double, 1> weights);
+
+} // namespace aare

include/aare/defs.hpp

@@ -1,11 +1,9 @@
 #pragma once
 
 #include "aare/Dtype.hpp"
-// #include "aare/utils/logger.hpp"
 
 #include <array>
 #include <stdexcept>
-
 #include <cassert>
 #include <cstdint>
 #include <cstring>
@@ -38,9 +36,12 @@
 
 namespace aare {
 
+inline constexpr size_t bits_per_byte = 8;
+
 void assert_failed(const std::string &msg);
 
 
+
 class DynamicCluster {
   public:
     int cluster_sizeX;
@@ -179,15 +180,49 @@ template <typename T> struct t_xy {
 using xy = t_xy<uint32_t>;
 
 
+/**
+ * @brief Class to hold the geometry of a module. Where pixel 0 is located and the size of the module
+ */
 struct ModuleGeometry{
-    int x{};
-    int y{};
+    int origin_x{};
+    int origin_y{};
     int height{};
     int width{};
+    int row_index{};
+    int col_index{}; 
 };
 
+/**
+ * @brief Class to hold the geometry of a detector. Number of modules, their size and where pixel 0 
+ * for each module is located
+ */
+struct DetectorGeometry{
+    int modules_x{};
+    int modules_y{};
+    int pixels_x{};
+    int pixels_y{};
+    int module_gap_row{};
+    int module_gap_col{};
+    std::vector<ModuleGeometry> module_pixel_0;
+    
+    auto size() const { return module_pixel_0.size(); }
+};
 
-using dynamic_shape = std::vector<int64_t>;
+struct ROI{
+    ssize_t xmin{};
+    ssize_t xmax{};
+    ssize_t ymin{};
+    ssize_t ymax{};
+  
+    ssize_t height() const { return ymax - ymin; }
+    ssize_t width() const { return xmax - xmin; }
+    bool contains(ssize_t x, ssize_t y) const {
+        return x >= xmin && x < xmax && y >= ymin && y < ymax;
+    }
+  };
+
+
+using dynamic_shape = std::vector<ssize_t>;
 
 //TODO! Can we uniform enums between the libraries?
 

include/aare/geo_helpers.hpp

@@ -0,0 +1,16 @@
+#pragma once
+#include "aare/defs.hpp"
+#include "aare/RawMasterFile.hpp" //ROI refactor away
+namespace aare{
+
+/**
+ * @brief Update the detector geometry given a region of interest
+ * 
+ * @param geo 
+ * @param roi 
+ * @return DetectorGeometry 
+ */
+DetectorGeometry update_geometry_with_roi(DetectorGeometry geo, ROI roi);
+
+
+} // namespace aare

include/aare/logger.hpp

@@ -0,0 +1,139 @@
+#pragma once
+/*Utility to log to console*/
+
+
+#include <iostream>
+#include <sstream>
+#include <sys/time.h>
+
+namespace aare {
+
+#define RED      "\x1b[31m"
+#define GREEN    "\x1b[32m"
+#define YELLOW   "\x1b[33m"
+#define BLUE     "\x1b[34m"
+#define MAGENTA  "\x1b[35m"
+#define CYAN     "\x1b[36m"
+#define GRAY     "\x1b[37m"
+#define DARKGRAY "\x1b[30m"
+
+#define BG_BLACK   "\x1b[48;5;232m"
+#define BG_RED     "\x1b[41m"
+#define BG_GREEN   "\x1b[42m"
+#define BG_YELLOW  "\x1b[43m"
+#define BG_BLUE    "\x1b[44m"
+#define BG_MAGENTA "\x1b[45m"
+#define BG_CYAN    "\x1b[46m"
+#define RESET      "\x1b[0m"
+#define BOLD       "\x1b[1m"
+
+
+enum TLogLevel {
+    logERROR,
+    logWARNING,
+    logINFOBLUE,
+    logINFOGREEN,
+    logINFORED,
+    logINFOCYAN,
+    logINFOMAGENTA,
+    logINFO,
+    logDEBUG,  // constructors, destructors etc. should still give too much output
+    logDEBUG1,
+    logDEBUG2,
+    logDEBUG3,
+    logDEBUG4,
+    logDEBUG5
+};
+
+// Compiler should optimize away anything below this value
+#ifndef AARE_LOG_LEVEL
+#define AARE_LOG_LEVEL "LOG LEVEL NOT SET IN CMAKE" //This is configured in the main CMakeLists.txt
+#endif
+
+#define __AT__                                                                 \
+    std::string(__FILE__) + std::string("::") + std::string(__func__) +        \
+        std::string("(): ")
+#define __SHORT_FORM_OF_FILE__                                                 \
+    (strrchr(__FILE__, '/') ? strrchr(__FILE__, '/') + 1 : __FILE__)
+#define __SHORT_AT__                                                           \
+    std::string(__SHORT_FORM_OF_FILE__) + std::string("::") +                  \
+        std::string(__func__) + std::string("(): ")
+
+class Logger {
+    std::ostringstream os;
+    TLogLevel m_level = AARE_LOG_LEVEL;
+
+  public:
+    Logger() = default;
+    explicit Logger(TLogLevel level) : m_level(level){};
+    ~Logger() {
+        // output in the destructor to allow for << syntax
+        os << RESET << '\n';
+        std::clog << os.str() << std::flush; // Single write
+    }
+
+    static TLogLevel &ReportingLevel() { // singelton eeh TODO! Do we need a runtime option?
+        static TLogLevel reportingLevel = logDEBUG5;
+        return reportingLevel;
+    }
+
+    // Danger this buffer need as many elements as TLogLevel
+    static const char *Color(TLogLevel level) noexcept {
+        static const char *const colors[] = {
+            RED BOLD, YELLOW BOLD, BLUE,  GREEN, RED,   CYAN,  MAGENTA,
+            RESET,    RESET,       RESET, RESET, RESET, RESET, RESET};
+        // out of bounds
+        if (level < 0 || level >= sizeof(colors) / sizeof(colors[0])) {
+            return RESET;
+        }
+        return colors[level];
+    }
+
+    // Danger this buffer need as many elements as TLogLevel
+    static std::string ToString(TLogLevel level) {
+        static const char *const buffer[] = {
+            "ERROR",  "WARNING", "INFO",   "INFO",  "INFO",
+            "INFO",   "INFO",    "INFO",   "DEBUG", "DEBUG1",
+            "DEBUG2", "DEBUG3",  "DEBUG4", "DEBUG5"};
+        // out of bounds
+        if (level < 0 || level >= sizeof(buffer) / sizeof(buffer[0])) {
+            return "UNKNOWN";
+        }
+        return buffer[level];
+    }
+
+    std::ostringstream &Get() {
+        os << Color(m_level) << "- " << Timestamp() << " " << ToString(m_level)
+           << ": ";
+        return os;
+    }
+
+    static std::string Timestamp() {
+        constexpr size_t buffer_len = 12;
+        char buffer[buffer_len];
+        time_t t;
+        ::time(&t);
+        tm r;
+        strftime(buffer, buffer_len, "%X", localtime_r(&t, &r));
+        buffer[buffer_len - 1] = '\0';
+        struct timeval tv;
+        gettimeofday(&tv, nullptr);
+        constexpr size_t result_len = 100;
+        char result[result_len];
+        snprintf(result, result_len, "%s.%03ld", buffer,
+                 static_cast<long>(tv.tv_usec) / 1000);
+        result[result_len - 1] = '\0';
+        return result;
+    }
+};
+
+// TODO! Do we need to keep the runtime option?
+#define LOG(level)                                                             \
+    if (level > AARE_LOG_LEVEL)                                                \
+        ;                                                                      \
+    else if (level > aare::Logger::ReportingLevel())                           \
+        ;                                                                      \
+    else                                                                       \
+        aare::Logger(level).Get()
+
+} // namespace aare

include/aare/utils/ifstream_helpers.hpp

@@ -0,0 +1,12 @@
+#pragma once
+
+#include <fstream>
+#include <string>
+namespace aare {
+
+/** 
+ * @brief Get the error message from an ifstream object
+*/
+std::string ifstream_error_msg(std::ifstream &ifs);
+
+} // namespace aare

include/aare/utils/par.hpp

@@ -0,0 +1,18 @@
+#include <thread>
+#include <vector>
+#include <utility>
+
+namespace aare {
+
+    template<typename F>
+    void RunInParallel(F func, const std::vector<std::pair<int, int>>& tasks) {
+        // auto tasks = split_task(0, y.shape(0), n_threads);
+        std::vector<std::thread> threads;
+        for (auto &task : tasks) {
+            threads.push_back(std::thread(func, task.first, task.second));
+        }
+        for (auto &thread : threads) {
+            thread.join();
+        }
+    }
+} // namespace aare

include/aare/utils/task.hpp

@@ -0,0 +1,8 @@
+
+#include <utility>
+#include <vector>
+
+namespace aare {
+std::vector<std::pair<int, int>> split_task(int first, int last, int n_threads);
+
+} // namespace aare

patches/libzmq_cmake_version.patch

@@ -0,0 +1,18 @@
+diff --git a/CMakeLists.txt b/CMakeLists.txt
+index dd3d8eb9..c0187747 100644
+--- a/CMakeLists.txt
++++ b/CMakeLists.txt
+@@ -1,11 +1,8 @@
+ # CMake build script for ZeroMQ
+ project(ZeroMQ)
+ 
+-if(${CMAKE_SYSTEM_NAME} STREQUAL Darwin)
+-  cmake_minimum_required(VERSION 3.0.2)
+-else()
+-  cmake_minimum_required(VERSION 2.8.12)
+-endif()
++cmake_minimum_required(VERSION 3.15)
++message(STATUS "Patched cmake version")
+ 
+ include(CheckIncludeFiles)
+ include(CheckCCompilerFlag)

patches/lmfit.patch

@@ -0,0 +1,13 @@
+diff --git a/lib/CMakeLists.txt b/lib/CMakeLists.txt
+index 4efb7ed..6533660 100644
+--- a/lib/CMakeLists.txt
++++ b/lib/CMakeLists.txt
+@@ -11,7 +11,7 @@ target_compile_definitions(${lib} PRIVATE "LMFIT_EXPORT") # for Windows DLL expo
+ 
+ target_include_directories(${lib}
+     PUBLIC
+-    $<BUILD_INTERFACE:${CMAKE_SOURCE_DIR}/>
++    $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/>
+     $<INSTALL_INTERFACE:include/>
+     )
+ 

pyproject.toml

@@ -1,15 +1,41 @@
+[tool.scikit-build.metadata.version]
+provider = "scikit_build_core.metadata.regex"
+input = "VERSION"
+regex = '^(?P<version>\d+(?:\.\d+)*(?:[\.\+\w]+)?)$'
+result = "{version}" 
+
 [build-system]
 requires = ["scikit-build-core>=0.10", "pybind11", "numpy"]
 build-backend = "scikit_build_core.build"
 
 [project]
 name = "aare"
-version = "2024.12.16.dev0"
+dynamic = ["version"]
+requires-python = ">=3.11"
+dependencies = [
+    "numpy",
+    "matplotlib",
+]
+
+
+[tool.cibuildwheel]
+
+build = "cp{311,312,313}-manylinux_x86_64"
+
+
+
 
 [tool.scikit-build]
-cmake.verbose = true
+build.verbose = true
+cmake.build-type = "Release"
+install.components = ["python"]
 
 [tool.scikit-build.cmake.define]
 AARE_PYTHON_BINDINGS = "ON"
-AARE_SYSTEM_LIBRARIES = "ON"
-AARE_INSTALL_PYTHONEXT = "ON"
+AARE_INSTALL_PYTHONEXT = "ON"
+
+
+[tool.pytest.ini_options]
+markers = [
+    "files: marks tests that need additional data (deselect with '-m \"not files\"')",
+]

python/CMakeLists.txt

@@ -1,12 +1,13 @@
 
-find_package (Python 3.10 COMPONENTS Interpreter Development REQUIRED)
+find_package (Python 3.10 COMPONENTS Interpreter Development.Module REQUIRED)
+set(PYBIND11_FINDPYTHON ON) # Needed for RH8
 
 # Download or find pybind11 depending on configuration
 if(AARE_FETCH_PYBIND11)
     FetchContent_Declare(
         pybind11
         GIT_REPOSITORY https://github.com/pybind/pybind11
-        GIT_TAG        v2.13.0
+        GIT_TAG        v2.13.6
     )
     FetchContent_MakeAvailable(pybind11)
 else()
@@ -28,12 +29,17 @@ target_link_libraries(_aare PRIVATE aare_core aare_compiler_flags)
 set( PYTHON_FILES
     aare/__init__.py
     aare/CtbRawFile.py
+    aare/ClusterFinder.py
+    aare/ClusterVector.py
+
+    aare/func.py
     aare/RawFile.py
     aare/transform.py
     aare/ScanParameters.py
     aare/utils.py
 )
 
+
 # Copy the python files to the build directory
 foreach(FILE ${PYTHON_FILES})
     configure_file(${FILE} ${CMAKE_BINARY_DIR}/${FILE}  )
@@ -43,17 +49,30 @@ set_target_properties(_aare PROPERTIES
     LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/aare
 )
 
+set(PYTHON_EXAMPLES
+    examples/play.py
+    examples/fits.py
+)
 
 
-# Copy the examples/scripts to the build directory
-configure_file(examples/play.py ${CMAKE_BINARY_DIR}/play.py)
+# Copy the python examples to the build directory
+foreach(FILE ${PYTHON_EXAMPLES})
+    configure_file(${FILE} ${CMAKE_BINARY_DIR}/${FILE}  )
+    message(STATUS "Copying ${FILE} to ${CMAKE_BINARY_DIR}/${FILE}")
+endforeach(FILE ${PYTHON_EXAMPLES})
 
 
 if(AARE_INSTALL_PYTHONEXT)
-    install(TARGETS _aare
+    install(
+        TARGETS _aare
         EXPORT "${TARGETS_EXPORT_NAME}"
         LIBRARY DESTINATION aare
+        COMPONENT python
     )
 
-    install(FILES ${PYTHON_FILES} DESTINATION aare)
+    install(
+        FILES ${PYTHON_FILES} 
+        DESTINATION aare
+        COMPONENT python
+        )
 endif()

python/aare/ClusterFinder.py

@@ -0,0 +1,84 @@
+
+# from ._aare import ClusterFinder_Cluster3x3i, ClusterFinder_Cluster2x2i, ClusterFinderMT_Cluster3x3i, ClusterFinderMT_Cluster2x2i, ClusterCollector_Cluster3x3i, ClusterCollector_Cluster2x2i
+
+
+# from ._aare import ClusterFileSink_Cluster3x3i, ClusterFileSink_Cluster2x2i
+
+from . import _aare
+import numpy as np
+
+_supported_cluster_sizes = [(2,2), (3,3), (5,5), (7,7), (9,9),]
+
+# def _get_class()
+
+def _type_to_char(dtype):
+    if dtype == np.int32:
+        return 'i'
+    elif dtype == np.float32:
+        return 'f'
+    elif dtype == np.float64:
+        return 'd'
+    else:
+        raise ValueError(f"Unsupported dtype: {dtype}. Only np.int32, np.float32, and np.float64 are supported.")
+
+def _get_class(name, cluster_size, dtype):
+    """
+    Helper function to get the class based on the name, cluster size, and dtype.
+    """
+    try:
+        class_name = f"{name}_Cluster{cluster_size[0]}x{cluster_size[1]}{_type_to_char(dtype)}"
+        cls = getattr(_aare, class_name)
+    except AttributeError:
+        raise ValueError(f"Unsupported combination of type and cluster size: {dtype}/{cluster_size} when requesting {class_name}")
+    return cls
+
+
+
+def ClusterFinder(image_size, cluster_size, n_sigma=5, dtype = np.int32, capacity = 1024):
+    """
+    Factory function to create a ClusterFinder object. Provides a cleaner syntax for 
+    the templated ClusterFinder in C++.
+    """
+    cls = _get_class("ClusterFinder", cluster_size, dtype)
+    return cls(image_size, n_sigma=n_sigma, capacity=capacity)
+
+
+
+def ClusterFinderMT(image_size, cluster_size = (3,3), dtype=np.int32, n_sigma=5, capacity = 1024, n_threads = 3): 
+    """ 
+    Factory function to create a ClusterFinderMT object. Provides a cleaner syntax for 
+    the templated ClusterFinderMT in C++.
+    """
+
+    cls = _get_class("ClusterFinderMT", cluster_size, dtype)
+    return cls(image_size, n_sigma=n_sigma, capacity=capacity, n_threads=n_threads)
+
+
+
+def ClusterCollector(clusterfindermt, cluster_size = (3,3), dtype=np.int32): 
+    """ 
+    Factory function to create a ClusterCollector object. Provides a cleaner syntax for 
+    the templated ClusterCollector in C++.
+    """
+
+    cls = _get_class("ClusterCollector", cluster_size, dtype)
+    return cls(clusterfindermt)
+
+def ClusterFileSink(clusterfindermt, cluster_file, dtype=np.int32): 
+    """ 
+    Factory function to create a ClusterCollector object. Provides a cleaner syntax for 
+    the templated ClusterCollector in C++.
+    """
+
+    cls = _get_class("ClusterFileSink", clusterfindermt.cluster_size, dtype)
+    return cls(clusterfindermt, cluster_file)
+
+
+def ClusterFile(fname, cluster_size=(3,3), dtype=np.int32):
+    """
+    Factory function to create a ClusterFile object. Provides a cleaner syntax for
+    the templated ClusterFile in C++.
+    """
+
+    cls = _get_class("ClusterFile", cluster_size, dtype)
+    return cls(fname)

python/aare/ClusterVector.py

@@ -0,0 +1,11 @@
+
+
+from ._aare import ClusterVector_Cluster3x3i
+import numpy as np
+
+def ClusterVector(cluster_size, dtype = np.int32):
+
+    if dtype == np.int32 and cluster_size == (3,3):
+        return ClusterVector_Cluster3x3i()
+    else:
+        raise ValueError(f"Unsupported dtype: {dtype}. Only np.int32 is supported.")

python/aare/__init__.py

@@ -2,14 +2,31 @@
 from . import _aare
 
 
-from ._aare import File, RawMasterFile, RawSubFile
-from ._aare import Pedestal_d, Pedestal_f, ClusterFinder, VarClusterFinder
+from ._aare import File, RawMasterFile, RawSubFile, JungfrauDataFile
+from ._aare import Pedestal_d, Pedestal_f, ClusterFinder_Cluster3x3i, VarClusterFinder
 from ._aare import DetectorType
-from ._aare import ClusterFile
 from ._aare import hitmap
+from ._aare import ROI
+
+# from ._aare import ClusterFinderMT, ClusterCollector, ClusterFileSink, ClusterVector_i
+
+from .ClusterFinder import ClusterFinder, ClusterCollector, ClusterFinderMT, ClusterFileSink, ClusterFile
+from .ClusterVector import ClusterVector
+
+
+from ._aare import fit_gaus, fit_pol1, fit_scurve, fit_scurve2
+from ._aare import Interpolator
+from ._aare import calculate_eta2
+
+
+from ._aare import apply_custom_weights
 
 from .CtbRawFile import CtbRawFile
 from .RawFile import RawFile
 from .ScanParameters import ScanParameters
 
-from .utils import random_pixels, random_pixel
+from .utils import random_pixels, random_pixel, flat_list, add_colorbar
+
+
+#make functions available in the top level API
+from .func import *

python/aare/func.py

@@ -0,0 +1 @@
+from ._aare import gaus, pol1, scurve, scurve2

python/aare/transform.py

@@ -2,6 +2,14 @@ import numpy as np
 from . import _aare
 
 
+class AdcSar04Transform64to16:
+    def __call__(self, data):
+        return _aare.adc_sar_04_decode64to16(data)
+
+class AdcSar05Transform64to16:
+    def __call__(self, data):
+        return _aare.adc_sar_05_decode64to16(data)
+
 class Moench05Transform:
     #Could be moved to C++ without changing the interface
     def __init__(self):
@@ -45,4 +53,6 @@ class Matterhorn02Transform:
 moench05 = Moench05Transform()
 moench05_1g = Moench05Transform1g()
 moench05_old = Moench05TransformOld()
-matterhorn02 = Matterhorn02Transform()
+matterhorn02 = Matterhorn02Transform()
+adc_sar_04_64to16 = AdcSar04Transform64to16()
+adc_sar_05_64to16 = AdcSar05Transform64to16()

python/aare/utils.py

@@ -1,4 +1,6 @@
 import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.axes_grid1 import make_axes_locatable
 
 def random_pixels(n_pixels, xmin=0, xmax=512, ymin=0, ymax=1024):
     """Return a list of random pixels.
@@ -20,4 +22,15 @@ def random_pixel(xmin=0, xmax=512, ymin=0, ymax=1024):
     Returns:
         tuple: (row, col)
     """
-    return random_pixels(1, xmin, xmax, ymin, ymax)[0]
+    return random_pixels(1, xmin, xmax, ymin, ymax)[0]
+
+def flat_list(xss):
+    """Flatten a list of lists."""
+    return [x for xs in xss for x in xs]
+
+def add_colorbar(ax, im, size="5%", pad=0.05):
+    """Add a colorbar with the same height as the image."""
+    divider = make_axes_locatable(ax)
+    cax = divider.append_axes("right", size=size, pad=pad)
+    plt.colorbar(im, cax=cax)
+    return ax, im, cax

python/examples/fits.py

@@ -0,0 +1,79 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from aare import fit_gaus, fit_pol1
+from aare import gaus, pol1
+
+textpm = f"±"  #
+textmu = f"μ"  #
+textsigma = f"σ"  #
+
+
+
+# ================================= Gauss fit =================================
+# Parameters
+mu = np.random.uniform(1, 100)  # Mean of Gaussian
+sigma = np.random.uniform(4, 20)  # Standard deviation
+num_points = 10000  # Number of points for smooth distribution
+noise_sigma = 100
+
+# Generate Gaussian distribution
+data = np.random.normal(mu, sigma, num_points)
+
+# Generate errors for each point
+errors = np.abs(np.random.normal(0, sigma, num_points))  # Errors with mean 0, std 0.5
+
+# Create subplot
+fig0, ax0 = plt.subplots(1, 1, num=0, figsize=(12, 8))
+
+x = np.histogram(data, bins=30)[1][:-1] + 0.05
+y = np.histogram(data, bins=30)[0]
+yerr = errors[:30]
+
+
+# Add the errors as error bars in the step plot
+ax0.errorbar(x, y, yerr=yerr, fmt=". ", capsize=5)
+ax0.grid()
+
+par, err = fit_gaus(x, y, yerr)
+print(par, err)
+
+x = np.linspace(x[0], x[-1], 1000)
+ax0.plot(x, gaus(x, par), marker="")
+ax0.set(xlabel="x", ylabel="Counts", title=f"A0 = {par[0]:0.2f}{textpm}{err[0]:0.2f}\n"
+                                           f"{textmu} = {par[1]:0.2f}{textpm}{err[1]:0.2f}\n"
+                                           f"{textsigma} = {par[2]:0.2f}{textpm}{err[2]:0.2f}\n"
+                                           f"(init: {textmu}: {mu:0.2f}, {textsigma}: {sigma:0.2f})")
+fig0.tight_layout()
+
+
+
+# ================================= pol1 fit =================================
+# Parameters
+n_points = 40
+
+# Generate random slope and intercept (origin)
+slope = np.random.uniform(-10, 10)  # Random slope between 0.5 and 2.0
+intercept = np.random.uniform(-10, 10)  # Random intercept between -10 and 10
+
+# Generate random x values
+x_values = np.random.uniform(-10, 10, n_points)
+
+# Calculate y values based on the linear function y = mx + b + error
+errors = np.abs(np.random.normal(0, np.random.uniform(1, 5), n_points))
+var_points = np.random.normal(0, np.random.uniform(0.1, 2), n_points)
+y_values = slope * x_values + intercept + var_points
+
+fig1, ax1 = plt.subplots(1, 1, num=1, figsize=(12, 8))
+ax1.errorbar(x_values, y_values, yerr=errors, fmt=". ", capsize=5)
+par, err = fit_pol1(x_values, y_values, errors)
+
+
+x = np.linspace(np.min(x_values), np.max(x_values), 1000)
+ax1.plot(x, pol1(x, par), marker="")
+ax1.set(xlabel="x", ylabel="y", title=f"a = {par[0]:0.2f}{textpm}{err[0]:0.2f}\n"
+                                      f"b = {par[1]:0.2f}{textpm}{err[1]:0.2f}\n"
+                                      f"(init: {slope:0.2f}, {intercept:0.2f})")
+fig1.tight_layout()
+
+plt.show()
+

python/examples/play.py

@@ -1,58 +1,89 @@
 import sys
 sys.path.append('/home/l_msdetect/erik/aare/build')
 
-#Our normal python imports
+
+from aare import RawSubFile, DetectorType, RawFile
+
 from pathlib import Path
-import matplotlib.pyplot as plt
-import numpy as np
-import boost_histogram as bh
-import time
+path = Path("/home/l_msdetect/erik/data/aare-test-data/raw/jungfrau/")
+f = RawSubFile(path/"jungfrau_single_d0_f0_0.raw", DetectorType.Jungfrau, 512, 1024, 16)
 
-from aare import File, ClusterFinder, VarClusterFinder
+# f = RawFile(path/"jungfrau_single_master_0.json")
 
-base = Path('/mnt/sls_det_storage/matterhorn_data/aare_test_data/')
 
-f = File(base/'Moench03new/cu_half_speed_master_4.json')
-cf = ClusterFinder((400,400), (3,3))
-for i in range(1000):
-    cf.push_pedestal_frame(f.read_frame())
+# from aare._aare import ClusterVector_i, Interpolator
 
-fig, ax = plt.subplots()
-im = ax.imshow(cf.pedestal())
-cf.pedestal()
-cf.noise()
+# import pickle 
+# import numpy as np
+# import matplotlib.pyplot as plt
+# import boost_histogram as bh
+# import torch
+# import math
+# import time
 
 
 
-N = 500
-t0 = time.perf_counter()
-hist1 = bh.Histogram(bh.axis.Regular(40, -2, 4000))
-f.seek(0)
+# def gaussian_2d(mx, my, sigma = 1, res=100, grid_size = 2):
+#     """
+#     Generate a 2D gaussian as position mx, my, with sigma=sigma. 
+#     The gaussian is placed on a 2x2 pixel matrix with resolution 
+#     res in one dimesion.
+#     """
+#     x = torch.linspace(0, pixel_size*grid_size, res)
+#     x,y = torch.meshgrid(x,x, indexing="ij")
+#     return 1 / (2*math.pi*sigma**2) * \
+#       torch.exp(-((x - my)**2 / (2*sigma**2) + (y - mx)**2 / (2*sigma**2)))
 
-t0 = time.perf_counter()
-data = f.read_n(N)
-t_elapsed = time.perf_counter()-t0
+# scale = 1000 #Scale factor when converting to integer
+# pixel_size = 25 #um
+# grid = 2
+# resolution = 100
+# sigma_um = 10
+# xa = np.linspace(0,grid*pixel_size,resolution)
+# ticks = [0, 25, 50]
+
+# hit = np.array((20,20))
+# etahist_fname = "/home/l_msdetect/erik/tmp/test_hist.pkl"
+
+# local_resolution = 99
+# grid_size = 3
+# xaxis = np.linspace(0,grid_size*pixel_size, local_resolution)
+# t = gaussian_2d(hit[0],hit[1], grid_size = grid_size, sigma = 10, res = local_resolution)
+# pixels = t.reshape(grid_size, t.shape[0] // grid_size, grid_size, t.shape[1] // grid_size).sum(axis = 3).sum(axis = 1)
+# pixels = pixels.numpy()
+# pixels = (pixels*scale).astype(np.int32)
+# v = ClusterVector_i(3,3)
+# v.push_back(1,1, pixels)
+
+# with open(etahist_fname, "rb") as f:
+#         hist = pickle.load(f)
+# eta = hist.view().copy()
+# etabinsx = np.array(hist.axes.edges.T[0].flat)
+# etabinsy = np.array(hist.axes.edges.T[1].flat)
+# ebins = np.array(hist.axes.edges.T[2].flat)
+# p = Interpolator(eta, etabinsx[0:-1], etabinsy[0:-1], ebins[0:-1])
 
 
-n_bytes = data.itemsize*data.size
-
-print(f'Reading {N} frames took {t_elapsed:.3f}s {N/t_elapsed:.0f} FPS, {n_bytes/1024**2:.4f} GB/s')
 
 
-for frame in data:
-    a = cf.find_clusters(frame)
-
-clusters = cf.steal_clusters()
-
-# t_elapsed = time.perf_counter()-t0
-# print(f'Clustering {N} frames took {t_elapsed:.2f}s  {N/t_elapsed:.0f} FPS')
+# #Generate the hit
 
 
-# t0 = time.perf_counter()
-# total_clusters = clusters.size
 
-# hist1.fill(clusters.sum())
 
-# t_elapsed = time.perf_counter()-t0
-# print(f'Filling histogram with the sum of {total_clusters} clusters took: {t_elapsed:.3f}s, {total_clusters/t_elapsed:.3g} clust/s')
-# print(f'Average number of clusters per frame {total_clusters/N:.3f}')
+# tmp = p.interpolate(v)
+# print(f'tmp:{tmp}')
+# pos = np.array((tmp['x'], tmp['y']))*25
+
+
+# print(pixels)
+# fig, ax = plt.subplots(figsize = (7,7))
+# ax.pcolormesh(xaxis, xaxis, t)
+# ax.plot(*pos, 'o')
+# ax.set_xticks([0,25,50,75])
+# ax.set_yticks([0,25,50,75])
+# ax.set_xlim(0,75)
+# ax.set_ylim(0,75)
+# ax.grid()
+# print(f'{hit=}')
+# print(f'{pos=}')

python/src/bind_Cluster.hpp

@@ -0,0 +1,64 @@
+#include "aare/Cluster.hpp"
+
+#include <cstdint>
+#include <fmt/format.h>
+#include <filesystem>
+#include <pybind11/pybind11.h>
+#include <pybind11/numpy.h>
+#include <pybind11/stl.h>
+#include <pybind11/stl_bind.h>
+
+namespace py = pybind11;
+using pd_type = double;
+
+using namespace aare;
+
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wunused-parameter"
+
+template <typename Type, uint8_t ClusterSizeX, uint8_t ClusterSizeY,
+          typename CoordType>
+void define_Cluster(py::module &m, const std::string &typestr) {
+    auto class_name = fmt::format("Cluster{}", typestr);
+
+    py::class_<Cluster<Type, ClusterSizeX, ClusterSizeY, CoordType>>(
+        m, class_name.c_str(), py::buffer_protocol())
+
+        .def(py::init([](uint8_t x, uint8_t y, py::array_t<Type> data) {
+            py::buffer_info buf_info = data.request();
+            Cluster<Type, ClusterSizeX, ClusterSizeY, CoordType> cluster;
+            cluster.x = x;
+            cluster.y = y;
+            auto r = data.template unchecked<1>(); // no bounds checks
+            for (py::ssize_t i = 0; i < data.size(); ++i) {
+                cluster.data[i] = r(i);
+            }
+            return cluster;
+        }));
+
+    /*
+    //TODO! Review if to keep or not
+    .def_property(
+        "data",
+        [](ClusterType &c) -> py::array {
+            return py::array(py::buffer_info(
+                c.data, sizeof(Type),
+                py::format_descriptor<Type>::format(), // Type
+                                                       // format
+                1, // Number of dimensions
+                {static_cast<ssize_t>(ClusterSizeX *
+                                      ClusterSizeY)}, // Shape (flattened)
+                {sizeof(Type)} // Stride (step size between elements)
+                ));
+        },
+        [](ClusterType &c, py::array_t<Type> arr) {
+            py::buffer_info buf_info = arr.request();
+            Type *ptr = static_cast<Type *>(buf_info.ptr);
+            std::copy(ptr, ptr + ClusterSizeX * ClusterSizeY,
+                      c.data); // TODO dont iterate over centers!!!
+
+        });
+    */
+}
+
+#pragma GCC diagnostic pop

python/src/bind_ClusterCollector.hpp

@@ -0,0 +1,46 @@
+#include "aare/ClusterCollector.hpp"
+#include "aare/ClusterFileSink.hpp"
+#include "aare/ClusterFinder.hpp"
+#include "aare/ClusterFinderMT.hpp"
+#include "aare/ClusterVector.hpp"
+#include "aare/NDView.hpp"
+#include "aare/Pedestal.hpp"
+#include "np_helper.hpp"
+
+#include <cstdint>
+#include <filesystem>
+#include <pybind11/pybind11.h>
+#include <pybind11/stl.h>
+#include <pybind11/stl_bind.h>
+
+namespace py = pybind11;
+using pd_type = double;
+
+using namespace aare;
+
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wunused-parameter"
+
+
+template <typename T, uint8_t ClusterSizeX, uint8_t ClusterSizeY,
+          typename CoordType = uint16_t>
+void define_ClusterCollector(py::module &m,
+                                       const std::string &typestr) {
+    auto class_name = fmt::format("ClusterCollector_{}", typestr);
+
+    using ClusterType = Cluster<T, ClusterSizeX, ClusterSizeY, CoordType>;
+
+    py::class_<ClusterCollector<ClusterType>>(m, class_name.c_str())
+        .def(py::init<ClusterFinderMT<ClusterType, uint16_t, double> *>())
+        .def("stop", &ClusterCollector<ClusterType>::stop)
+        .def(
+            "steal_clusters",
+            [](ClusterCollector<ClusterType> &self) {
+                auto v = new std::vector<ClusterVector<ClusterType>>(
+                    self.steal_clusters());
+                return v; // TODO change!!!
+            },
+            py::return_value_policy::take_ownership);
+}
+
+#pragma GCC diagnostic pop

python/src/bind_ClusterFile.hpp

@@ -0,0 +1,94 @@
+#include "aare/CalculateEta.hpp"
+#include "aare/ClusterFile.hpp"
+#include "aare/defs.hpp"
+
+#include <cstdint>
+#include <filesystem>
+#include <pybind11/iostream.h>
+#include <pybind11/numpy.h>
+#include <pybind11/pybind11.h>
+#include <pybind11/stl.h>
+#include <pybind11/stl/filesystem.h>
+#include <string>
+
+// Disable warnings for unused parameters, as we ignore some
+// in the __exit__ method
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wunused-parameter"
+
+namespace py = pybind11;
+using namespace ::aare;
+
+template <typename Type, uint8_t CoordSizeX, uint8_t CoordSizeY,
+          typename CoordType = uint16_t>
+void define_ClusterFile(py::module &m,
+                                     const std::string &typestr) {
+
+    using ClusterType = Cluster<Type, CoordSizeX, CoordSizeY, CoordType>;
+
+    auto class_name = fmt::format("ClusterFile_{}", typestr);
+
+    py::class_<ClusterFile<ClusterType>>(m, class_name.c_str())
+        .def(py::init<const std::filesystem::path &, size_t,
+                      const std::string &>(),
+             py::arg(), py::arg("chunk_size") = 1000, py::arg("mode") = "r")
+        .def(
+            "read_clusters",
+            [](ClusterFile<ClusterType> &self, size_t n_clusters) {
+                auto v = new ClusterVector<ClusterType>(
+                    self.read_clusters(n_clusters));
+                return v;
+            },
+            py::return_value_policy::take_ownership)
+        .def("read_frame",
+             [](ClusterFile<ClusterType> &self) {
+                 auto v = new ClusterVector<ClusterType>(self.read_frame());
+                 return v;
+             })
+        .def("set_roi", &ClusterFile<ClusterType>::set_roi)
+        .def(
+            "set_noise_map",
+            [](ClusterFile<ClusterType> &self, py::array_t<int32_t> noise_map) {
+                auto view = make_view_2d(noise_map);
+                self.set_noise_map(view);
+            })
+
+        .def("set_gain_map",
+             [](ClusterFile<ClusterType> &self, py::array_t<double> gain_map) {
+                 auto view = make_view_2d(gain_map);
+                 self.set_gain_map(view);
+             })
+
+        .def("close", &ClusterFile<ClusterType>::close)
+        .def("write_frame", &ClusterFile<ClusterType>::write_frame)
+        .def("__enter__", [](ClusterFile<ClusterType> &self) { return &self; })
+        .def("__exit__",
+             [](ClusterFile<ClusterType> &self,
+                const std::optional<pybind11::type> &exc_type,
+                const std::optional<pybind11::object> &exc_value,
+                const std::optional<pybind11::object> &traceback) {
+                 self.close();
+             })
+        .def("__iter__", [](ClusterFile<ClusterType> &self) { return &self; })
+        .def("__next__", [](ClusterFile<ClusterType> &self) {
+            auto v = new ClusterVector<ClusterType>(
+                self.read_clusters(self.chunk_size()));
+            if (v->size() == 0) {
+                throw py::stop_iteration();
+            }
+            return v;
+        });
+}
+
+template <typename Type, uint8_t CoordSizeX, uint8_t CoordSizeY,
+          typename CoordType = uint16_t>
+void register_calculate_eta(py::module &m) {
+    using ClusterType = Cluster<Type, CoordSizeX, CoordSizeY, CoordType>;
+    m.def("calculate_eta2",
+          [](const aare::ClusterVector<ClusterType> &clusters) {
+              auto eta2 = new NDArray<double, 2>(calculate_eta2(clusters));
+              return return_image_data(eta2);
+          });
+}
+
+#pragma GCC diagnostic pop

python/src/bind_ClusterFileSink.hpp

@@ -0,0 +1,44 @@
+#include "aare/ClusterCollector.hpp"
+#include "aare/ClusterFileSink.hpp"
+#include "aare/ClusterFinder.hpp"
+#include "aare/ClusterFinderMT.hpp"
+#include "aare/ClusterVector.hpp"
+#include "aare/NDView.hpp"
+#include "aare/Pedestal.hpp"
+#include "np_helper.hpp"
+
+#include <cstdint>
+#include <filesystem>
+#include <pybind11/pybind11.h>
+#include <pybind11/stl.h>
+#include <pybind11/stl_bind.h>
+
+namespace py = pybind11;
+using pd_type = double;
+
+using namespace aare;
+
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wunused-parameter"
+
+
+
+
+
+
+template <typename T, uint8_t ClusterSizeX, uint8_t ClusterSizeY,
+          typename CoordType = uint16_t>
+void define_ClusterFileSink(py::module &m,
+                                       const std::string &typestr) {
+    auto class_name = fmt::format("ClusterFileSink_{}", typestr);
+
+    using ClusterType = Cluster<T, ClusterSizeX, ClusterSizeY, CoordType>;
+
+    py::class_<ClusterFileSink<ClusterType>>(m, class_name.c_str())
+        .def(py::init<ClusterFinderMT<ClusterType, uint16_t, double> *,
+                      const std::filesystem::path &>())
+        .def("stop", &ClusterFileSink<ClusterType>::stop);
+}
+
+
+#pragma GCC diagnostic pop

python/src/bind_ClusterFinder.hpp

@@ -0,0 +1,77 @@
+#include "aare/ClusterCollector.hpp"
+#include "aare/ClusterFileSink.hpp"
+#include "aare/ClusterFinder.hpp"
+#include "aare/ClusterFinderMT.hpp"
+#include "aare/ClusterVector.hpp"
+#include "aare/NDView.hpp"
+#include "aare/Pedestal.hpp"
+#include "np_helper.hpp"
+
+#include <cstdint>
+#include <filesystem>
+#include <pybind11/pybind11.h>
+#include <pybind11/stl.h>
+#include <pybind11/stl_bind.h>
+
+namespace py = pybind11;
+using pd_type = double;
+
+using namespace aare;
+
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wunused-parameter"
+
+template <typename T, uint8_t ClusterSizeX, uint8_t ClusterSizeY,
+          typename CoordType = uint16_t>
+void define_ClusterFinder(py::module &m, const std::string &typestr) {
+    auto class_name = fmt::format("ClusterFinder_{}", typestr);
+
+    using ClusterType = Cluster<T, ClusterSizeX, ClusterSizeY, CoordType>;
+
+    py::class_<ClusterFinder<ClusterType, uint16_t, pd_type>>(
+        m, class_name.c_str())
+        .def(py::init<Shape<2>, pd_type, size_t>(), py::arg("image_size"),
+             py::arg("n_sigma") = 5.0, py::arg("capacity") = 1'000'000)
+        .def("push_pedestal_frame",
+             [](ClusterFinder<ClusterType, uint16_t, pd_type> &self,
+                py::array_t<uint16_t> frame) {
+                 auto view = make_view_2d(frame);
+                 self.push_pedestal_frame(view);
+             })
+        .def("clear_pedestal",
+             &ClusterFinder<ClusterType, uint16_t, pd_type>::clear_pedestal)
+        .def_property_readonly(
+            "pedestal",
+            [](ClusterFinder<ClusterType, uint16_t, pd_type> &self) {
+                auto pd = new NDArray<pd_type, 2>{};
+                *pd = self.pedestal();
+                return return_image_data(pd);
+            })
+        .def_property_readonly(
+            "noise",
+            [](ClusterFinder<ClusterType, uint16_t, pd_type> &self) {
+                auto arr = new NDArray<pd_type, 2>{};
+                *arr = self.noise();
+                return return_image_data(arr);
+            })
+        .def(
+            "steal_clusters",
+            [](ClusterFinder<ClusterType, uint16_t, pd_type> &self,
+               bool realloc_same_capacity) {
+                ClusterVector<ClusterType> clusters =
+                    self.steal_clusters(realloc_same_capacity);
+                return clusters;
+            },
+            py::arg("realloc_same_capacity") = false)
+        .def(
+            "find_clusters",
+            [](ClusterFinder<ClusterType, uint16_t, pd_type> &self,
+               py::array_t<uint16_t> frame, uint64_t frame_number) {
+                auto view = make_view_2d(frame);
+                self.find_clusters(view, frame_number);
+                return;
+            },
+            py::arg(), py::arg("frame_number") = 0);
+}
+
+#pragma GCC diagnostic pop

python/src/bind_ClusterFinderMT.hpp

@@ -0,0 +1,81 @@
+#include "aare/ClusterCollector.hpp"
+#include "aare/ClusterFileSink.hpp"
+#include "aare/ClusterFinder.hpp"
+#include "aare/ClusterFinderMT.hpp"
+#include "aare/ClusterVector.hpp"
+#include "aare/NDView.hpp"
+#include "aare/Pedestal.hpp"
+#include "np_helper.hpp"
+
+#include <cstdint>
+#include <filesystem>
+#include <pybind11/pybind11.h>
+#include <pybind11/stl.h>
+#include <pybind11/stl_bind.h>
+
+namespace py = pybind11;
+using pd_type = double;
+
+using namespace aare;
+
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wunused-parameter"
+
+template <typename T, uint8_t ClusterSizeX, uint8_t ClusterSizeY,
+          typename CoordType = uint16_t>
+void define_ClusterFinderMT(py::module &m,
+                                       const std::string &typestr) {
+    auto class_name = fmt::format("ClusterFinderMT_{}", typestr);
+
+    using ClusterType = Cluster<T, ClusterSizeX, ClusterSizeY, CoordType>;
+
+    py::class_<ClusterFinderMT<ClusterType, uint16_t, pd_type>>(
+        m, class_name.c_str())
+        .def(py::init<Shape<2>, pd_type, size_t, size_t>(),
+             py::arg("image_size"), py::arg("n_sigma") = 5.0,
+             py::arg("capacity") = 2048, py::arg("n_threads") = 3)
+        .def("push_pedestal_frame",
+             [](ClusterFinderMT<ClusterType, uint16_t, pd_type> &self,
+                py::array_t<uint16_t> frame) {
+                 auto view = make_view_2d(frame);
+                 self.push_pedestal_frame(view);
+             })
+        .def(
+            "find_clusters",
+            [](ClusterFinderMT<ClusterType, uint16_t, pd_type> &self,
+               py::array_t<uint16_t> frame, uint64_t frame_number) {
+                auto view = make_view_2d(frame);
+                self.find_clusters(view, frame_number);
+                return;
+            },
+            py::arg(), py::arg("frame_number") = 0)
+        .def_property_readonly("cluster_size", [](ClusterFinderMT<ClusterType, uint16_t, pd_type> &self){
+            return py::make_tuple(ClusterSizeX, ClusterSizeY);
+        })
+        .def("clear_pedestal",
+             &ClusterFinderMT<ClusterType, uint16_t, pd_type>::clear_pedestal)
+        .def("sync", &ClusterFinderMT<ClusterType, uint16_t, pd_type>::sync)
+        .def("stop", &ClusterFinderMT<ClusterType, uint16_t, pd_type>::stop)
+        .def("start", &ClusterFinderMT<ClusterType, uint16_t, pd_type>::start)
+        .def(
+            "pedestal",
+            [](ClusterFinderMT<ClusterType, uint16_t, pd_type> &self,
+               size_t thread_index) {
+                auto pd = new NDArray<pd_type, 2>{};
+                *pd = self.pedestal(thread_index);
+                return return_image_data(pd);
+            },
+            py::arg("thread_index") = 0)
+        .def(
+            "noise",
+            [](ClusterFinderMT<ClusterType, uint16_t, pd_type> &self,
+               size_t thread_index) {
+                auto arr = new NDArray<pd_type, 2>{};
+                *arr = self.noise(thread_index);
+                return return_image_data(arr);
+            },
+            py::arg("thread_index") = 0);
+}
+
+
+#pragma GCC diagnostic pop

python/src/bind_ClusterVector.hpp

@@ -0,0 +1,106 @@
+#include "aare/ClusterCollector.hpp"
+#include "aare/ClusterFileSink.hpp"
+#include "aare/ClusterFinder.hpp"
+#include "aare/ClusterFinderMT.hpp"
+#include "aare/ClusterVector.hpp"
+#include "aare/NDView.hpp"
+#include "aare/Pedestal.hpp"
+#include "np_helper.hpp"
+
+#include <cstdint>
+#include <filesystem>
+#include <pybind11/pybind11.h>
+#include <pybind11/stl.h>
+#include <pybind11/stl_bind.h>
+
+namespace py = pybind11;
+using pd_type = double;
+
+using namespace aare;
+
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wunused-parameter"
+
+template <typename Type, uint8_t ClusterSizeX, uint8_t ClusterSizeY,
+          typename CoordType = uint16_t>
+void define_ClusterVector(py::module &m, const std::string &typestr) {
+    using ClusterType = Cluster<Type, ClusterSizeX, ClusterSizeY, CoordType>;
+    auto class_name = fmt::format("ClusterVector_{}", typestr);
+
+    py::class_<ClusterVector<
+        Cluster<Type, ClusterSizeX, ClusterSizeY, CoordType>, void>>(
+        m, class_name.c_str(),
+        py::buffer_protocol())
+
+        .def(py::init()) // TODO change!!!
+
+        .def("push_back",
+             [](ClusterVector<ClusterType> &self, const ClusterType &cluster) {
+                 self.push_back(cluster);
+             })
+
+        .def("sum",
+             [](ClusterVector<ClusterType> &self) {
+                 auto *vec = new std::vector<Type>(self.sum());
+                 return return_vector(vec);
+             })
+        .def("sum_2x2", [](ClusterVector<ClusterType> &self){
+            auto *vec = new std::vector<Type>(self.sum_2x2());
+            return return_vector(vec);
+        })
+        .def_property_readonly("size", &ClusterVector<ClusterType>::size)
+        .def("item_size", &ClusterVector<ClusterType>::item_size)
+        .def_property_readonly("fmt",
+                               [typestr](ClusterVector<ClusterType> &self) {
+                                   return fmt_format<ClusterType>;
+                               })
+
+        .def_property_readonly("cluster_size_x",
+                               &ClusterVector<ClusterType>::cluster_size_x)
+        .def_property_readonly("cluster_size_y",
+                               &ClusterVector<ClusterType>::cluster_size_y)
+        .def_property_readonly("capacity",
+                               &ClusterVector<ClusterType>::capacity)
+        .def_property("frame_number", &ClusterVector<ClusterType>::frame_number,
+                      &ClusterVector<ClusterType>::set_frame_number)
+        .def_buffer(
+            [typestr](ClusterVector<ClusterType> &self) -> py::buffer_info {
+                return py::buffer_info(
+                    self.data(),             /* Pointer to buffer */
+                    self.item_size(),        /* Size of one scalar */
+                    fmt_format<ClusterType>, /* Format descriptor */
+                    1,                       /* Number of dimensions */
+                    {self.size()},           /* Buffer dimensions */
+                    {self.item_size()} /* Strides (in bytes) for each index */
+                );
+            });
+
+    // Free functions using ClusterVector
+    m.def("hitmap",
+          [](std::array<size_t, 2> image_size, ClusterVector<ClusterType> &cv) {
+              // Create a numpy array to hold the hitmap
+              // The shape of the array is (image_size[0], image_size[1])
+              // note that the python array is passed as [row, col] which
+              // is the opposite of the clusters [x,y]
+              py::array_t<int32_t> hitmap(image_size);
+              auto r = hitmap.mutable_unchecked<2>();
+
+              // Initialize hitmap to 0
+              for (py::ssize_t i = 0; i < r.shape(0); i++)
+                  for (py::ssize_t j = 0; j < r.shape(1); j++)
+                      r(i, j) = 0;
+
+              // Loop over the clusters and increment the hitmap
+              // Skip out of bound clusters
+              for (const auto &cluster : cv) {
+                  auto x = cluster.x;
+                  auto y = cluster.y;
+                  if (x < image_size[1] && y < image_size[0])
+                      r(cluster.y, cluster.x) += 1;
+              }
+
+              return hitmap;
+          });
+}
+
+#pragma GCC diagnostic pop

python/src/cluster.hpp

@@ -1,124 +0,0 @@
-#include "aare/ClusterFinder.hpp"
-#include "aare/ClusterVector.hpp"
-#include "aare/NDView.hpp"
-#include "aare/Pedestal.hpp"
-#include "np_helper.hpp"
-
-#include <cstdint>
-#include <filesystem>
-#include <pybind11/pybind11.h>
-#include <pybind11/stl.h>
-
-namespace py = pybind11;
-using pd_type = float;
-
-template <typename T>
-void define_cluster_vector(py::module &m, const std::string &typestr) {
-    auto class_name = fmt::format("ClusterVector_{}", typestr);
-    py::class_<ClusterVector<T>>(m, class_name.c_str(), py::buffer_protocol())
-        .def(py::init<int, int>())
-        .def_property_readonly("size", &ClusterVector<T>::size)
-        .def("element_offset",
-             py::overload_cast<>(&ClusterVector<T>::element_offset, py::const_))
-        .def_property_readonly("fmt",
-             [typestr](ClusterVector<T> &self) {
-                 return fmt::format(
-                     self.fmt_base(), self.cluster_size_x(),
-                     self.cluster_size_y(), typestr);
-             })
-        .def("sum", [](ClusterVector<T> &self) {
-            auto *vec = new std::vector<T>(self.sum());
-            return return_vector(vec);
-        })
-        .def_buffer([typestr](ClusterVector<T> &self) -> py::buffer_info {
-            return py::buffer_info(
-                self.data(),           /* Pointer to buffer */
-                self.element_offset(), /* Size of one scalar */
-                fmt::format(self.fmt_base(), self.cluster_size_x(),
-                            self.cluster_size_y(),
-                            typestr), /* Format descriptor */
-                1,                    /* Number of dimensions */
-                {self.size()},           /* Buffer dimensions */
-                {self.element_offset()}  /* Strides (in bytes) for each index */
-            );
-        });
-}
-
-void define_cluster_finder_bindings(py::module &m) {
-    py::class_<ClusterFinder<uint16_t, pd_type>>(m, "ClusterFinder")
-        .def(py::init<Shape<2>, Shape<2>, pd_type, size_t>(), py::arg("image_size"),
-             py::arg("cluster_size"), py::arg("n_sigma") = 5.0,
-             py::arg("capacity") = 1'000'000)
-        .def("push_pedestal_frame",
-             [](ClusterFinder<uint16_t, pd_type> &self,
-                py::array_t<uint16_t> frame) {
-                 auto view = make_view_2d(frame);
-                 self.push_pedestal_frame(view);
-             })
-        .def("pedestal",
-             [](ClusterFinder<uint16_t, pd_type> &self) {
-                 auto pd = new NDArray<pd_type, 2>{};
-                 *pd = self.pedestal();
-                 return return_image_data(pd);
-             })
-        .def("noise",
-             [](ClusterFinder<uint16_t, pd_type> &self) {
-                 auto arr = new NDArray<pd_type, 2>{};
-                 *arr = self.noise();
-                 return return_image_data(arr);
-             })
-        .def("steal_clusters",
-             [](ClusterFinder<uint16_t, pd_type> &self, bool realloc_same_capacity) {
-                 auto v = new ClusterVector<int>(self.steal_clusters(realloc_same_capacity));
-                 return v;
-             }, py::arg("realloc_same_capacity") = false)
-        .def("find_clusters",
-             [](ClusterFinder<uint16_t, pd_type> &self,
-                py::array_t<uint16_t> frame) {
-                 auto view = make_view_2d(frame);
-                 self.find_clusters(view);
-                 return;
-             });
-
-    m.def("hitmap", [](std::array<size_t, 2> image_size, ClusterVector<int32_t>& cv){
-        
-        py::array_t<int32_t> hitmap(image_size);
-        auto r = hitmap.mutable_unchecked<2>(); 
-
-        // Initialize hitmap to 0
-        for (py::ssize_t i = 0; i < r.shape(0); i++)
-            for (py::ssize_t j = 0; j < r.shape(1); j++)
-                r(i, j) = 0;
-
-        size_t stride = cv.element_offset();
-        auto ptr = cv.data();
-        for(size_t i=0; i<cv.size(); i++){
-            auto x = *reinterpret_cast<int16_t*>(ptr);
-            auto y = *reinterpret_cast<int16_t*>(ptr+sizeof(int16_t));
-            r(y, x) += 1;
-            ptr += stride;
-        }
-        return hitmap;
-    });
-    define_cluster_vector<int>(m, "i");
-    define_cluster_vector<double>(m, "d");
-    define_cluster_vector<float>(m, "f");
-
-
-    py::class_<DynamicCluster>(m, "DynamicCluster", py::buffer_protocol())
-        .def(py::init<int, int, Dtype>())
-        .def("size", &DynamicCluster::size)
-        .def("begin", &DynamicCluster::begin)
-        .def("end", &DynamicCluster::end)
-        .def_readwrite("x", &DynamicCluster::x)
-        .def_readwrite("y", &DynamicCluster::y)
-        .def_buffer([](DynamicCluster &c) -> py::buffer_info {
-            return py::buffer_info(c.data(), c.dt.bytes(), c.dt.format_descr(),
-                                   1, {c.size()}, {c.dt.bytes()});
-        })
-
-        .def("__repr__", [](const DynamicCluster &a) {
-            return "<DynamicCluster: x: " + std::to_string(a.x) +
-                   ", y: " + std::to_string(a.y) + ">";
-        });
-}

python/src/cluster_file.hpp

@@ -1,76 +0,0 @@
-#include "aare/ClusterFile.hpp"
-#include "aare/defs.hpp"
-
-#include <cstdint>
-#include <filesystem>
-#include <pybind11/iostream.h>
-#include <pybind11/numpy.h>
-#include <pybind11/pybind11.h>
-#include <pybind11/stl.h>
-#include <pybind11/stl/filesystem.h>
-#include <string>
-
-//Disable warnings for unused parameters, as we ignore some
-//in the __exit__ method
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wunused-parameter"
-
-
-namespace py = pybind11;
-using namespace ::aare;
-
-void define_cluster_file_io_bindings(py::module &m) {
-    PYBIND11_NUMPY_DTYPE(Cluster3x3, x, y, data);
-
-    py::class_<ClusterFile>(m, "ClusterFile")
-        .def(py::init<const std::filesystem::path &, size_t,
-                      const std::string &>(),
-             py::arg(), py::arg("chunk_size") = 1000, py::arg("mode") = "r")
-        .def("read_clusters",
-             [](ClusterFile &self, size_t n_clusters) {
-                 auto *vec =
-                     new std::vector<Cluster3x3>(self.read_clusters(n_clusters));
-                 return return_vector(vec);
-             })
-        .def("read_frame",
-             [](ClusterFile &self) {
-                 int32_t frame_number;
-                 auto *vec =
-                     new std::vector<Cluster3x3>(self.read_frame(frame_number));
-                 return py::make_tuple(frame_number, return_vector(vec));
-             })
-        .def("write_frame", &ClusterFile::write_frame)
-        .def("read_cluster_with_cut",
-             [](ClusterFile &self, size_t n_clusters,
-                py::array_t<double> noise_map, int nx, int ny) {
-                 auto view = make_view_2d(noise_map);
-                 auto *vec =
-                     new std::vector<Cluster3x3>(self.read_cluster_with_cut(
-                         n_clusters, view.data(), nx, ny));
-                 return return_vector(vec);
-             })
-        .def("__enter__", [](ClusterFile &self) { return &self; })
-        .def("__exit__",
-             [](ClusterFile &self,
-                const std::optional<pybind11::type> &exc_type,
-                const std::optional<pybind11::object> &exc_value,
-                const std::optional<pybind11::object> &traceback) {
-                 self.close();
-             })
-        .def("__iter__", [](ClusterFile &self) { return &self; })
-        .def("__next__", [](ClusterFile &self) {
-            auto vec =
-                new std::vector<Cluster3x3>(self.read_clusters(self.chunk_size()));
-            if (vec->size() == 0) {
-                throw py::stop_iteration();
-            }
-            return return_vector(vec);
-        });
-
-        m.def("calculate_eta2", []( aare::ClusterVector<int32_t> &clusters) {
-            auto eta2 = new NDArray<double, 2>(calculate_eta2(clusters));
-            return return_image_data(eta2);
-        });
-}
-
-#pragma GCC diagnostic pop

python/src/ctb_raw_file.hpp

@@ -7,8 +7,11 @@
 #include "aare/RawSubFile.hpp"
 
 #include "aare/defs.hpp"
+#include "aare/decode.hpp"
 // #include "aare/fClusterFileV2.hpp"
 
+#include "np_helper.hpp"
+
 #include <cstdint>
 #include <filesystem>
 #include <pybind11/iostream.h>
@@ -23,35 +26,95 @@ using namespace ::aare;
 
 void define_ctb_raw_file_io_bindings(py::module &m) {
 
- py::class_<CtbRawFile>(m, "CtbRawFile")
-        .def(py::init<const std::filesystem::path &>())
-        .def("read_frame",
-             [](CtbRawFile &self) {
-                 size_t image_size = self.image_size_in_bytes();
-                 py::array image;
-                 std::vector<ssize_t> shape;
-                 shape.reserve(2);
-                 shape.push_back(1);
-                 shape.push_back(image_size);
+m.def("adc_sar_05_decode64to16", [](py::array_t<uint8_t> input) {
 
-                 py::array_t<DetectorHeader> header(1);
+    
+    if(input.ndim() != 2){
+        throw std::runtime_error("Only 2D arrays are supported at this moment"); 
+    }
 
-                 // always read bytes
-                 image = py::array_t<uint8_t>(shape);
+    //Create a 2D output array with the same shape as the input
+    std::vector<ssize_t> shape{input.shape(0), input.shape(1)/static_cast<ssize_t>(bits_per_byte)};
+    py::array_t<uint16_t> output(shape);
 
-                 self.read_into(
-                     reinterpret_cast<std::byte *>(image.mutable_data()),
-                     header.mutable_data());
+    //Create a view of the input and output arrays
+    NDView<uint64_t, 2> input_view(reinterpret_cast<uint64_t*>(input.mutable_data()), {output.shape(0), output.shape(1)});
+    NDView<uint16_t, 2> output_view(output.mutable_data(), {output.shape(0), output.shape(1)});
 
-                 return py::make_tuple(header, image);
-             })
-        .def("seek", &CtbRawFile::seek)
-        .def("tell", &CtbRawFile::tell)
-        .def("master", &CtbRawFile::master)
+    adc_sar_05_decode64to16(input_view, output_view);
 
-        .def_property_readonly("image_size_in_bytes",
-                               &CtbRawFile::image_size_in_bytes)
+    return output;
+});
 
-        .def_property_readonly("frames_in_file", &CtbRawFile::frames_in_file);
 
-}
+m.def("adc_sar_04_decode64to16", [](py::array_t<uint8_t> input) {
+
+    
+    if(input.ndim() != 2){
+        throw std::runtime_error("Only 2D arrays are supported at this moment"); 
+    }
+
+    //Create a 2D output array with the same shape as the input
+    std::vector<ssize_t> shape{input.shape(0), input.shape(1)/static_cast<ssize_t>(bits_per_byte)};
+    py::array_t<uint16_t> output(shape);
+
+    //Create a view of the input and output arrays
+    NDView<uint64_t, 2> input_view(reinterpret_cast<uint64_t*>(input.mutable_data()), {output.shape(0), output.shape(1)});
+    NDView<uint16_t, 2> output_view(output.mutable_data(), {output.shape(0), output.shape(1)});
+
+    adc_sar_04_decode64to16(input_view, output_view);
+
+    return output;
+});
+
+m.def(
+    "apply_custom_weights",
+    [](py::array_t<uint16_t, py::array::c_style | py::array::forcecast> &input,
+       py::array_t<double, py::array::c_style | py::array::forcecast>
+           &weights) {
+        
+
+        // Create new array with same shape as the input array (uninitialized values)
+        py::buffer_info buf = input.request();
+        py::array_t<double> output(buf.shape);
+
+        // Use NDViews to call into the C++ library
+        auto weights_view = make_view_1d(weights);
+        NDView<uint16_t, 1> input_view(input.mutable_data(), {input.size()});
+        NDView<double, 1> output_view(output.mutable_data(), {output.size()});
+
+        apply_custom_weights(input_view, output_view, weights_view);
+        return output;
+    });
+
+py::class_<CtbRawFile>(m, "CtbRawFile")
+    .def(py::init<const std::filesystem::path &>())
+    .def("read_frame",
+         [](CtbRawFile &self) {
+             size_t image_size = self.image_size_in_bytes();
+             py::array image;
+             std::vector<ssize_t> shape;
+             shape.reserve(2);
+             shape.push_back(1);
+             shape.push_back(image_size);
+
+             py::array_t<DetectorHeader> header(1);
+
+             // always read bytes
+             image = py::array_t<uint8_t>(shape);
+
+             self.read_into(reinterpret_cast<std::byte *>(image.mutable_data()),
+                            header.mutable_data());
+
+             return py::make_tuple(header, image);
+         })
+    .def("seek", &CtbRawFile::seek)
+    .def("tell", &CtbRawFile::tell)
+    .def("master", &CtbRawFile::master)
+
+    .def_property_readonly("image_size_in_bytes",
+                           &CtbRawFile::image_size_in_bytes)
+
+    .def_property_readonly("frames_in_file", &CtbRawFile::frames_in_file);
+
+}

python/src/file.hpp

@@ -20,6 +20,14 @@
 namespace py = pybind11;
 using namespace ::aare;
 
+
+
+
+//Disable warnings for unused parameters, as we ignore some
+//in the __exit__ method
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wunused-parameter"
+
 void define_file_io_bindings(py::module &m) {
 
 
@@ -124,8 +132,41 @@ void define_file_io_bindings(py::module &m) {
             self.read_into(reinterpret_cast<std::byte *>(image.mutable_data()),
                            n_frames);
             return image;
+        })
+        .def("__enter__", [](File &self) { return &self; })
+        .def("__exit__",
+             [](File &self,
+                const std::optional<pybind11::type> &exc_type,
+                const std::optional<pybind11::object> &exc_value,
+                const std::optional<pybind11::object> &traceback) {
+                //  self.close();
+             })
+        .def("__iter__", [](File &self) { return &self; })
+        .def("__next__", [](File &self) {
+
+            try{
+                const uint8_t item_size = self.bytes_per_pixel();
+                 py::array image;
+                 std::vector<ssize_t> shape;
+                 shape.reserve(2);
+                 shape.push_back(self.rows());
+                 shape.push_back(self.cols());
+                 if (item_size == 1) {
+                     image = py::array_t<uint8_t>(shape);
+                 } else if (item_size == 2) {
+                     image = py::array_t<uint16_t>(shape);
+                 } else if (item_size == 4) {
+                     image = py::array_t<uint32_t>(shape);
+                 }
+                 self.read_into(
+                     reinterpret_cast<std::byte *>(image.mutable_data()));
+                 return image;
+            }catch(std::runtime_error &e){
+                throw py::stop_iteration();
+            }
         });
 
+
     py::class_<FileConfig>(m, "FileConfig")
         .def(py::init<>())
         .def_readwrite("rows", &FileConfig::rows)
@@ -157,6 +198,8 @@ void define_file_io_bindings(py::module &m) {
 
     py::class_<ROI>(m, "ROI")
         .def(py::init<>())
+        .def(py::init<ssize_t, ssize_t, ssize_t, ssize_t>(), py::arg("xmin"),
+             py::arg("xmax"), py::arg("ymin"), py::arg("ymax"))
         .def_readwrite("xmin", &ROI::xmin)
         .def_readwrite("xmax", &ROI::xmax)
         .def_readwrite("ymin", &ROI::ymin)
@@ -174,38 +217,11 @@ void define_file_io_bindings(py::module &m) {
     
 
 
-    py::class_<RawSubFile>(m, "RawSubFile")
-        .def(py::init<const std::filesystem::path &, DetectorType, size_t,
-                      size_t, size_t>())
-        .def_property_readonly("bytes_per_frame", &RawSubFile::bytes_per_frame)
-        .def_property_readonly("pixels_per_frame",
-                               &RawSubFile::pixels_per_frame)
-        .def("seek", &RawSubFile::seek)
-        .def("tell", &RawSubFile::tell)
-        .def_property_readonly("rows", &RawSubFile::rows)
-        .def_property_readonly("cols", &RawSubFile::cols)
-        .def("read_frame",
-             [](RawSubFile &self) {
-                 const uint8_t item_size = self.bytes_per_pixel();
-                 py::array image;
-                 std::vector<ssize_t> shape;
-                 shape.reserve(2);
-                 shape.push_back(self.rows());
-                 shape.push_back(self.cols());
-                 if (item_size == 1) {
-                     image = py::array_t<uint8_t>(shape);
-                 } else if (item_size == 2) {
-                     image = py::array_t<uint16_t>(shape);
-                 } else if (item_size == 4) {
-                     image = py::array_t<uint32_t>(shape);
-                 }
-                 fmt::print("item_size: {} rows: {} cols: {}\n", item_size, self.rows(), self.cols());
-                 self.read_into(
-                     reinterpret_cast<std::byte *>(image.mutable_data()));
-                 return image;
-             });
 
 
+
+
+#pragma GCC diagnostic pop
     // py::class_<ClusterHeader>(m, "ClusterHeader")
     //     .def(py::init<>())
     //     .def_readwrite("frame_number", &ClusterHeader::frame_number)

python/src/fit.hpp

@@ -0,0 +1,465 @@
+#include <cstdint>
+#include <filesystem>
+#include <pybind11/pybind11.h>
+#include <pybind11/stl.h>
+#include <pybind11/stl_bind.h>
+
+#include "aare/Fit.hpp"
+
+namespace py = pybind11;
+using namespace pybind11::literals;
+
+
+void define_fit_bindings(py::module &m) {
+
+    // TODO! Evaluate without converting to double
+    m.def(
+        "gaus",
+        [](py::array_t<double, py::array::c_style | py::array::forcecast> x,
+           py::array_t<double, py::array::c_style | py::array::forcecast> par) {
+            auto x_view = make_view_1d(x);
+            auto par_view = make_view_1d(par);
+            auto y = new NDArray<double, 1>{aare::func::gaus(x_view, par_view)};
+            return return_image_data(y);
+        },
+        R"(
+        Evaluate a 1D Gaussian function for all points in x using parameters par.
+
+        Parameters
+        ----------
+        x : array_like
+            The points at which to evaluate the Gaussian function.
+        par : array_like
+            The parameters of the Gaussian function. The first element is the amplitude, the second element is the mean, and the third element is the standard deviation.
+        )",
+        py::arg("x"), py::arg("par"));
+
+    m.def(
+        "pol1",
+        [](py::array_t<double, py::array::c_style | py::array::forcecast> x,
+           py::array_t<double, py::array::c_style | py::array::forcecast> par) {
+            auto x_view = make_view_1d(x);
+            auto par_view = make_view_1d(par);
+            auto y = new NDArray<double, 1>{aare::func::pol1(x_view, par_view)};
+            return return_image_data(y);
+        },
+        R"(
+        Evaluate a 1D polynomial function for all points in x using parameters par. (p0+p1*x)
+        
+        Parameters
+        ----------
+        x : array_like
+            The points at which to evaluate the polynomial function.
+        par : array_like
+            The parameters of the polynomial function. The first element is the intercept, and the second element is the slope.    
+        )",
+        py::arg("x"), py::arg("par"));
+
+    m.def(
+        "scurve",
+        [](py::array_t<double, py::array::c_style | py::array::forcecast> x,
+           py::array_t<double, py::array::c_style | py::array::forcecast> par) {
+            auto x_view = make_view_1d(x);
+            auto par_view = make_view_1d(par);
+            auto y = new NDArray<double, 1>{aare::func::scurve(x_view, par_view)};
+            return return_image_data(y);
+        },
+        R"(
+        Evaluate a 1D scurve function for all points in x using parameters par. 
+        
+        Parameters
+        ----------
+        x : array_like
+            The points at which to evaluate the scurve function.
+        par : array_like
+            The parameters of the scurve function. The first element is the background slope, the second element is the background intercept, the third element is the mean, the fourth element is the standard deviation, the fifth element is inflexion point count number, and the sixth element is C.     
+        )",
+        py::arg("x"), py::arg("par"));
+
+    m.def(
+        "scurve2",
+        [](py::array_t<double, py::array::c_style | py::array::forcecast> x,
+           py::array_t<double, py::array::c_style | py::array::forcecast> par) {
+            auto x_view = make_view_1d(x);
+            auto par_view = make_view_1d(par);
+            auto y = new NDArray<double, 1>{aare::func::scurve2(x_view, par_view)};
+            return return_image_data(y);
+        },
+        R"(
+        Evaluate a 1D scurve2 function for all points in x using parameters par. 
+        
+        Parameters
+        ----------
+        x : array_like
+            The points at which to evaluate the scurve function.
+        par : array_like
+            The parameters of the scurve2 function. The first element is the background slope, the second element is the background intercept, the third element is the mean, the fourth element is the standard deviation, the fifth element is inflexion point count number, and the sixth element is C.     
+        )",
+        py::arg("x"), py::arg("par"));
+
+    m.def(
+        "fit_gaus",
+        [](py::array_t<double, py::array::c_style | py::array::forcecast> x,
+           py::array_t<double, py::array::c_style | py::array::forcecast> y,
+           int n_threads) {
+            if (y.ndim() == 3) {
+                auto par = new NDArray<double, 3>{};
+                auto y_view = make_view_3d(y);
+                auto x_view = make_view_1d(x);
+                *par = aare::fit_gaus(x_view, y_view, n_threads);
+                return return_image_data(par);
+            } else if (y.ndim() == 1) {
+                auto par = new NDArray<double, 1>{};
+                auto y_view = make_view_1d(y);
+                auto x_view = make_view_1d(x);
+                *par = aare::fit_gaus(x_view, y_view);
+                return return_image_data(par);
+            } else {
+                throw std::runtime_error("Data must be 1D or 3D");
+            }
+        },
+        R"(
+
+Fit a 1D Gaussian to data.
+
+Parameters
+----------
+x : array_like
+    The x values.
+y : array_like
+    The y values.
+n_threads : int, optional
+    The number of threads to use. Default is 4.
+)",
+        py::arg("x"), py::arg("y"), py::arg("n_threads") = 4);
+
+    m.def(
+        "fit_gaus",
+        [](py::array_t<double, py::array::c_style | py::array::forcecast> x,
+           py::array_t<double, py::array::c_style | py::array::forcecast> y,
+           py::array_t<double, py::array::c_style | py::array::forcecast> y_err,
+           int n_threads) {
+
+            if (y.ndim() == 3) {
+                // Allocate memory for the output
+                // Need to have pointers to allow python to manage
+                // the memory
+                auto par = new NDArray<double, 3>({y.shape(0), y.shape(1), 3});
+                auto par_err =
+                    new NDArray<double, 3>({y.shape(0), y.shape(1), 3});
+                auto chi2 = new NDArray<double, 2>({y.shape(0), y.shape(1)});
+
+                // Make views of the numpy arrays
+                auto y_view = make_view_3d(y);
+                auto y_view_err = make_view_3d(y_err);
+                auto x_view = make_view_1d(x);
+
+                aare::fit_gaus(x_view, y_view, y_view_err, par->view(),
+                               par_err->view(), chi2->view(), n_threads);
+
+                return py::dict("par"_a = return_image_data(par),
+                                "par_err"_a = return_image_data(par_err),
+                                "chi2"_a = return_image_data(chi2),
+                                "Ndf"_a = y.shape(2) - 3);
+            } else if (y.ndim() == 1) {
+                // Allocate memory for the output
+                // Need to have pointers to allow python to manage
+                // the memory
+                auto par = new NDArray<double, 1>({3});
+                auto par_err = new NDArray<double, 1>({3});
+
+                // Decode the numpy arrays
+                auto y_view = make_view_1d(y);
+                auto y_view_err = make_view_1d(y_err);
+                auto x_view = make_view_1d(x);
+
+
+                double chi2 = 0;
+                aare::fit_gaus(x_view, y_view, y_view_err, par->view(),
+                               par_err->view(), chi2);
+
+                return py::dict("par"_a = return_image_data(par),
+                                "par_err"_a = return_image_data(par_err),
+                                "chi2"_a = chi2, "Ndf"_a = y.size() - 3);
+
+            } else {
+                throw std::runtime_error("Data must be 1D or 3D");
+            }
+        },
+        R"(
+
+Fit a 1D Gaussian to data with error estimates.
+
+Parameters
+----------
+x : array_like
+    The x values.
+y : array_like
+    The y values.
+y_err : array_like
+    The error in the y values.
+n_threads : int, optional
+    The number of threads to use. Default is 4.
+)",
+        py::arg("x"), py::arg("y"), py::arg("y_err"), py::arg("n_threads") = 4);
+
+    m.def(
+        "fit_pol1",
+        [](py::array_t<double, py::array::c_style | py::array::forcecast> x,
+           py::array_t<double, py::array::c_style | py::array::forcecast> y,
+           int n_threads) {
+            if (y.ndim() == 3) {
+                auto par = new NDArray<double, 3>{};
+
+                auto x_view = make_view_1d(x);
+                auto y_view = make_view_3d(y);
+                *par = aare::fit_pol1(x_view, y_view, n_threads);
+                return return_image_data(par);
+            } else if (y.ndim() == 1) {
+                auto par = new NDArray<double, 1>{};
+                auto x_view = make_view_1d(x);
+                auto y_view = make_view_1d(y);
+                *par = aare::fit_pol1(x_view, y_view);
+                return return_image_data(par);
+            } else {
+                throw std::runtime_error("Data must be 1D or 3D");
+            }
+        },
+        py::arg("x"), py::arg("y"), py::arg("n_threads") = 4);
+
+    m.def(
+        "fit_pol1",
+        [](py::array_t<double, py::array::c_style | py::array::forcecast> x,
+           py::array_t<double, py::array::c_style | py::array::forcecast> y,
+           py::array_t<double, py::array::c_style | py::array::forcecast> y_err,
+           int n_threads) {
+            if (y.ndim() == 3) {
+                auto par = new NDArray<double, 3>({y.shape(0), y.shape(1), 2});
+
+                auto par_err =
+                    new NDArray<double, 3>({y.shape(0), y.shape(1), 2});
+
+                auto y_view = make_view_3d(y);
+                auto y_view_err = make_view_3d(y_err);
+                auto x_view = make_view_1d(x);
+
+                auto chi2 = new NDArray<double, 2>({y.shape(0), y.shape(1)});
+
+                aare::fit_pol1(x_view, y_view, y_view_err, par->view(),
+                               par_err->view(), chi2->view(), n_threads);
+                return py::dict("par"_a = return_image_data(par),
+                                "par_err"_a = return_image_data(par_err), 
+                                "chi2"_a = return_image_data(chi2),
+                                "Ndf"_a = y.shape(2) - 2);
+
+
+            } else if (y.ndim() == 1) {
+                auto par = new NDArray<double, 1>({2});
+                auto par_err = new NDArray<double, 1>({2});
+
+                auto y_view = make_view_1d(y);
+                auto y_view_err = make_view_1d(y_err);
+                auto x_view = make_view_1d(x);
+
+                double chi2 = 0;
+
+                aare::fit_pol1(x_view, y_view, y_view_err, par->view(),
+                               par_err->view(), chi2);
+                return py::dict("par"_a = return_image_data(par),
+                                "par_err"_a = return_image_data(par_err),
+                                "chi2"_a = chi2, "Ndf"_a = y.size() - 2);
+
+            } else {
+                throw std::runtime_error("Data must be 1D or 3D");
+            }
+        },
+        R"(
+Fit a 1D polynomial to data with error estimates.
+
+Parameters
+----------
+x : array_like
+    The x values.
+y : array_like
+    The y values.
+y_err : array_like
+    The error in the y values.
+n_threads : int, optional
+    The number of threads to use. Default is 4.
+)",
+        py::arg("x"), py::arg("y"), py::arg("y_err"), py::arg("n_threads") = 4);
+
+//=========
+    m.def(
+        "fit_scurve",
+        [](py::array_t<double, py::array::c_style | py::array::forcecast> x,
+           py::array_t<double, py::array::c_style | py::array::forcecast> y,
+           int n_threads) {
+            if (y.ndim() == 3) {
+                auto par = new NDArray<double, 3>{};
+
+                auto x_view = make_view_1d(x);
+                auto y_view = make_view_3d(y);
+                *par = aare::fit_scurve(x_view, y_view, n_threads);
+                return return_image_data(par);
+            } else if (y.ndim() == 1) {
+                auto par = new NDArray<double, 1>{};
+                auto x_view = make_view_1d(x);
+                auto y_view = make_view_1d(y);
+                *par = aare::fit_scurve(x_view, y_view);
+                return return_image_data(par);
+            } else {
+                throw std::runtime_error("Data must be 1D or 3D");
+            }
+        },
+        py::arg("x"), py::arg("y"), py::arg("n_threads") = 4);
+
+    m.def(
+        "fit_scurve",
+        [](py::array_t<double, py::array::c_style | py::array::forcecast> x,
+           py::array_t<double, py::array::c_style | py::array::forcecast> y,
+           py::array_t<double, py::array::c_style | py::array::forcecast> y_err,
+           int n_threads) {
+            if (y.ndim() == 3) {
+                auto par = new NDArray<double, 3>({y.shape(0), y.shape(1), 6});
+
+                auto par_err =
+                    new NDArray<double, 3>({y.shape(0), y.shape(1), 6});
+
+                auto y_view = make_view_3d(y);
+                auto y_view_err = make_view_3d(y_err);
+                auto x_view = make_view_1d(x);
+
+                auto chi2 = new NDArray<double, 2>({y.shape(0), y.shape(1)});
+
+                aare::fit_scurve(x_view, y_view, y_view_err, par->view(),
+                               par_err->view(), chi2->view(), n_threads);
+                return py::dict("par"_a = return_image_data(par),
+                                "par_err"_a = return_image_data(par_err), 
+                                "chi2"_a = return_image_data(chi2),
+                                "Ndf"_a = y.shape(2) - 2);
+
+
+            } else if (y.ndim() == 1) {
+                auto par = new NDArray<double, 1>({2});
+                auto par_err = new NDArray<double, 1>({2});
+
+                auto y_view = make_view_1d(y);
+                auto y_view_err = make_view_1d(y_err);
+                auto x_view = make_view_1d(x);
+
+                double chi2 = 0;
+
+                aare::fit_scurve(x_view, y_view, y_view_err, par->view(),
+                               par_err->view(), chi2);
+                return py::dict("par"_a = return_image_data(par),
+                                "par_err"_a = return_image_data(par_err),
+                                "chi2"_a = chi2, "Ndf"_a = y.size() - 2);
+
+            } else {
+                throw std::runtime_error("Data must be 1D or 3D");
+            }
+        },
+        R"(
+Fit a 1D polynomial to data with error estimates.
+
+Parameters
+----------
+x : array_like
+    The x values.
+y : array_like
+    The y values.
+y_err : array_like
+    The error in the y values.
+n_threads : int, optional
+    The number of threads to use. Default is 4.
+)",
+        py::arg("x"), py::arg("y"), py::arg("y_err"), py::arg("n_threads") = 4);
+    
+
+    m.def(
+        "fit_scurve2",
+        [](py::array_t<double, py::array::c_style | py::array::forcecast> x,
+           py::array_t<double, py::array::c_style | py::array::forcecast> y,
+           int n_threads) {
+            if (y.ndim() == 3) {
+                auto par = new NDArray<double, 3>{};
+
+                auto x_view = make_view_1d(x);
+                auto y_view = make_view_3d(y);
+                *par = aare::fit_scurve2(x_view, y_view, n_threads);
+                return return_image_data(par);
+            } else if (y.ndim() == 1) {
+                auto par = new NDArray<double, 1>{};
+                auto x_view = make_view_1d(x);
+                auto y_view = make_view_1d(y);
+                *par = aare::fit_scurve2(x_view, y_view);
+                return return_image_data(par);
+            } else {
+                throw std::runtime_error("Data must be 1D or 3D");
+            }
+        },
+        py::arg("x"), py::arg("y"), py::arg("n_threads") = 4);
+
+    m.def(
+        "fit_scurve2",
+        [](py::array_t<double, py::array::c_style | py::array::forcecast> x,
+           py::array_t<double, py::array::c_style | py::array::forcecast> y,
+           py::array_t<double, py::array::c_style | py::array::forcecast> y_err,
+           int n_threads) {
+            if (y.ndim() == 3) {
+                auto par = new NDArray<double, 3>({y.shape(0), y.shape(1), 6});
+
+                auto par_err =
+                    new NDArray<double, 3>({y.shape(0), y.shape(1), 6});
+
+                auto y_view = make_view_3d(y);
+                auto y_view_err = make_view_3d(y_err);
+                auto x_view = make_view_1d(x);
+
+                auto chi2 = new NDArray<double, 2>({y.shape(0), y.shape(1)});
+
+                aare::fit_scurve2(x_view, y_view, y_view_err, par->view(),
+                               par_err->view(), chi2->view(), n_threads);
+                return py::dict("par"_a = return_image_data(par),
+                                "par_err"_a = return_image_data(par_err), 
+                                "chi2"_a = return_image_data(chi2),
+                                "Ndf"_a = y.shape(2) - 2);
+
+
+            } else if (y.ndim() == 1) {
+                auto par = new NDArray<double, 1>({6});
+                auto par_err = new NDArray<double, 1>({6});
+
+                auto y_view = make_view_1d(y);
+                auto y_view_err = make_view_1d(y_err);
+                auto x_view = make_view_1d(x);
+
+                double chi2 = 0;
+
+                aare::fit_scurve2(x_view, y_view, y_view_err, par->view(),
+                               par_err->view(), chi2);
+                return py::dict("par"_a = return_image_data(par),
+                                "par_err"_a = return_image_data(par_err),
+                                "chi2"_a = chi2, "Ndf"_a = y.size() - 2);
+
+            } else {
+                throw std::runtime_error("Data must be 1D or 3D");
+            }
+        },
+        R"(
+Fit a 1D polynomial to data with error estimates.
+
+Parameters
+----------
+x : array_like
+    The x values.
+y : array_like
+    The y values.
+y_err : array_like
+    The error in the y values.
+n_threads : int, optional
+    The number of threads to use. Default is 4.
+)",
+        py::arg("x"), py::arg("y"), py::arg("y_err"), py::arg("n_threads") = 4);
+}

python/src/interpolation.hpp

@@ -0,0 +1,82 @@
+#include "aare/Interpolator.hpp"
+#include "aare/NDArray.hpp"
+#include "aare/NDView.hpp"
+#include "np_helper.hpp"
+#include <cstdint>
+#include <filesystem>
+#include <pybind11/pybind11.h>
+#include <pybind11/stl.h>
+
+namespace py = pybind11;
+
+template <typename Type, uint8_t CoordSizeX, uint8_t CoordSizeY,
+          typename CoordType = uint16_t>
+void register_interpolate(py::class_<aare::Interpolator> &interpolator) {
+
+    using ClusterType = Cluster<Type, CoordSizeX, CoordSizeY, CoordType>;
+
+    interpolator.def("interpolate",
+                     [](aare::Interpolator &self,
+                        const ClusterVector<ClusterType> &clusters) {
+                         auto photons = self.interpolate<ClusterType>(clusters);
+                         auto *ptr = new std::vector<Photon>{photons};
+                         return return_vector(ptr);
+                     });
+}
+
+void define_interpolation_bindings(py::module &m) {
+
+    PYBIND11_NUMPY_DTYPE(aare::Photon, x, y, energy);
+
+    auto interpolator =
+        py::class_<aare::Interpolator>(m, "Interpolator")
+            .def(py::init([](py::array_t<double, py::array::c_style |
+                                                     py::array::forcecast>
+                                 etacube,
+                             py::array_t<double> xbins,
+                             py::array_t<double> ybins,
+                             py::array_t<double> ebins) {
+                return Interpolator(make_view_3d(etacube), make_view_1d(xbins),
+                                    make_view_1d(ybins), make_view_1d(ebins));
+            }))
+            .def("get_ietax",
+                 [](Interpolator &self) {
+                     auto *ptr = new NDArray<double, 3>{};
+                     *ptr = self.get_ietax();
+                     return return_image_data(ptr);
+                 })
+            .def("get_ietay", [](Interpolator &self) {
+                auto *ptr = new NDArray<double, 3>{};
+                *ptr = self.get_ietay();
+                return return_image_data(ptr);
+            });
+
+    register_interpolate<int, 3, 3, uint16_t>(interpolator);
+    register_interpolate<float, 3, 3, uint16_t>(interpolator);
+    register_interpolate<double, 3, 3, uint16_t>(interpolator);
+    register_interpolate<int, 2, 2, uint16_t>(interpolator);
+    register_interpolate<float, 2, 2, uint16_t>(interpolator);
+    register_interpolate<double, 2, 2, uint16_t>(interpolator);
+
+    // TODO! Evaluate without converting to double
+    m.def(
+        "hej",
+        []() {
+            // auto boost_histogram = py::module_::import("boost_histogram");
+            // py::object axis =
+            // boost_histogram.attr("axis").attr("Regular")(10, 0.0, 10.0);
+            // py::object histogram = boost_histogram.attr("Histogram")(axis);
+            // return histogram;
+            // return h;
+        },
+        R"(
+        Evaluate a 1D Gaussian function for all points in x using parameters par.
+
+        Parameters
+        ----------
+        x : array_like
+            The points at which to evaluate the Gaussian function.
+        par : array_like
+            The parameters of the Gaussian function. The first element is the amplitude, the second element is the mean, and the third element is the standard deviation.
+        )");
+}

python/src/jungfrau_data_file.hpp

@@ -0,0 +1,116 @@
+
+#include "aare/JungfrauDataFile.hpp"
+#include "aare/defs.hpp"
+
+#include <cstdint>
+#include <filesystem>
+#include <pybind11/iostream.h>
+#include <pybind11/numpy.h>
+#include <pybind11/pybind11.h>
+#include <pybind11/stl.h>
+#include <pybind11/stl/filesystem.h>
+#include <string>
+
+namespace py = pybind11;
+using namespace ::aare;
+
+// Disable warnings for unused parameters, as we ignore some
+// in the __exit__ method
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wunused-parameter"
+
+auto read_dat_frame(JungfrauDataFile &self) {
+    py::array_t<JungfrauDataHeader> header(1);
+    py::array_t<uint16_t> image({
+        self.rows(), 
+        self.cols() 
+    });
+
+    self.read_into(reinterpret_cast<std::byte *>(image.mutable_data()),
+                   header.mutable_data());
+
+    return py::make_tuple(header, image);
+}
+
+auto read_n_dat_frames(JungfrauDataFile &self, size_t n_frames) {
+    // adjust for actual frames left in the file
+    n_frames = std::min(n_frames, self.total_frames() - self.tell());
+    if (n_frames == 0) {
+        throw std::runtime_error("No frames left in file");
+    }
+
+    py::array_t<JungfrauDataHeader> header(n_frames);
+    py::array_t<uint16_t> image({
+        n_frames, self.rows(), 
+        self.cols()});
+
+    self.read_into(reinterpret_cast<std::byte *>(image.mutable_data()),
+                   n_frames, header.mutable_data());
+
+    return py::make_tuple(header, image);
+}
+
+void define_jungfrau_data_file_io_bindings(py::module &m) {
+    // Make the JungfrauDataHeader usable from numpy
+    PYBIND11_NUMPY_DTYPE(JungfrauDataHeader, framenum, bunchid);
+
+    py::class_<JungfrauDataFile>(m, "JungfrauDataFile")
+        .def(py::init<const std::filesystem::path &>())
+        .def("seek", &JungfrauDataFile::seek,
+             R"(
+               Seek to the given frame index.
+               )")
+        .def("tell", &JungfrauDataFile::tell,
+             R"(
+               Get the current frame index.
+               )")
+        .def_property_readonly("rows", &JungfrauDataFile::rows)
+        .def_property_readonly("cols", &JungfrauDataFile::cols)
+        .def_property_readonly("base_name", &JungfrauDataFile::base_name)
+        .def_property_readonly("bytes_per_frame",
+                               &JungfrauDataFile::bytes_per_frame)
+        .def_property_readonly("pixels_per_frame",
+                               &JungfrauDataFile::pixels_per_frame)
+        .def_property_readonly("bytes_per_pixel",
+                               &JungfrauDataFile::bytes_per_pixel)
+        .def_property_readonly("bitdepth", &JungfrauDataFile::bitdepth)
+        .def_property_readonly("current_file", &JungfrauDataFile::current_file)
+        .def_property_readonly("total_frames", &JungfrauDataFile::total_frames)
+        .def_property_readonly("n_files", &JungfrauDataFile::n_files)
+        .def("read_frame", &read_dat_frame,
+             R"(
+               Read a single frame from the file.
+               )")
+        .def("read_n", &read_n_dat_frames,
+             R"(
+               Read maximum n_frames frames from the file.
+               )")
+        .def(
+            "read",
+            [](JungfrauDataFile &self) {
+                self.seek(0);
+                auto n_frames = self.total_frames();
+                return read_n_dat_frames(self, n_frames);
+            },
+            R"(
+              Read all frames from the file. Seeks to the beginning before reading.
+              )")
+        .def("__enter__", [](JungfrauDataFile &self) { return &self; })
+        .def("__exit__",
+             [](JungfrauDataFile &self,
+                const std::optional<pybind11::type> &exc_type,
+                const std::optional<pybind11::object> &exc_value,
+                const std::optional<pybind11::object> &traceback) {
+                 //  self.close();
+             })
+        .def("__iter__", [](JungfrauDataFile &self) { return &self; })
+        .def("__next__", [](JungfrauDataFile &self) {
+            try {
+                return read_dat_frame(self);
+            } catch (std::runtime_error &e) {
+                throw py::stop_iteration();
+            }
+        });
+}
+
+#pragma GCC diagnostic pop

python/src/module.cpp

@@ -1,29 +1,83 @@
-//Files with bindings to the different classes
-#include "file.hpp"
-#include "raw_file.hpp"
-#include "ctb_raw_file.hpp"
-#include "raw_master_file.hpp"
-#include "var_cluster.hpp"
-#include "pixel_map.hpp"
-#include "pedestal.hpp"
-#include "cluster.hpp"
-#include "cluster_file.hpp"
+// Files with bindings to the different classes
 
-//Pybind stuff
+//New style file naming
+#include "bind_Cluster.hpp"
+#include "bind_ClusterCollector.hpp"
+#include "bind_ClusterFinder.hpp"
+#include "bind_ClusterFinderMT.hpp"
+#include "bind_ClusterFile.hpp"
+#include "bind_ClusterFileSink.hpp"
+#include "bind_ClusterVector.hpp"
+
+//TODO! migrate the other names
+#include "ctb_raw_file.hpp"
+#include "file.hpp"
+#include "fit.hpp"
+#include "interpolation.hpp"
+#include "raw_sub_file.hpp"
+#include "raw_master_file.hpp"
+#include "raw_file.hpp"
+#include "pixel_map.hpp"
+#include "var_cluster.hpp"
+#include "pedestal.hpp"
+#include "jungfrau_data_file.hpp"
+
+// Pybind stuff
 #include <pybind11/pybind11.h>
 #include <pybind11/stl.h>
 
 namespace py = pybind11;
 
+/* MACRO that defines Cluster bindings for a specific size and type
+
+T - Storage type of the cluster data (int, float, double)
+N - Number of rows in the cluster
+M - Number of columns in the cluster
+U - Type of the pixel data (e.g., uint16_t)
+TYPE_CODE - A character representing the type code (e.g., 'i' for int, 'd' for double, 'f' for float)
+
+*/
+#define DEFINE_CLUSTER_BINDINGS(T, N, M, U, TYPE_CODE) \
+    define_ClusterFile<T, N, M, U>(m, "Cluster" #N "x" #M #TYPE_CODE); \
+    define_ClusterVector<T, N, M, U>(m, "Cluster" #N "x" #M #TYPE_CODE); \
+    define_ClusterFinder<T, N, M, U>(m, "Cluster" #N "x" #M #TYPE_CODE); \
+    define_ClusterFinderMT<T, N, M, U>(m, "Cluster" #N "x" #M #TYPE_CODE); \
+    define_ClusterFileSink<T, N, M, U>(m, "Cluster" #N "x" #M #TYPE_CODE); \
+    define_ClusterCollector<T, N, M, U>(m, "Cluster" #N "x" #M #TYPE_CODE); \
+    define_Cluster<T, N, M, U>(m, #N "x" #M #TYPE_CODE); \
+    register_calculate_eta<T, N, M, U>(m);
+
 PYBIND11_MODULE(_aare, m) {
     define_file_io_bindings(m);
     define_raw_file_io_bindings(m);
+    define_raw_sub_file_io_bindings(m);
     define_ctb_raw_file_io_bindings(m);
     define_raw_master_file_bindings(m);
     define_var_cluster_finder_bindings(m);
     define_pixel_map_bindings(m);
     define_pedestal_bindings<double>(m, "Pedestal_d");
     define_pedestal_bindings<float>(m, "Pedestal_f");
-    define_cluster_finder_bindings(m);
-    define_cluster_file_io_bindings(m);
-}
+    define_fit_bindings(m);
+    define_interpolation_bindings(m);
+    define_jungfrau_data_file_io_bindings(m);
+
+    DEFINE_CLUSTER_BINDINGS(int, 3, 3, uint16_t, i);
+    DEFINE_CLUSTER_BINDINGS(double, 3, 3, uint16_t, d);
+    DEFINE_CLUSTER_BINDINGS(float, 3, 3, uint16_t, f);
+
+    DEFINE_CLUSTER_BINDINGS(int, 2, 2, uint16_t, i);
+    DEFINE_CLUSTER_BINDINGS(double, 2, 2, uint16_t, d);
+    DEFINE_CLUSTER_BINDINGS(float, 2, 2, uint16_t, f);
+
+    DEFINE_CLUSTER_BINDINGS(int, 5, 5, uint16_t, i);
+    DEFINE_CLUSTER_BINDINGS(double, 5, 5, uint16_t, d);
+    DEFINE_CLUSTER_BINDINGS(float, 5, 5, uint16_t, f);
+
+    DEFINE_CLUSTER_BINDINGS(int, 7, 7, uint16_t, i);
+    DEFINE_CLUSTER_BINDINGS(double, 7, 7, uint16_t, d);
+    DEFINE_CLUSTER_BINDINGS(float, 7, 7, uint16_t, f);
+
+    DEFINE_CLUSTER_BINDINGS(int, 9, 9, uint16_t, i);
+    DEFINE_CLUSTER_BINDINGS(double, 9, 9, uint16_t, d);
+    DEFINE_CLUSTER_BINDINGS(float, 9, 9, uint16_t, f);
+}

python/src/np_helper.hpp

@@ -10,9 +10,10 @@
 #include "aare/NDView.hpp"
 
 namespace py = pybind11;
+using namespace aare;
 
 // Pass image data back to python as a numpy array
-template <typename T, int64_t Ndim>
+template <typename T, ssize_t Ndim>
 py::array return_image_data(aare::NDArray<T, Ndim> *image) {
 
     py::capsule free_when_done(image, [](void *f) {
@@ -39,78 +40,47 @@ template <typename T> py::array return_vector(std::vector<T> *vec) {
                           free_when_done); // numpy array references this parent
 }
 
-// template <typename Reader> py::array do_read(Reader &r, size_t n_frames) {
-//     py::array image;
-//     if (n_frames == 0)
-//         n_frames = r.total_frames();
-
-//     std::array<ssize_t, 3> shape{static_cast<ssize_t>(n_frames), r.rows(),
-//                                  r.cols()};
-//     const uint8_t item_size = r.bytes_per_pixel();
-//     if (item_size == 1) {
-//         image = py::array_t<uint8_t, py::array::c_style | py::array::forcecast>(
-//             shape);
-//     } else if (item_size == 2) {
-//         image =
-//             py::array_t<uint16_t, py::array::c_style | py::array::forcecast>(
-//                 shape);
-//     } else if (item_size == 4) {
-//         image =
-//             py::array_t<uint32_t, py::array::c_style | py::array::forcecast>(
-//                 shape);
-//     }
-//     r.read_into(reinterpret_cast<std::byte *>(image.mutable_data()), n_frames);
-//     return image;
-// }
-
-// py::array return_frame(pl::Frame *ptr) {
-//     py::capsule free_when_done(ptr, [](void *f) {
-//         pl::Frame *foo = reinterpret_cast<pl::Frame *>(f);
-//         delete foo;
-//     });
-
-//     const uint8_t item_size = ptr->bytes_per_pixel();
-//     std::vector<ssize_t> shape;
-//     for (auto val : ptr->shape())
-//         if (val > 1)
-//             shape.push_back(val);
-
-//     std::vector<ssize_t> strides;
-//     if (shape.size() == 1)
-//         strides.push_back(item_size);
-//     else if (shape.size() == 2) {
-//         strides.push_back(item_size * shape[1]);
-//         strides.push_back(item_size);
-//     }
-
-//     if (item_size == 1)
-//         return py::array_t<uint8_t>(
-//             shape, strides,
-//             reinterpret_cast<uint8_t *>(ptr->data()), free_when_done);
-//     else if (item_size == 2)
-//         return py::array_t<uint16_t>(shape, strides,
-//                                      reinterpret_cast<uint16_t *>(ptr->data()),
-//                                      free_when_done);
-//     else if (item_size == 4)
-//         return py::array_t<uint32_t>(shape, strides,
-//                                      reinterpret_cast<uint32_t *>(ptr->data()),
-//                                      free_when_done);
-//     return {};
-// }
-
 // todo rewrite generic
-template <class T, int Flags> auto get_shape_3d(py::array_t<T, Flags> arr) {
+template <class T, int Flags>
+auto get_shape_3d(const py::array_t<T, Flags> &arr) {
     return aare::Shape<3>{arr.shape(0), arr.shape(1), arr.shape(2)};
 }
 
-template <class T, int Flags> auto make_view_3d(py::array_t<T, Flags> arr) {
+template <class T, int Flags> auto make_view_3d(py::array_t<T, Flags> &arr) {
     return aare::NDView<T, 3>(arr.mutable_data(), get_shape_3d<T, Flags>(arr));
 }
 
-template <class T, int Flags> auto get_shape_2d(py::array_t<T, Flags> arr) {
+template <class T, int Flags>
+auto get_shape_2d(const py::array_t<T, Flags> &arr) {
     return aare::Shape<2>{arr.shape(0), arr.shape(1)};
 }
 
-template <class T, int Flags> auto make_view_2d(py::array_t<T, Flags> arr) {
+template <class T, int Flags>
+auto get_shape_1d(const py::array_t<T, Flags> &arr) {
+    return aare::Shape<1>{arr.shape(0)};
+}
+
+template <class T, int Flags> auto make_view_2d(py::array_t<T, Flags> &arr) {
     return aare::NDView<T, 2>(arr.mutable_data(), get_shape_2d<T, Flags>(arr));
-}
+}
+template <class T, int Flags> auto make_view_1d(py::array_t<T, Flags> &arr) {
+    return aare::NDView<T, 1>(arr.mutable_data(), get_shape_1d<T, Flags>(arr));
+}
+
+template <typename ClusterType> struct fmt_format_trait; // forward declaration
+
+template <typename T, uint8_t ClusterSizeX, uint8_t ClusterSizeY,
+          typename CoordType>
+struct fmt_format_trait<Cluster<T, ClusterSizeX, ClusterSizeY, CoordType>> {
+
+    static std::string value() {
+        return fmt::format("T{{{}:x:{}:y:{}:data:}}",
+                           py::format_descriptor<CoordType>::format(),
+                           py::format_descriptor<CoordType>::format(),
+                           fmt::format("({},{}){}", ClusterSizeX, ClusterSizeY,
+                                       py::format_descriptor<T>::format()));
+    }
+};
+
+template <typename ClusterType>
+auto fmt_format = fmt_format_trait<ClusterType>::value();

python/src/raw_file.hpp

@@ -32,7 +32,7 @@ void define_raw_file_io_bindings(py::module &m) {
                  shape.push_back(self.cols());
 
                  // return headers from all subfiles
-                 py::array_t<DetectorHeader> header(self.n_mod());
+                 py::array_t<DetectorHeader> header(self.n_modules());
 
                  const uint8_t item_size = self.bytes_per_pixel();
                  if (item_size == 1) {
@@ -61,10 +61,10 @@ void define_raw_file_io_bindings(py::module &m) {
             
                 // return headers from all subfiles
                 py::array_t<DetectorHeader> header;
-                if (self.n_mod() == 1) {
+                if (self.n_modules() == 1) {
                     header = py::array_t<DetectorHeader>(n_frames);
                 } else {
-                    header = py::array_t<DetectorHeader>({self.n_mod(), n_frames});
+                    header = py::array_t<DetectorHeader>({self.n_modules(), n_frames});
                 }
                 // py::array_t<DetectorHeader> header({self.n_mod(), n_frames});
 
@@ -100,7 +100,7 @@ void define_raw_file_io_bindings(py::module &m) {
         .def_property_readonly("cols", &RawFile::cols)
         .def_property_readonly("bitdepth", &RawFile::bitdepth)
         .def_property_readonly("geometry", &RawFile::geometry)
-        .def_property_readonly("n_mod", &RawFile::n_mod)
+        .def_property_readonly("n_modules", &RawFile::n_modules)
         .def_property_readonly("detector_type", &RawFile::detector_type)
         .def_property_readonly("master", &RawFile::master);
 }

python/src/raw_sub_file.hpp

@@ -0,0 +1,110 @@
+#include "aare/CtbRawFile.hpp"
+#include "aare/File.hpp"
+#include "aare/Frame.hpp"
+#include "aare/RawFile.hpp"
+#include "aare/RawMasterFile.hpp"
+#include "aare/RawSubFile.hpp"
+
+#include "aare/defs.hpp"
+// #include "aare/fClusterFileV2.hpp"
+
+#include <cstdint>
+#include <filesystem>
+#include <pybind11/iostream.h>
+#include <pybind11/numpy.h>
+#include <pybind11/pybind11.h>
+#include <pybind11/stl.h>
+#include <pybind11/stl/filesystem.h>
+#include <string>
+
+namespace py = pybind11;
+using namespace ::aare;
+
+auto read_frame_from_RawSubFile(RawSubFile &self) {
+    py::array_t<DetectorHeader> header(1);
+    const uint8_t item_size = self.bytes_per_pixel();
+    std::vector<ssize_t> shape{static_cast<ssize_t>(self.rows()),
+        static_cast<ssize_t>(self.cols())};
+    
+    py::array image;
+    if (item_size == 1) {
+        image = py::array_t<uint8_t>(shape);
+    } else if (item_size == 2) {
+        image = py::array_t<uint16_t>(shape);
+    } else if (item_size == 4) {
+        image = py::array_t<uint32_t>(shape);
+    }
+    self.read_into(reinterpret_cast<std::byte *>(image.mutable_data()),
+                   header.mutable_data());
+
+    return py::make_tuple(header, image);
+}
+
+auto read_n_frames_from_RawSubFile(RawSubFile &self, size_t n_frames) {
+    py::array_t<DetectorHeader> header(n_frames);
+    const uint8_t item_size = self.bytes_per_pixel();
+    std::vector<ssize_t> shape{
+        static_cast<ssize_t>(n_frames), 
+        static_cast<ssize_t>(self.rows()),
+        static_cast<ssize_t>(self.cols())
+    };
+    
+    py::array image;
+    if (item_size == 1) {
+        image = py::array_t<uint8_t>(shape);
+    } else if (item_size == 2) {
+        image = py::array_t<uint16_t>(shape);
+    } else if (item_size == 4) {
+        image = py::array_t<uint32_t>(shape);
+    }
+    self.read_into(reinterpret_cast<std::byte *>(image.mutable_data()), n_frames,
+                   header.mutable_data());
+
+    return py::make_tuple(header, image);
+}
+
+
+//Disable warnings for unused parameters, as we ignore some
+//in the __exit__ method
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wunused-parameter"
+
+void define_raw_sub_file_io_bindings(py::module &m) {
+    py::class_<RawSubFile>(m, "RawSubFile")
+        .def(py::init<const std::filesystem::path &, DetectorType, size_t,
+                      size_t, size_t>())
+        .def_property_readonly("bytes_per_frame", &RawSubFile::bytes_per_frame)
+        .def_property_readonly("pixels_per_frame",
+                               &RawSubFile::pixels_per_frame)
+        .def_property_readonly("bytes_per_pixel", &RawSubFile::bytes_per_pixel)                    
+        .def("seek", &RawSubFile::seek)
+        .def("tell", &RawSubFile::tell)
+        .def_property_readonly("rows", &RawSubFile::rows)
+        .def_property_readonly("cols", &RawSubFile::cols)
+        .def_property_readonly("frames_in_file", &RawSubFile::frames_in_file)
+        .def("read_frame", &read_frame_from_RawSubFile)
+        .def("read_n", &read_n_frames_from_RawSubFile)
+        .def("read", [](RawSubFile &self){
+            self.seek(0);
+            auto n_frames = self.frames_in_file();
+            return read_n_frames_from_RawSubFile(self, n_frames);
+        })
+        .def("__enter__", [](RawSubFile &self) { return &self; })
+        .def("__exit__",
+             [](RawSubFile &self,
+                const std::optional<pybind11::type> &exc_type,
+                const std::optional<pybind11::object> &exc_value,
+                const std::optional<pybind11::object> &traceback) {
+             })
+        .def("__iter__", [](RawSubFile &self) { return &self; })
+        .def("__next__", [](RawSubFile &self) {
+            try {
+                return read_frame_from_RawSubFile(self);
+            } catch (std::runtime_error &e) {
+                throw py::stop_iteration();
+            }
+        });
+
+}
+
+#pragma GCC diagnostic pop

python/src/var_cluster.hpp

@@ -19,15 +19,24 @@ using namespace::aare;
 
 void define_var_cluster_finder_bindings(py::module &m) {
     PYBIND11_NUMPY_DTYPE(VarClusterFinder<double>::Hit, size, row, col,
-                         reserved, energy, max);
+                         reserved, energy, max, rows, cols, enes);
 
     py::class_<VarClusterFinder<double>>(m, "VarClusterFinder")
         .def(py::init<Shape<2>, double>())
         .def("labeled",
              [](VarClusterFinder<double> &self) {
-                 auto ptr = new NDArray<int, 2>(self.labeled());
+                 auto *ptr = new NDArray<int, 2>(self.labeled());
                  return return_image_data(ptr);
              })
+        .def("set_noiseMap",
+            [](VarClusterFinder<double> &self,
+                py::array_t<double, py::array::c_style | py::array::forcecast>
+                    noise_map) {
+                auto noise_map_span = make_view_2d(noise_map);
+                self.set_noiseMap(noise_map_span);
+            })
+        .def("set_peripheralThresholdFactor",
+             &VarClusterFinder<double>::set_peripheralThresholdFactor)
         .def("find_clusters",
              [](VarClusterFinder<double> &self,
                 py::array_t<double, py::array::c_style | py::array::forcecast>
@@ -35,6 +44,30 @@ void define_var_cluster_finder_bindings(py::module &m) {
                  auto view = make_view_2d(img);
                  self.find_clusters(view);
              })
+        .def("find_clusters_X",
+             [](VarClusterFinder<double> &self,
+                py::array_t<double, py::array::c_style | py::array::forcecast>
+                    img) {
+                 auto img_span = make_view_2d(img);
+                 self.find_clusters_X(img_span);
+             })
+        .def("single_pass",
+             [](VarClusterFinder<double> &self,
+                py::array_t<double, py::array::c_style | py::array::forcecast>
+                    img) {
+                 auto img_span = make_view_2d(img);
+                 self.single_pass(img_span);
+             })
+        .def("hits",
+             [](VarClusterFinder<double> &self) {
+                 auto ptr = new std::vector<VarClusterFinder<double>::Hit>(
+                     self.steal_hits());
+                 return return_vector(ptr);
+             })
+        .def("clear_hits",
+             [](VarClusterFinder<double> &self) {
+                 self.clear_hits();
+             })
         .def("steal_hits",
              [](VarClusterFinder<double> &self) {
                  auto ptr = new std::vector<VarClusterFinder<double>::Hit>(

python/tests/conftest.py

@@ -0,0 +1,34 @@
+import os
+from pathlib import Path
+import pytest
+
+
+
+def pytest_addoption(parser):
+    parser.addoption(
+        "--files", action="store_true", default=False, help="run slow tests"
+    )
+
+
+def pytest_configure(config):
+    config.addinivalue_line("markers", "files: mark test as needing image files to run")
+
+
+def pytest_collection_modifyitems(config, items):
+    if config.getoption("--files"):
+        return
+    skip = pytest.mark.skip(reason="need --files option to run")
+    for item in items:
+        if "files" in item.keywords:
+            item.add_marker(skip)
+
+
+@pytest.fixture
+def test_data_path():
+    env_value = os.environ.get("AARE_TEST_DATA")
+    if not env_value:
+        raise RuntimeError("Environment variable AARE_TEST_DATA is not set or is empty")
+
+    return Path(env_value)
+
+

python/tests/test_Cluster.py

@@ -0,0 +1,110 @@
+import pytest 
+import numpy as np
+
+from aare import _aare #import the C++ module
+from conftest import test_data_path
+
+
+def test_cluster_vector_can_be_converted_to_numpy():
+    cv = _aare.ClusterVector_Cluster3x3i()
+    arr = np.array(cv, copy=False)
+    assert arr.shape == (0,)  # 4 for x, y, size, energy and 9 for the cluster data
+
+
+def test_ClusterVector(): 
+    """Test ClusterVector""" 
+
+    clustervector = _aare.ClusterVector_Cluster3x3i()
+    assert clustervector.cluster_size_x == 3
+    assert clustervector.cluster_size_y == 3
+    assert clustervector.item_size() == 4+9*4
+    assert clustervector.frame_number == 0
+    assert clustervector.size == 0
+
+    cluster = _aare.Cluster3x3i(0,0,np.ones(9, dtype=np.int32))
+    
+    clustervector.push_back(cluster) 
+    assert clustervector.size == 1
+
+    with pytest.raises(TypeError):  # Or use the appropriate exception type
+        clustervector.push_back(_aare.Cluster2x2i(0,0,np.ones(4, dtype=np.int32)))
+
+    with pytest.raises(TypeError): 
+        clustervector.push_back(_aare.Cluster3x3f(0,0,np.ones(9, dtype=np.float32))) 
+
+def test_Interpolator(): 
+    """Test Interpolator"""
+
+    ebins = np.linspace(0,10, 20, dtype=np.float64)
+    xbins = np.linspace(0, 5, 30, dtype=np.float64)
+    ybins = np.linspace(0, 5, 30, dtype=np.float64)
+
+    etacube = np.zeros(shape=[30, 30, 20], dtype=np.float64)
+    interpolator = _aare.Interpolator(etacube, xbins, ybins, ebins)
+
+    assert interpolator.get_ietax().shape == (30,30,20)
+    assert interpolator.get_ietay().shape == (30,30,20)
+    clustervector = _aare.ClusterVector_Cluster3x3i()
+    
+    cluster = _aare.Cluster3x3i(0,0, np.ones(9, dtype=np.int32))
+    clustervector.push_back(cluster) 
+
+    interpolated_photons = interpolator.interpolate(clustervector)
+
+    assert interpolated_photons.size == 1
+
+    assert interpolated_photons[0]["x"] == -1 
+    assert interpolated_photons[0]["y"] == -1
+    assert interpolated_photons[0]["energy"] == 4 #eta_sum = 4, dx, dy = -1,-1 m_ietax = 0, m_ietay = 0
+
+    clustervector = _aare.ClusterVector_Cluster2x2i()
+
+    cluster = _aare.Cluster2x2i(0,0, np.ones(4, dtype=np.int32))
+    clustervector.push_back(cluster) 
+
+    interpolated_photons = interpolator.interpolate(clustervector)
+
+    assert interpolated_photons.size == 1
+
+    assert interpolated_photons[0]["x"] == 0
+    assert interpolated_photons[0]["y"] == 0
+    assert interpolated_photons[0]["energy"] == 4
+
+
+
+def test_calculate_eta(): 
+    """Calculate Eta""" 
+    clusters = _aare.ClusterVector_Cluster3x3i() 
+    clusters.push_back(_aare.Cluster3x3i(0,0, np.ones(9, dtype=np.int32)))
+    clusters.push_back(_aare.Cluster3x3i(0,0, np.array([1,1,1,2,2,2,3,3,3])))
+
+    eta2 = _aare.calculate_eta2(clusters)
+
+    assert eta2.shape == (2,2)
+    assert eta2[0,0] == 0.5
+    assert eta2[0,1] == 0.5
+    assert eta2[1,0] == 0.5
+    assert eta2[1,1] == 0.6 #1/5
+
+def test_cluster_finder(): 
+    """Test ClusterFinder""" 
+
+    clusterfinder = _aare.ClusterFinder_Cluster3x3i([100,100])
+
+    #frame = np.random.rand(100,100)
+    frame = np.zeros(shape=[100,100])
+
+    clusterfinder.find_clusters(frame)
+
+    clusters = clusterfinder.steal_clusters(False) #conversion does not work
+
+    assert clusters.size == 0
+
+
+
+
+
+
+
+
+

python/tests/test_ClusterFile.py

@@ -0,0 +1,64 @@
+
+import pytest 
+import numpy as np
+import boost_histogram as bh
+import time
+from pathlib import Path
+import pickle
+
+from aare import ClusterFile
+from conftest import test_data_path
+
+@pytest.mark.files
+def test_cluster_file(test_data_path): 
+    """Test ClusterFile""" 
+    f =  ClusterFile(test_data_path / "clust/single_frame_97_clustrers.clust") 
+    cv = f.read_clusters(10) #conversion does not work
+
+
+    assert cv.frame_number == 135
+    assert cv.size == 10
+
+    #Known data 
+    #frame_number, num_clusters   [135] 97
+    #[  1 200] [0 1 2 3 4 5 6 7 8]
+    #[  2 201] [ 9 10 11 12 13 14 15 16 17]
+    #[  3 202] [18 19 20 21 22 23 24 25 26]
+    #[  4 203] [27 28 29 30 31 32 33 34 35]
+    #[  5 204] [36 37 38 39 40 41 42 43 44]
+    #[  6 205] [45 46 47 48 49 50 51 52 53]
+    #[  7 206] [54 55 56 57 58 59 60 61 62]
+    #[  8 207] [63 64 65 66 67 68 69 70 71]
+    #[  9 208] [72 73 74 75 76 77 78 79 80]
+    #[ 10 209] [81 82 83 84 85 86 87 88 89]
+
+    #conversion to numpy array
+    arr = np.array(cv, copy = False)
+    
+    assert arr.size == 10
+    for i in range(10):
+        assert arr[i]['x'] == i+1
+
+@pytest.mark.files
+def test_read_clusters_and_fill_histogram(test_data_path): 
+    # Create the histogram
+    n_bins = 100
+    xmin = -100
+    xmax = 1e4
+    hist_aare = bh.Histogram(bh.axis.Regular(n_bins, xmin, xmax))
+
+    fname = test_data_path / "clust/beam_En700eV_-40deg_300V_10us_d0_f0_100.clust"
+
+    #Read clusters and fill the histogram with pixel values
+    with ClusterFile(fname, chunk_size = 10000) as f:
+        for clusters in f:
+            arr = np.array(clusters, copy = False)
+            hist_aare.fill(arr['data'].flat)
+
+
+    #Load the histogram from the pickle file
+    with open(fname.with_suffix('.pkl'), 'rb') as f:
+        hist_py = pickle.load(f)
+        
+    #Compare the two histograms
+    assert hist_aare == hist_py

python/tests/test_ClusterVector.py

@@ -0,0 +1,54 @@
+import pytest 
+import numpy as np
+import boost_histogram as bh
+import time
+from pathlib import Path
+import pickle
+
+from aare import ClusterFile
+from aare import _aare
+from conftest import test_data_path
+
+
+def test_create_cluster_vector():
+    cv = _aare.ClusterVector_Cluster3x3i()
+    assert cv.cluster_size_x == 3
+    assert cv.cluster_size_y == 3
+    assert cv.size == 0
+
+
+def test_push_back_on_cluster_vector():
+    cv = _aare.ClusterVector_Cluster2x2i()
+    assert cv.cluster_size_x == 2
+    assert cv.cluster_size_y == 2
+    assert cv.size == 0
+
+    cluster = _aare.Cluster2x2i(19, 22, np.ones(4, dtype=np.int32))
+    cv.push_back(cluster)
+    assert cv.size == 1
+
+    arr = np.array(cv, copy=False)
+    assert arr[0]['x'] == 19
+    assert arr[0]['y'] == 22
+
+
+def test_make_a_hitmap_from_cluster_vector():
+    cv = _aare.ClusterVector_Cluster3x3i()
+
+    # Push back 4 clusters with different positions
+    cv.push_back(_aare.Cluster3x3i(0, 0, np.ones(9, dtype=np.int32)))
+    cv.push_back(_aare.Cluster3x3i(1, 1, np.ones(9, dtype=np.int32)))
+    cv.push_back(_aare.Cluster3x3i(1, 1, np.ones(9, dtype=np.int32)))
+    cv.push_back(_aare.Cluster3x3i(2, 2, np.ones(9, dtype=np.int32)))
+
+    ref = np.zeros((5, 5), dtype=np.int32)
+    ref[0,0] = 1
+    ref[1,1] = 2
+    ref[2,2] = 1
+
+
+    img = _aare.hitmap((5,5), cv)
+    # print(img)
+    # print(ref)
+    assert (img == ref).all()
+    

python/tests/test_RawSubFile.py

@@ -0,0 +1,39 @@
+import pytest
+import numpy as np
+from aare import RawSubFile, DetectorType
+
+
+@pytest.mark.files
+def test_read_a_jungfrau_RawSubFile(test_data_path):
+
+    # Starting with f1 there is now 7 frames left in the series of files
+    with RawSubFile(test_data_path / "raw/jungfrau/jungfrau_single_d0_f1_0.raw", DetectorType.Jungfrau, 512, 1024, 16) as f:
+        assert f.frames_in_file == 7
+
+        headers, frames = f.read()
+
+    assert headers.size == 7
+    assert frames.shape == (7, 512, 1024)
+    
+
+    for i,h in zip(range(4,11,1), headers):
+        assert h["frameNumber"] == i
+
+    # Compare to canned data using numpy
+    data = np.load(test_data_path / "raw/jungfrau/jungfrau_single_0.npy")
+    assert np.all(data[3:] == frames)
+
+@pytest.mark.files
+def test_iterate_over_a_jungfrau_RawSubFile(test_data_path):
+
+    data = np.load(test_data_path / "raw/jungfrau/jungfrau_single_0.npy")
+
+    # Given the first subfile in a series we can read all frames from f0, f1, f2...fN
+    with RawSubFile(test_data_path / "raw/jungfrau/jungfrau_single_d0_f0_0.raw", DetectorType.Jungfrau, 512, 1024, 16) as f:
+        i = 0
+        for header, frame in f:
+            assert header["frameNumber"] == i+1
+            assert np.all(frame == data[i])
+            i += 1
+        assert i == 10
+        assert header["frameNumber"] == 10

python/tests/test_jungfrau_dat_files.py

@@ -0,0 +1,92 @@
+import pytest
+import numpy as np
+from aare import JungfrauDataFile
+
+@pytest.mark.files
+def test_jfungfrau_dat_read_number_of_frames(test_data_path):
+    with JungfrauDataFile(test_data_path / "dat/AldoJF500k_000000.dat") as dat_file:
+        assert dat_file.total_frames == 24
+
+    with JungfrauDataFile(test_data_path / "dat/AldoJF250k_000000.dat") as dat_file:
+        assert dat_file.total_frames == 53
+    
+    with JungfrauDataFile(test_data_path / "dat/AldoJF65k_000000.dat") as dat_file:
+        assert dat_file.total_frames == 113
+
+
+@pytest.mark.files
+def test_jfungfrau_dat_read_number_of_file(test_data_path):
+    with JungfrauDataFile(test_data_path / "dat/AldoJF500k_000000.dat") as dat_file:
+        assert dat_file.n_files == 4
+
+    with JungfrauDataFile(test_data_path / "dat/AldoJF250k_000000.dat") as dat_file:
+        assert dat_file.n_files == 7
+    
+    with JungfrauDataFile(test_data_path / "dat/AldoJF65k_000000.dat") as dat_file:
+        assert dat_file.n_files == 7
+
+
+@pytest.mark.files
+def test_read_module(test_data_path):
+    """
+    Read all frames from the series of .dat files. Compare to canned data in npz format. 
+    """
+
+    # Read all frames from the .dat file
+    with JungfrauDataFile(test_data_path / "dat/AldoJF500k_000000.dat") as f:
+        header, data = f.read()
+
+    #Sanity check
+    n_frames = 24
+    assert header.size == n_frames
+    assert data.shape == (n_frames, 512, 1024)
+
+    # Read reference data using numpy
+    with np.load(test_data_path / "dat/AldoJF500k.npz") as f:
+        ref_header = f["headers"]
+        ref_data = f["frames"]
+
+    # Check that the data is the same
+    assert np.all(ref_header == header)
+    assert np.all(ref_data == data)
+
+@pytest.mark.files
+def test_read_half_module(test_data_path):
+
+    # Read all frames from the .dat file
+    with JungfrauDataFile(test_data_path / "dat/AldoJF250k_000000.dat") as f:
+        header, data = f.read()
+
+    n_frames = 53
+    assert header.size == n_frames
+    assert data.shape == (n_frames, 256, 1024)
+
+    # Read reference data using numpy
+    with np.load(test_data_path / "dat/AldoJF250k.npz") as f:
+        ref_header = f["headers"]
+        ref_data = f["frames"]
+
+    # Check that the data is the same
+    assert np.all(ref_header == header)
+    assert np.all(ref_data == data)
+
+
+@pytest.mark.files
+def test_read_single_chip(test_data_path):
+
+    # Read all frames from the .dat file
+    with JungfrauDataFile(test_data_path / "dat/AldoJF65k_000000.dat") as f:
+        header, data = f.read()
+
+    n_frames = 113
+    assert header.size == n_frames
+    assert data.shape == (n_frames, 256, 256)
+
+    # Read reference data using numpy
+    with np.load(test_data_path / "dat/AldoJF65k.npz") as f:
+        ref_header = f["headers"]
+        ref_data = f["frames"]
+
+    # Check that the data is the same
+    assert np.all(ref_header == header)
+    assert np.all(ref_data == data)

src/CalculateEta.test.cpp

@@ -0,0 +1,127 @@
+/************************************************
+ * @file CalculateEta.test.cpp
+ * @short test case to calculate_eta2
+ ***********************************************/
+
+#include "aare/CalculateEta.hpp"
+#include "aare/Cluster.hpp"
+#include "aare/ClusterFile.hpp"
+
+// #include "catch.hpp"
+#include <array>
+#include <catch2/catch_all.hpp>
+#include <catch2/catch_test_macros.hpp>
+
+using namespace aare;
+
+using ClusterTypes =
+    std::variant<Cluster<int, 2, 2>, Cluster<int, 3, 3>, Cluster<int, 5, 5>,
+                 Cluster<int, 4, 2>, Cluster<int, 2, 3>>;
+
+auto get_test_parameters() {
+    return GENERATE(
+        std::make_tuple(ClusterTypes{Cluster<int, 2, 2>{0, 0, {1, 2, 3, 1}}},
+                        Eta2<int>{2. / 3, 3. / 4,
+                                  static_cast<int>(corner::cBottomLeft), 7}),
+        std::make_tuple(
+            ClusterTypes{Cluster<int, 3, 3>{0, 0, {1, 2, 3, 4, 5, 6, 1, 2, 7}}},
+            Eta2<int>{6. / 11, 2. / 7, static_cast<int>(corner::cTopRight),
+                      20}),
+        std::make_tuple(ClusterTypes{Cluster<int, 5, 5>{
+                            0, 0, {1, 6, 7, 6, 5, 4, 3, 2, 1, 2, 8, 9, 8,
+                                   1, 4, 5, 6, 7, 8, 4, 1, 1, 1, 1, 1}}},
+                        Eta2<int>{8. / 17, 7. / 15, 9, 30}),
+        std::make_tuple(
+            ClusterTypes{Cluster<int, 4, 2>{0, 0, {1, 4, 7, 2, 5, 6, 4, 3}}},
+            Eta2<int>{4. / 10, 4. / 11, 1, 21}),
+        std::make_tuple(
+            ClusterTypes{Cluster<int, 2, 3>{0, 0, {1, 3, 2, 3, 4, 2}}},
+            Eta2<int>{3. / 5, 2. / 5, 1, 11}));
+}
+
+TEST_CASE("compute_largest_2x2_subcluster", "[eta_calculation]") {
+    auto [cluster, expected_eta] = get_test_parameters();
+
+    auto [sum, index] = std::visit(
+        [](const auto &clustertype) { return clustertype.max_sum_2x2(); },
+        cluster);
+    CHECK(expected_eta.c == index);
+    CHECK(expected_eta.sum == sum);
+}
+
+TEST_CASE("calculate_eta2", "[eta_calculation]") {
+
+    auto [cluster, expected_eta] = get_test_parameters();
+
+    auto eta = std::visit(
+        [](const auto &clustertype) { return calculate_eta2(clustertype); },
+        cluster);
+
+    CHECK(eta.x == expected_eta.x);
+    CHECK(eta.y == expected_eta.y);
+    CHECK(eta.c == expected_eta.c);
+    CHECK(eta.sum == expected_eta.sum);
+}
+
+// 3x3 cluster layout (rotated to match the cBottomLeft enum):
+//  6, 7, 8
+//  3, 4, 5
+//  0, 1, 2
+
+TEST_CASE("Calculate eta2 for a 3x3 int32 cluster with the largest 2x2 sum in "
+          "the bottom left",
+          "[eta_calculation]") {
+
+    // Create a 3x3 cluster
+    Cluster<int32_t, 3, 3> cl;
+    cl.x = 0;
+    cl.y = 0;
+    cl.data[0] = 30;
+    cl.data[1] = 23;
+    cl.data[2] = 5;
+    cl.data[3] = 20;
+    cl.data[4] = 50;
+    cl.data[5] = 3;
+    cl.data[6] = 8;
+    cl.data[7] = 2;
+    cl.data[8] = 3;
+
+    // 8, 2, 3
+    // 20, 50, 3
+    // 30, 23, 5
+
+    auto eta = calculate_eta2(cl);
+    CHECK(eta.c == static_cast<int>(corner::cBottomLeft));
+    CHECK(eta.x == 50.0 / (20 + 50)); // 4/(3+4)
+    CHECK(eta.y == 50.0 / (23 + 50)); // 4/(1+4)
+    CHECK(eta.sum == 30 + 23 + 20 + 50);
+}
+
+TEST_CASE("Calculate eta2 for a 3x3 int32 cluster with the largest 2x2 sum in "
+          "the top left",
+          "[eta_calculation]") {
+
+    // Create a 3x3 cluster
+    Cluster<int32_t, 3, 3> cl;
+    cl.x = 0;
+    cl.y = 0;
+    cl.data[0] = 8;
+    cl.data[1] = 12;
+    cl.data[2] = 5;
+    cl.data[3] = 77;
+    cl.data[4] = 80;
+    cl.data[5] = 3;
+    cl.data[6] = 82;
+    cl.data[7] = 91;
+    cl.data[8] = 3;
+
+    // 82, 91, 3
+    // 77, 80, 3
+    // 8, 12, 5
+
+    auto eta = calculate_eta2(cl);
+    CHECK(eta.c == static_cast<int>(corner::cTopLeft));
+    CHECK(eta.x == 80. / (77 + 80));  // 4/(3+4)
+    CHECK(eta.y == 91.0 / (91 + 80)); // 7/(7+4)
+    CHECK(eta.sum == 77 + 80 + 82 + 91);
+}

src/Cluster.test.cpp

@@ -0,0 +1,21 @@
+/************************************************
+ * @file test-Cluster.cpp
+ * @short test case for generic Cluster, ClusterVector, and calculate_eta2
+ ***********************************************/
+
+#include "aare/Cluster.hpp"
+#include "aare/CalculateEta.hpp"
+#include "aare/ClusterFile.hpp"
+
+// #include "catch.hpp"
+#include <array>
+#include <catch2/catch_all.hpp>
+#include <catch2/catch_test_macros.hpp>
+
+using namespace aare;
+
+TEST_CASE("Test sum of Cluster", "[.cluster]") {
+    Cluster<int, 2, 2> cluster{0, 0, {1, 2, 3, 4}};
+
+    CHECK(cluster.sum() == 10);
+}

src/ClusterFile.cpp

@@ -20,11 +20,35 @@ ClusterFile::ClusterFile(const std::filesystem::path &fname, size_t chunk_size,
             throw std::runtime_error("Could not open file for writing: " +
                                      fname.string());
         }
+    } else if (mode == "a") {
+        fp = fopen(fname.c_str(), "ab");
+        if (!fp) {
+            throw std::runtime_error("Could not open file for appending: " +
+                                     fname.string());
+        }
     } else {
         throw std::runtime_error("Unsupported mode: " + mode);
     }
 }
 
+void ClusterFile::set_roi(ROI roi){
+    m_roi = roi;
+}
+
+void ClusterFile::set_noise_map(const NDView<int32_t, 2> noise_map){
+    m_noise_map = NDArray<int32_t, 2>(noise_map);
+}
+
+void ClusterFile::set_gain_map(const NDView<double, 2> gain_map){
+    m_gain_map = NDArray<double, 2>(gain_map);
+    
+    // Gain map is passed as ADU/keV to avoid dividing in when applying the gain
+    // map we invert it here
+    for (auto &item : m_gain_map->view()) {
+        item = 1.0 / item;
+    }
+}
+
 ClusterFile::~ClusterFile() { close(); }
 
 void ClusterFile::close() {
@@ -34,35 +58,58 @@ void ClusterFile::close() {
     }
 }
 
-void ClusterFile::write_frame(int32_t frame_number,
-                              const ClusterVector<int32_t> &clusters) {
-    if (m_mode != "w") {
+void ClusterFile::write_frame(const ClusterVector<int32_t> &clusters) {
+    if (m_mode != "w" && m_mode != "a") {
         throw std::runtime_error("File not opened for writing");
     }
     if (!(clusters.cluster_size_x() == 3) &&
         !(clusters.cluster_size_y() == 3)) {
         throw std::runtime_error("Only 3x3 clusters are supported");
     }
-    fwrite(&frame_number, sizeof(frame_number), 1, fp);
+    //First write the frame number - 4 bytes
+    int32_t frame_number = clusters.frame_number();
+    if(fwrite(&frame_number, sizeof(frame_number), 1, fp)!=1){
+        throw std::runtime_error(LOCATION + "Could not write frame number");
+    }
+
+    //Then write the number of clusters - 4 bytes
     uint32_t n_clusters = clusters.size();
-    fwrite(&n_clusters, sizeof(n_clusters), 1, fp);
-    fwrite(clusters.data(), clusters.element_offset(), clusters.size(), fp);
-    // write clusters
-    // fwrite(clusters.data(), sizeof(Cluster), clusters.size(), fp);
+    if(fwrite(&n_clusters, sizeof(n_clusters), 1, fp)!=1){
+        throw std::runtime_error(LOCATION + "Could not write number of clusters");
+    }
+
+    //Now write the clusters in the frame
+    if(fwrite(clusters.data(), clusters.item_size(), clusters.size(), fp)!=clusters.size()){
+        throw std::runtime_error(LOCATION + "Could not write clusters");
+    }
 }
 
-std::vector<Cluster3x3> ClusterFile::read_clusters(size_t n_clusters) {
+
+ClusterVector<int32_t> ClusterFile::read_clusters(size_t n_clusters){
     if (m_mode != "r") {
         throw std::runtime_error("File not opened for reading");
     }
-    std::vector<Cluster3x3> clusters(n_clusters);
+    if (m_noise_map || m_roi){
+        return read_clusters_with_cut(n_clusters);
+    }else{
+        return read_clusters_without_cut(n_clusters);
+    }
+}
+
+ClusterVector<int32_t> ClusterFile::read_clusters_without_cut(size_t n_clusters) {
+    if (m_mode != "r") {
+        throw std::runtime_error("File not opened for reading");
+    }
+    
+    ClusterVector<int32_t> clusters(3,3, n_clusters);
 
     int32_t iframe = 0; // frame number needs to be 4 bytes!
     size_t nph_read = 0;
     uint32_t nn = m_num_left;
     uint32_t nph = m_num_left; // number of clusters in frame needs to be 4
 
-    auto buf = reinterpret_cast<Cluster3x3 *>(clusters.data());
+    // auto buf = reinterpret_cast<Cluster3x3 *>(clusters.data());
+    auto buf = clusters.data();
     // if there are photons left from previous frame read them first
     if (nph) {
         if (nph > n_clusters) {
@@ -72,14 +119,15 @@ std::vector<Cluster3x3> ClusterFile::read_clusters(size_t n_clusters) {
         } else {
             nn = nph;
         }
-        nph_read += fread(reinterpret_cast<void *>(buf + nph_read),
-                          sizeof(Cluster3x3), nn, fp);
+        nph_read += fread((buf + nph_read*clusters.item_size()),
+                          clusters.item_size(), nn, fp);
         m_num_left = nph - nn; // write back the number of photons left
     }
 
     if (nph_read < n_clusters) {
         // keep on reading frames and photons until reaching n_clusters
         while (fread(&iframe, sizeof(iframe), 1, fp)) {
+            clusters.set_frame_number(iframe);
             // read number of clusters in frame
             if (fread(&nph, sizeof(nph), 1, fp)) {
                 if (nph > (n_clusters - nph_read))
@@ -87,8 +135,8 @@ std::vector<Cluster3x3> ClusterFile::read_clusters(size_t n_clusters) {
                 else
                     nn = nph;
 
-                nph_read += fread(reinterpret_cast<void *>(buf + nph_read),
-                                  sizeof(Cluster3x3), nn, fp);
+                nph_read += fread((buf + nph_read*clusters.item_size()),
+                                  clusters.item_size(), nn, fp);
                 m_num_left = nph - nn;
             }
             if (nph_read >= n_clusters)
@@ -99,10 +147,80 @@ std::vector<Cluster3x3> ClusterFile::read_clusters(size_t n_clusters) {
     // Resize the vector to the number of clusters.
     // No new allocation, only change bounds.
     clusters.resize(nph_read);
+    if(m_gain_map)
+        clusters.apply_gain_map(m_gain_map->view());
     return clusters;
 }
 
-std::vector<Cluster3x3> ClusterFile::read_frame(int32_t &out_fnum) {
+
+ClusterVector<int32_t> ClusterFile::read_clusters_with_cut(size_t n_clusters) {
+    ClusterVector<int32_t> clusters(3,3);
+    clusters.reserve(n_clusters);
+
+    // if there are photons left from previous frame read them first
+    if (m_num_left) {
+        while(m_num_left && clusters.size() < n_clusters){
+            Cluster3x3 c = read_one_cluster();
+            if(is_selected(c)){
+                clusters.push_back(c.x, c.y, reinterpret_cast<std::byte*>(c.data));
+            }
+        }
+    }
+
+    // we did not have enough clusters left in the previous frame
+    // keep on reading frames until reaching n_clusters
+    if (clusters.size() < n_clusters) {
+        // sanity check
+        if (m_num_left) {
+            throw std::runtime_error(LOCATION + "Entered second loop with clusters left\n");
+        }
+        
+        int32_t frame_number = 0; // frame number needs to be 4 bytes!
+        while (fread(&frame_number, sizeof(frame_number), 1, fp)) {
+            if (fread(&m_num_left, sizeof(m_num_left), 1, fp)) {
+                clusters.set_frame_number(frame_number); //cluster vector will hold the last frame number
+                while(m_num_left && clusters.size() < n_clusters){
+                    Cluster3x3 c = read_one_cluster();
+                    if(is_selected(c)){
+                        clusters.push_back(c.x, c.y, reinterpret_cast<std::byte*>(c.data));
+                    }
+                }
+            }
+
+            // we have enough clusters, break out of the outer while loop
+            if (clusters.size() >= n_clusters)
+                break;
+        }
+
+    }
+    if(m_gain_map)
+        clusters.apply_gain_map(m_gain_map->view());
+
+    return clusters;
+}
+
+Cluster3x3 ClusterFile::read_one_cluster(){
+    Cluster3x3 c;
+    auto rc = fread(&c, sizeof(c), 1, fp);
+    if (rc != 1) {
+        throw std::runtime_error(LOCATION + "Could not read cluster");
+    }
+    --m_num_left;
+    return c;
+}
+
+ClusterVector<int32_t> ClusterFile::read_frame(){
+    if (m_mode != "r") {
+        throw std::runtime_error(LOCATION + "File not opened for reading");
+    }
+    if (m_noise_map || m_roi){
+        return read_frame_with_cut();
+    }else{
+        return read_frame_without_cut();
+    }
+}
+
+ClusterVector<int32_t> ClusterFile::read_frame_without_cut() {
     if (m_mode != "r") {
         throw std::runtime_error("File not opened for reading");
     }
@@ -110,167 +228,113 @@ std::vector<Cluster3x3> ClusterFile::read_frame(int32_t &out_fnum) {
         throw std::runtime_error(
             "There are still photons left in the last frame");
     }
-
-    if (fread(&out_fnum, sizeof(out_fnum), 1, fp) != 1) {
-        throw std::runtime_error("Could not read frame number");
+    int32_t frame_number;
+    if (fread(&frame_number, sizeof(frame_number), 1, fp) != 1) {
+        throw std::runtime_error(LOCATION + "Could not read frame number");
     }
 
     int32_t n_clusters; // Saved as 32bit integer in the cluster file
     if (fread(&n_clusters, sizeof(n_clusters), 1, fp) != 1) {
-        throw std::runtime_error("Could not read number of clusters");
+        throw std::runtime_error(LOCATION + "Could not read number of clusters");
     }
-    std::vector<Cluster3x3> clusters(n_clusters);
 
-    if (fread(clusters.data(), sizeof(Cluster3x3), n_clusters, fp) !=
+    ClusterVector<int32_t> clusters(3, 3, n_clusters);
+    clusters.set_frame_number(frame_number);
+
+    if (fread(clusters.data(), clusters.item_size(), n_clusters, fp) !=
         static_cast<size_t>(n_clusters)) {
-        throw std::runtime_error("Could not read clusters");
+        throw std::runtime_error(LOCATION + "Could not read clusters");
     }
+    clusters.resize(n_clusters);
+    if (m_gain_map)
+        clusters.apply_gain_map(m_gain_map->view());
     return clusters;
 }
 
-std::vector<Cluster3x3> ClusterFile::read_cluster_with_cut(size_t n_clusters,
-                                                           double *noise_map,
-                                                           int nx, int ny) {
+ClusterVector<int32_t> ClusterFile::read_frame_with_cut() {
     if (m_mode != "r") {
         throw std::runtime_error("File not opened for reading");
     }
-    std::vector<Cluster3x3> clusters(n_clusters);
-    // size_t read_clusters_with_cut(FILE *fp, size_t n_clusters, Cluster *buf,
-    //                               uint32_t *n_left, double *noise_map, int
-    //                               nx, int ny) {
-    int iframe = 0;
-    //     uint32_t nph = *n_left;
-    uint32_t nph = m_num_left;
-    //     uint32_t nn = *n_left;
-    uint32_t nn = m_num_left;
-    size_t nph_read = 0;
+    if (m_num_left) {
+        throw std::runtime_error(
+            "There are still photons left in the last frame");
+    }
+    int32_t frame_number;
+    if (fread(&frame_number, sizeof(frame_number), 1, fp) != 1) {
+        throw std::runtime_error("Could not read frame number");
+    }
 
-    int32_t t2max, tot1;
-    int32_t tot3;
-    // Cluster *ptr = buf;
-    Cluster3x3 *ptr = clusters.data();
-    int good = 1;
-    double noise;
-    // read photons left from previous frame
-    if (noise_map)
-        printf("Using noise map\n");
 
-    if (nph) {
-        if (nph > n_clusters) {
-            // if we have more photons left in the frame then photons to
-            // read we read directly the requested number
-            nn = n_clusters;
-        } else {
-            nn = nph;
-        }
-        for (size_t iph = 0; iph < nn; iph++) {
-            // read photons 1 by 1
-            size_t n_read =
-                fread(reinterpret_cast<void *>(ptr), sizeof(Cluster3x3), 1, fp);
-            if (n_read != 1) {
-                clusters.resize(nph_read);
-                return clusters;
-            }
-            // TODO! error handling on read
-            good = 1;
-            if (noise_map) {
-                if (ptr->x >= 0 && ptr->x < nx && ptr->y >= 0 && ptr->y < ny) {
-                    tot1 = ptr->data[4];
-                    analyze_cluster(*ptr, &t2max, &tot3, NULL, NULL, NULL, NULL,
-                                    NULL);
-                    noise = noise_map[ptr->y * nx + ptr->x];
-                    if (tot1 > noise || t2max > 2 * noise || tot3 > 3 * noise) {
-                        ;
-                    } else {
-                        good = 0;
-                        printf("%d %d %f %d %d %d\n", ptr->x, ptr->y, noise,
-                               tot1, t2max, tot3);
-                    }
-                } else {
-                    printf("Bad pixel number %d %d\n", ptr->x, ptr->y);
-                    good = 0;
-                }
-            }
-            if (good) {
-                ptr++;
-                nph_read++;
-            }
-            (m_num_left)--;
-            if (nph_read >= n_clusters)
-                break;
+    if (fread(&m_num_left, sizeof(m_num_left), 1, fp) != 1) {
+        throw std::runtime_error("Could not read number of clusters");
+    }
+    
+    ClusterVector<int32_t> clusters(3, 3);
+    clusters.reserve(m_num_left);
+    clusters.set_frame_number(frame_number);
+    while(m_num_left){
+        Cluster3x3 c = read_one_cluster();
+        if(is_selected(c)){
+            clusters.push_back(c.x, c.y, reinterpret_cast<std::byte*>(c.data));
         }
     }
-    if (nph_read < n_clusters) {
-        //         // keep on reading frames and photons until reaching
-        //         n_clusters
-        while (fread(&iframe, sizeof(iframe), 1, fp)) {
-            //             // printf("%d\n",nph_read);
-
-            if (fread(&nph, sizeof(nph), 1, fp)) {
-                //                 // printf("** %d\n",nph);
-                m_num_left = nph;
-                for (size_t iph = 0; iph < nph; iph++) {
-                    //                     // read photons 1 by 1
-                    size_t n_read = fread(reinterpret_cast<void *>(ptr),
-                                          sizeof(Cluster3x3), 1, fp);
-                    if (n_read != 1) {
-                        clusters.resize(nph_read);
-                        return clusters;
-                        // return nph_read;
-                    }
-                    good = 1;
-                    if (noise_map) {
-                        if (ptr->x >= 0 && ptr->x < nx && ptr->y >= 0 &&
-                            ptr->y < ny) {
-                            tot1 = ptr->data[4];
-                            analyze_cluster(*ptr, &t2max, &tot3, NULL, NULL,
-                                            NULL, NULL, NULL);
-                            // noise = noise_map[ptr->y * nx + ptr->x];
-                            noise = noise_map[ptr->y + ny * ptr->x];
-                            if (tot1 > noise || t2max > 2 * noise ||
-                                tot3 > 3 * noise) {
-                                ;
-                            } else
-                                good = 0;
-                        } else {
-                            printf("Bad pixel number %d %d\n", ptr->x, ptr->y);
-                            good = 0;
-                        }
-                    }
-                    if (good) {
-                        ptr++;
-                        nph_read++;
-                    }
-                    (m_num_left)--;
-                    if (nph_read >= n_clusters)
-                        break;
-                }
-            }
-            if (nph_read >= n_clusters)
-                break;
-        }
-    }
-    // printf("%d\n",nph_read);
-    clusters.resize(nph_read);
+    if (m_gain_map)
+        clusters.apply_gain_map(m_gain_map->view());
     return clusters;
 }
 
-NDArray<double, 2> calculate_eta2(ClusterVector<int> &clusters) {
-    NDArray<double, 2> eta2({clusters.size(), 2});
-    for (size_t i = 0; i < clusters.size(); i++) {
-        // int32_t t2;
-        // auto* ptr = reinterpret_cast<int32_t*> (clusters.element_ptr(i) + 2 *
-        // sizeof(int16_t)); analyze_cluster(clusters.at<Cluster3x3>(i), &t2,
-        // nullptr, nullptr, &eta2(i,0), &eta2(i,1) , nullptr, nullptr);
-        auto [x, y] = calculate_eta2(clusters.at<Cluster3x3>(i));
-        eta2(i, 0) = x;
-        eta2(i, 1) = y;
+
+
+bool ClusterFile::is_selected(Cluster3x3 &cl) {
+    //Should fail fast
+    if (m_roi) {
+        if (!(m_roi->contains(cl.x, cl.y))) {
+            return false;
+        }
     }
+    if (m_noise_map){
+        int32_t sum_1x1 = cl.data[4]; // central pixel
+        int32_t sum_2x2 = cl.sum_2x2(); // highest sum of 2x2 subclusters
+        int32_t sum_3x3 = cl.sum(); // sum of all pixels
+
+        auto noise = (*m_noise_map)(cl.y, cl.x); //TODO! check if this is correct
+        if (sum_1x1 <= noise || sum_2x2 <= 2 * noise || sum_3x3 <= 3 * noise) {
+            return false;
+        }
+    }
+    //we passed all checks
+    return true;
+}
+
+NDArray<double, 2> calculate_eta2(ClusterVector<int> &clusters) {
+    //TOTO! make work with 2x2 clusters
+    NDArray<double, 2> eta2({static_cast<int64_t>(clusters.size()), 2});
+    
+    if (clusters.cluster_size_x() == 3 || clusters.cluster_size_y() == 3) {
+        for (size_t i = 0; i < clusters.size(); i++) {
+            auto e = calculate_eta2(clusters.at<Cluster3x3>(i));
+            eta2(i, 0) = e.x;
+            eta2(i, 1) = e.y;
+        }
+    }else if(clusters.cluster_size_x() == 2 || clusters.cluster_size_y() == 2){
+        for (size_t i = 0; i < clusters.size(); i++) {
+            auto e = calculate_eta2(clusters.at<Cluster2x2>(i));
+            eta2(i, 0) = e.x;
+            eta2(i, 1) = e.y;
+        }
+    }else{
+        throw std::runtime_error("Only 3x3 and 2x2 clusters are supported");
+    }
+    
     return eta2;
 }
 
-std::array<double, 2> calculate_eta2(Cluster3x3 &cl) {
-    std::array<double, 2> eta2{};
+/** 
+ * @brief Calculate the eta2 values for a 3x3 cluster and return them in a Eta2 struct
+ * containing etay, etax and the corner of the cluster. 
+*/
+Eta2 calculate_eta2(Cluster3x3 &cl) {
+    Eta2 eta{};
 
     std::array<int32_t, 4> tot2;
     tot2[0] = cl.data[0] + cl.data[1] + cl.data[3] + cl.data[4];
@@ -279,149 +343,60 @@ std::array<double, 2> calculate_eta2(Cluster3x3 &cl) {
     tot2[3] = cl.data[4] + cl.data[5] + cl.data[7] + cl.data[8];
 
     auto c = std::max_element(tot2.begin(), tot2.end()) - tot2.begin();
-
+    eta.sum = tot2[c];
     switch (c) {
     case cBottomLeft:
         if ((cl.data[3] + cl.data[4]) != 0)
-            eta2[0] =
+            eta.x =
                 static_cast<double>(cl.data[4]) / (cl.data[3] + cl.data[4]);
         if ((cl.data[1] + cl.data[4]) != 0)
-            eta2[1] =
+            eta.y =
                 static_cast<double>(cl.data[4]) / (cl.data[1] + cl.data[4]);
+        eta.c = cBottomLeft;
         break;
     case cBottomRight:
         if ((cl.data[2] + cl.data[5]) != 0)
-            eta2[0] =
+            eta.x =
                 static_cast<double>(cl.data[5]) / (cl.data[4] + cl.data[5]);
         if ((cl.data[1] + cl.data[4]) != 0)
-            eta2[1] =
+            eta.y =
                 static_cast<double>(cl.data[4]) / (cl.data[1] + cl.data[4]);
+        eta.c = cBottomRight;
         break;
     case cTopLeft:
         if ((cl.data[7] + cl.data[4]) != 0)
-            eta2[0] =
+            eta.x =
                 static_cast<double>(cl.data[4]) / (cl.data[3] + cl.data[4]);
         if ((cl.data[7] + cl.data[4]) != 0)
-            eta2[1] =
+            eta.y =
                 static_cast<double>(cl.data[7]) / (cl.data[7] + cl.data[4]);
+        eta.c = cTopLeft;
         break;
     case cTopRight:
         if ((cl.data[5] + cl.data[4]) != 0)
-            eta2[0] =
+            eta.x =
                 static_cast<double>(cl.data[5]) / (cl.data[5] + cl.data[4]);
         if ((cl.data[7] + cl.data[4]) != 0)
-            eta2[1] =
+            eta.y =
                 static_cast<double>(cl.data[7]) / (cl.data[7] + cl.data[4]);
+        eta.c = cTopRight;
         break;
-    // default:;
+    // no default to allow compiler to warn about missing cases
     }
-    return eta2;
+    return eta;
 }
 
-int analyze_cluster(Cluster3x3 &cl, int32_t *t2, int32_t *t3, char *quad,
-                    double *eta2x, double *eta2y, double *eta3x,
-                    double *eta3y) {
 
-    return analyze_data(cl.data, t2, t3, quad, eta2x, eta2y, eta3x, eta3y);
+Eta2 calculate_eta2(Cluster2x2 &cl) {
+    Eta2 eta{};
+    if ((cl.data[0] + cl.data[1]) != 0)
+        eta.x = static_cast<double>(cl.data[1]) / (cl.data[0] + cl.data[1]);
+    if ((cl.data[0] + cl.data[2]) != 0)
+        eta.y = static_cast<double>(cl.data[2]) / (cl.data[0] + cl.data[2]);
+    eta.sum = cl.data[0] + cl.data[1] + cl.data[2]+ cl.data[3];
+    eta.c = cBottomLeft; //TODO! This is not correct, but need to put something
+    return eta;
 }
 
-int analyze_data(int32_t *data, int32_t *t2, int32_t *t3, char *quad,
-                 double *eta2x, double *eta2y, double *eta3x, double *eta3y) {
-
-    int ok = 1;
-
-    int32_t tot2[4];
-    int32_t t2max = 0;
-    char c = 0;
-    int32_t val, tot3;
-
-    tot3 = 0;
-    for (int i = 0; i < 4; i++)
-        tot2[i] = 0;
-
-    for (int ix = 0; ix < 3; ix++) {
-        for (int iy = 0; iy < 3; iy++) {
-            val = data[iy * 3 + ix];
-            //	printf ("%d ",data[iy * 3 + ix]);
-            tot3 += val;
-            if (ix <= 1 && iy <= 1)
-                tot2[cBottomLeft] += val;
-            if (ix >= 1 && iy <= 1)
-                tot2[cBottomRight] += val;
-            if (ix <= 1 && iy >= 1)
-                tot2[cTopLeft] += val;
-            if (ix >= 1 && iy >= 1)
-                tot2[cTopRight] += val;
-        }
-        //	printf ("\n");
-    }
-    // printf ("\n");
-
-    if (t2 || quad) {
-
-        t2max = tot2[0];
-        c = cBottomLeft;
-        for (int i = 1; i < 4; i++) {
-            if (tot2[i] > t2max) {
-                t2max = tot2[i];
-                c = i;
-            }
-        }
-        // printf("*** %d %d %d %d --
-        // %d\n",tot2[0],tot2[1],tot2[2],tot2[3],t2max);
-        if (quad)
-            *quad = c;
-        if (t2)
-            *t2 = t2max;
-    }
-
-    if (t3)
-        *t3 = tot3;
-
-    if (eta2x || eta2y) {
-        if (eta2x)
-            *eta2x = 0;
-        if (eta2y)
-            *eta2y = 0;
-        switch (c) {
-        case cBottomLeft:
-            if (eta2x && (data[3] + data[4]) != 0)
-                *eta2x = static_cast<double>(data[4]) / (data[3] + data[4]);
-            if (eta2y && (data[1] + data[4]) != 0)
-                *eta2y = static_cast<double>(data[4]) / (data[1] + data[4]);
-            break;
-        case cBottomRight:
-            if (eta2x && (data[2] + data[5]) != 0)
-                *eta2x = static_cast<double>(data[5]) / (data[4] + data[5]);
-            if (eta2y && (data[1] + data[4]) != 0)
-                *eta2y = static_cast<double>(data[4]) / (data[1] + data[4]);
-            break;
-        case cTopLeft:
-            if (eta2x && (data[7] + data[4]) != 0)
-                *eta2x = static_cast<double>(data[4]) / (data[3] + data[4]);
-            if (eta2y && (data[7] + data[4]) != 0)
-                *eta2y = static_cast<double>(data[7]) / (data[7] + data[4]);
-            break;
-        case cTopRight:
-            if (eta2x && t2max != 0)
-                *eta2x = static_cast<double>(data[5]) / (data[5] + data[4]);
-            if (eta2y && t2max != 0)
-                *eta2y = static_cast<double>(data[7]) / (data[7] + data[4]);
-            break;
-        default:;
-        }
-    }
-
-    if (eta3x || eta3y) {
-        if (eta3x && (data[3] + data[4] + data[5]) != 0)
-            *eta3x = static_cast<double>(-data[3] + data[3 + 2]) /
-                     (data[3] + data[4] + data[5]);
-        if (eta3y && (data[1] + data[4] + data[7]) != 0)
-            *eta3y = static_cast<double>(-data[1] + data[2 * 3 + 1]) /
-                     (data[1] + data[4] + data[7]);
-    }
-
-    return ok;
-}
 
 } // namespace aare