pkgs for python 3.14

Adapted eta interpolation: 

### Issues with previous interpolation: 

## Eta Calculation: 
- previously assumed photon hit to be in bottom left pixel of cluster
(photon hit assumed in bottom right pixel of cluster)
- clusters are filled from top left to bottom right (previously assumed:
bottom left to top right)

## Actual Interpolation: 
- photon hits are given in pixel coordinates (previous interpolation
assumed euclidean coordinates, e.g. positive distance in y coordinate
becomes negative distance in row pixels)
- removed *2 of calculated distance 

## General Adaption: 
- max_sum_2x2 return subcluster index relative to cluster center e.g.
bottomleft, bottomright

## Added proper test case 
- simulated photon hit with normal energy distribution 
- Note: Test case for 2x2 cluster fails - Think uniform photon hit
distribution cant be modeled by normalized eta distribution for 2x2
clusters

CMakeLists.txt

@@ -388,7 +388,7 @@ set(SourceFiles
     ${CMAKE_CURRENT_SOURCE_DIR}/src/RawSubFile.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 
@@ -412,6 +412,8 @@ target_link_libraries(
 
 )
 
+set_property(TARGET aare_core PROPERTY POSITION_INDEPENDENT_CODE ON)
+
 if(AARE_TESTS)
     target_compile_definitions(aare_core PRIVATE AARE_TESTS)
 endif()
@@ -431,10 +433,6 @@ set_target_properties(aare_core PROPERTIES
     PUBLIC_HEADER "${PUBLICHEADERS}"
 )
 
-if (AARE_PYTHON_BINDINGS)
-    set_property(TARGET aare_core PROPERTY POSITION_INDEPENDENT_CODE ON)
-endif()
-
 if(AARE_TESTS)
     set(TestSources
             ${CMAKE_CURRENT_SOURCE_DIR}/src/algorithm.test.cpp
@@ -465,6 +463,7 @@ if(AARE_TESTS)
     target_sources(tests PRIVATE ${TestSources} )
 endif()
 
+
 if(AARE_MASTER_PROJECT)
     install(TARGETS aare_core aare_compiler_flags 
         EXPORT "${TARGETS_EXPORT_NAME}"
@@ -474,7 +473,6 @@ if(AARE_MASTER_PROJECT)
     )
 endif()
 
-set(CMAKE_POSITION_INDEPENDENT_CODE ON)
 set(CMAKE_INSTALL_RPATH $ORIGIN)
 set(CMAKE_BUILD_WITH_INSTALL_RPATH FALSE)
 

RELEASE.md

@@ -1,16 +1,27 @@
 # Release notes
 
+### 2025.10.1
 
-### head
+Bugfixes: 
+
+- File supports reading new master json file format (multiple ROI's not supported yet)
+
+### 2025.8.22
 
 Features:
 
 - Apply calibration works in G0 if passes a 2D calibration and pedestal
 - count pixels that switch
 - calculate pedestal (also g0 version)
+- NDArray::view() needs an lvalue to reduce issues with the view outliving the array
 
 
-### 2025.07.18
+Bugfixes:
+
+- Now using glibc 2.17 in conda builds (was using the host)
+- Fixed shifted pixels in clusters close to the edge of a frame
+
+### 2025.7.18
 
 Features:
 
@@ -24,7 +35,7 @@ Bugfixes:
 - Removed unused file: ClusterFile.cpp 
 
 
-### 2025.05.22
+### 2025.5.22
 
 Features:
 
@@ -37,3 +48,6 @@ Bugfixes:
 
 
 
+
+
+

VERSION

@@ -1 +1 @@
-2025.7.18
+2025.8.22

benchmarks/CMakeLists.txt

@@ -15,7 +15,7 @@ FetchContent_MakeAvailable(benchmark)
 
 add_executable(benchmarks)
 
-target_sources(benchmarks PRIVATE ndarray_benchmark.cpp calculateeta_benchmark.cpp)
+target_sources(benchmarks PRIVATE ndarray_benchmark.cpp calculateeta_benchmark.cpp reduce_benchmark.cpp)
 
 # Link Google Benchmark and other necessary libraries
 target_link_libraries(benchmarks PRIVATE benchmark::benchmark aare_core aare_compiler_flags)

benchmarks/reduce_benchmark.cpp

@@ -0,0 +1,168 @@
+#include "aare/Cluster.hpp"
+#include <benchmark/benchmark.h>
+
+using namespace aare;
+
+class ClustersForReduceFixture : public benchmark::Fixture {
+  public:
+    Cluster<int, 5, 5> cluster_5x5{};
+    Cluster<int, 3, 3> cluster_3x3{};
+
+  private:
+    using benchmark::Fixture::SetUp;
+
+    void SetUp([[maybe_unused]] const benchmark::State &state) override {
+        int temp_data[25] = {1, 1, 1, 1, 1, 1, 1, 2, 1, 1,
+                             1, 2, 3, 1, 2, 1, 1, 1, 1, 2};
+        std::copy(std::begin(temp_data), std::end(temp_data),
+                  std::begin(cluster_5x5.data));
+
+        cluster_5x5.x = 5;
+        cluster_5x5.y = 5;
+
+        int temp_data2[9] = {1, 1, 1, 2, 3, 1, 2, 2, 1};
+        std::copy(std::begin(temp_data2), std::end(temp_data2),
+                  std::begin(cluster_3x3.data));
+
+        cluster_3x3.x = 5;
+        cluster_3x3.y = 5;
+    }
+
+    // void TearDown(::benchmark::State& state) {
+    // }
+};
+
+template <typename T>
+Cluster<T, 3, 3, int16_t> reduce_to_3x3(const Cluster<T, 5, 5, int16_t> &c) {
+    Cluster<T, 3, 3, int16_t> result;
+
+    // Write out the sums in the hope that the compiler can optimize this
+    std::array<T, 9> sum_3x3_subclusters;
+
+    // Write out the sums in the hope that the compiler can optimize this
+    sum_3x3_subclusters[0] = c.data[0] + c.data[1] + c.data[2] + c.data[5] +
+                             c.data[6] + c.data[7] + c.data[10] + c.data[11] +
+                             c.data[12];
+    sum_3x3_subclusters[1] = c.data[1] + c.data[2] + c.data[3] + c.data[6] +
+                             c.data[7] + c.data[8] + c.data[11] + c.data[12] +
+                             c.data[13];
+    sum_3x3_subclusters[2] = c.data[2] + c.data[3] + c.data[4] + c.data[7] +
+                             c.data[8] + c.data[9] + c.data[12] + c.data[13] +
+                             c.data[14];
+    sum_3x3_subclusters[3] = c.data[5] + c.data[6] + c.data[7] + c.data[10] +
+                             c.data[11] + c.data[12] + c.data[15] + c.data[16] +
+                             c.data[17];
+    sum_3x3_subclusters[4] = c.data[6] + c.data[7] + c.data[8] + c.data[11] +
+                             c.data[12] + c.data[13] + c.data[16] + c.data[17] +
+                             c.data[18];
+    sum_3x3_subclusters[5] = c.data[7] + c.data[8] + c.data[9] + c.data[12] +
+                             c.data[13] + c.data[14] + c.data[17] + c.data[18] +
+                             c.data[19];
+    sum_3x3_subclusters[6] = c.data[10] + c.data[11] + c.data[12] + c.data[15] +
+                             c.data[16] + c.data[17] + c.data[20] + c.data[21] +
+                             c.data[22];
+    sum_3x3_subclusters[7] = c.data[11] + c.data[12] + c.data[13] + c.data[16] +
+                             c.data[17] + c.data[18] + c.data[21] + c.data[22] +
+                             c.data[23];
+    sum_3x3_subclusters[8] = c.data[12] + c.data[13] + c.data[14] + c.data[17] +
+                             c.data[18] + c.data[19] + c.data[22] + c.data[23] +
+                             c.data[24];
+
+    auto index = std::max_element(sum_3x3_subclusters.begin(),
+                                  sum_3x3_subclusters.end()) -
+                 sum_3x3_subclusters.begin();
+
+    switch (index) {
+    case 0:
+        result.x = c.x - 1;
+        result.y = c.y + 1;
+        result.data = {c.data[0], c.data[1],  c.data[2],  c.data[5], c.data[6],
+                       c.data[7], c.data[10], c.data[11], c.data[12]};
+        break;
+    case 1:
+        result.x = c.x;
+        result.y = c.y + 1;
+        result.data = {c.data[1], c.data[2],  c.data[3],  c.data[6], c.data[7],
+                       c.data[8], c.data[11], c.data[12], c.data[13]};
+        break;
+    case 2:
+        result.x = c.x + 1;
+        result.y = c.y + 1;
+        result.data = {c.data[2], c.data[3],  c.data[4],  c.data[7], c.data[8],
+                       c.data[9], c.data[12], c.data[13], c.data[14]};
+        break;
+    case 3:
+        result.x = c.x - 1;
+        result.y = c.y;
+        result.data = {c.data[5],  c.data[6],  c.data[7],
+                       c.data[10], c.data[11], c.data[12],
+                       c.data[15], c.data[16], c.data[17]};
+        break;
+    case 4:
+        result.x = c.x + 1;
+        result.y = c.y;
+        result.data = {c.data[6],  c.data[7],  c.data[8],
+                       c.data[11], c.data[12], c.data[13],
+                       c.data[16], c.data[17], c.data[18]};
+        break;
+    case 5:
+        result.x = c.x + 1;
+        result.y = c.y;
+        result.data = {c.data[7],  c.data[8],  c.data[9],
+                       c.data[12], c.data[13], c.data[14],
+                       c.data[17], c.data[18], c.data[19]};
+        break;
+    case 6:
+        result.x = c.x + 1;
+        result.y = c.y - 1;
+        result.data = {c.data[10], c.data[11], c.data[12],
+                       c.data[15], c.data[16], c.data[17],
+                       c.data[20], c.data[21], c.data[22]};
+        break;
+    case 7:
+        result.x = c.x + 1;
+        result.y = c.y - 1;
+        result.data = {c.data[11], c.data[12], c.data[13],
+                       c.data[16], c.data[17], c.data[18],
+                       c.data[21], c.data[22], c.data[23]};
+        break;
+    case 8:
+        result.x = c.x + 1;
+        result.y = c.y - 1;
+        result.data = {c.data[12], c.data[13], c.data[14],
+                       c.data[17], c.data[18], c.data[19],
+                       c.data[22], c.data[23], c.data[24]};
+        break;
+    }
+    return result;
+}
+
+BENCHMARK_F(ClustersForReduceFixture, Reduce2x2)(benchmark::State &st) {
+    for (auto _ : st) {
+        // This code gets timed
+        benchmark::DoNotOptimize(reduce_to_2x2<int, 3, 3, int16_t>(
+            cluster_3x3)); // make sure compiler evaluates the expression
+    }
+}
+
+BENCHMARK_F(ClustersForReduceFixture, SpecificReduce2x2)(benchmark::State &st) {
+    for (auto _ : st) {
+        // This code gets timed
+        benchmark::DoNotOptimize(reduce_to_2x2<int>(cluster_3x3));
+    }
+}
+
+BENCHMARK_F(ClustersForReduceFixture, Reduce3x3)(benchmark::State &st) {
+    for (auto _ : st) {
+        // This code gets timed
+        benchmark::DoNotOptimize(
+            reduce_to_3x3<int, 5, 5, int16_t>(cluster_5x5));
+    }
+}
+
+BENCHMARK_F(ClustersForReduceFixture, SpecificReduce3x3)(benchmark::State &st) {
+    for (auto _ : st) {
+        // This code gets timed
+        benchmark::DoNotOptimize(reduce_to_3x3<int>(cluster_5x5));
+    }
+}

conda-recipe/conda_build_config.yaml

@@ -1,5 +1,17 @@
 python:
-    - 3.11
-    - 3.12
-    - 3.13
+    # - 3.11
+    # - 3.12
+    # - 3.13
+    - 3.14
 
+c_compiler:
+  - gcc                        # [linux]
+
+c_stdlib:
+  - sysroot                    # [linux]
+
+cxx_compiler:
+  - gxx                        # [linux]
+
+c_stdlib_version:              # [linux]
+  - 2.17                       # [linux]

conda-recipe/meta.yaml

@@ -16,6 +16,8 @@ build:
 
 requirements:
   build:
+    - {{ compiler('c') }}
+    - {{ stdlib("c") }}
     - {{ compiler('cxx') }}
     - cmake
     - ninja
@@ -23,7 +25,7 @@ requirements:
   host:
     - python
     - pip
-    - numpy=2.1
+    - numpy=2.3
     - scikit-build-core
     - pybind11 >=2.13.0
     - matplotlib # needed in host to solve the environment for run
@@ -40,11 +42,11 @@ test:
     - aare
   requires:
     - pytest
-    - boost-histogram 
+    # - boost-histogram 
   source_files:
-    - python/tests
+    # - python/tests
   commands:
-    - python -m pytest python/tests
+    # - python -m pytest python/tests
 
 about:
   summary: Data analysis library for hybrid pixel detectors from PSI 

docs/src/ClusterVector.rst

@@ -12,4 +12,11 @@ ClusterVector
    :members:
    :undoc-members:
    :private-members:
+   
+
+**Free Functions:** 
+
+.. doxygenfunction:: aare::reduce_to_3x3(const ClusterVector<Cluster<T, ClusterSizeX, ClusterSizeY, CoordType>>&)
+   
+.. doxygenfunction:: aare::reduce_to_2x2(const ClusterVector<Cluster<T, ClusterSizeX, ClusterSizeY, CoordType>>&)
    

docs/src/pyClusterVector.rst

@@ -33,4 +33,17 @@ C++ functions that support the ClusterVector or to view it as a numpy array.
     :members:
     :undoc-members:
     :show-inheritance:
-    :inherited-members:
+    :inherited-members:
+
+
+**Free Functions:** 
+
+.. autofunction:: reduce_to_3x3
+   :noindex:
+
+   Reduce a single Cluster to 3x3 by taking the 3x3 subcluster with highest photon energy.
+
+.. autofunction:: reduce_to_2x2
+   :noindex:
+
+   Reduce a single Cluster to 2x2 by taking the 2x2 subcluster with highest photon energy.

etc/dev-env.yml

@@ -13,4 +13,5 @@ dependencies:
   - pybind11
   - numpy
   - matplotlib
+  - nlohmann_json
 

include/aare/CalculateEta.hpp

@@ -7,10 +7,10 @@
 namespace aare {
 
 enum class corner : int {
-    cBottomLeft = 0,
-    cBottomRight = 1,
-    cTopLeft = 2,
-    cTopRight = 3
+    cTopLeft = 0,
+    cTopRight = 1,
+    cBottomLeft = 2,
+    cBottomRight = 3
 };
 
 enum class pixel : int {
@@ -28,7 +28,7 @@ enum class pixel : int {
 template <typename T> struct Eta2 {
     double x;
     double y;
-    int c;
+    int c{0};
     T sum;
 };
 
@@ -58,85 +58,126 @@ 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;
+    static_assert(ClusterSizeX > 1 && ClusterSizeY > 1);
+    Eta2<T> eta{};
 
     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);
+    auto max_sum = cl.max_sum_2x2();
+    eta.sum = max_sum.first;
+    int c = max_sum.second;
 
-    // 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] +
+    // subcluster top right from center
+    switch (static_cast<corner>(c)) {
+    case (corner::cTopLeft):
+        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]);
+        if ((cl.data[cluster_center_index - ClusterSizeX] +
+             cl.data[cluster_center_index]) != 0)
+            eta.y = static_cast<double>(
+                        cl.data[cluster_center_index - ClusterSizeX]) /
+                    static_cast<double>(
+                        cl.data[cluster_center_index - ClusterSizeX] +
+                        cl.data[cluster_center_index]);
 
-            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)
-
+        // dx = 0
+        // dy = 0
+        break;
+    case (corner::cTopRight):
+        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
+                    static_cast<double>(cl.data[cluster_center_index] +
+                                        cl.data[cluster_center_index + 1]);
+        if ((cl.data[cluster_center_index - ClusterSizeX] +
+             cl.data[cluster_center_index]) != 0)
+            eta.y = static_cast<double>(
+                        cl.data[cluster_center_index - ClusterSizeX]) /
+                    static_cast<double>(
+                        cl.data[cluster_center_index - ClusterSizeX] +
+                        cl.data[cluster_center_index]);
+        // dx = 1
+        // dy = 0
+        break;
+    case (corner::cBottomLeft):
+        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]);
         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]));
+                        cl.data[cluster_center_index] +
+                        cl.data[cluster_center_index + ClusterSizeX]);
+        // dx = 0
+        // dy = 1
+        break;
+    case (corner::cBottomRight):
+        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] +
+                                        cl.data[cluster_center_index + 1]);
+        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]);
+        // dx = 1
+        // dy = 1
+        break;
     }
 
-    eta.c = c; // TODO only supported for 2x2 and 3x3 clusters -> at least no
-               // underyling enum class
+    eta.c = c;
+
     return eta;
 }
 
-// TODO! Look up eta2 calculation - photon center should be top right corner
+// TODO! Look up eta2 calculation - photon center should be bottom 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]);
+        eta.x = static_cast<double>(cl.data[2]) /
+                (cl.data[2] + cl.data[3]); // between (0,1) the closer to zero
+                                           // left value probably larger
     if ((cl.data[0] + cl.data[2]) != 0)
-        eta.y = static_cast<double>(cl.data[2]) / (cl.data[0] + cl.data[2]);
+        eta.y = static_cast<double>(cl.data[1]) /
+                (cl.data[1] + cl.data[3]); // between (0,1) the closer to zero
+                                           // bottom value probably larger
     eta.sum = cl.sum();
-    eta.c = static_cast<int>(corner::cBottomLeft); // TODO! This is not correct,
-                                                   // but need to put something
+
     return eta;
 }
 
+// TODO generalize
+template <typename T>
+Eta2<T> calculate_eta2(const Cluster<T, 1, 2, int16_t> &cl) {
+    Eta2<T> eta{};
+
+    eta.x = 0;
+    eta.y = static_cast<double>(cl.data[0]) / cl.data[1];
+    eta.sum = cl.sum();
+}
+
+template <typename T>
+Eta2<T> calculate_eta2(const Cluster<T, 2, 1, int16_t> &cl) {
+    Eta2<T> eta{};
+
+    eta.x = static_cast<double>(cl.data[0]) / cl.data[1];
+    eta.y = 0;
+    eta.sum = cl.sum();
+}
+
 // 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) {
@@ -150,13 +191,11 @@ template <typename T> Eta2<T> calculate_eta3(const Cluster<T, 3, 3> &cl) {
 
     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]);
+                (cl.data[3] + cl.data[4] + cl.data[5]); // (-1,1)
 
     if ((cl.data[1] + cl.data[4] + cl.data[7]) != 0)
 

include/aare/ChunkedPedestal.hpp

@@ -1,152 +0,0 @@
-#pragma once
-#include "aare/Frame.hpp"
-#include "aare/NDArray.hpp"
-#include "aare/NDView.hpp"
-#include <cstddef>
-
-//JMulvey
-//This is a new way to do pedestals (inspired by Dominic's cluster finder)
-//Instead of pedestal tracking, we split the data (photon data) up into chunks (say 50K frames)
-//For each chunk, we look at the spectra and fit to the noise peak. When we run the cluster finder, we then use this chunked pedestal data
-//The smaller the chunk size, the more accurate, but also the longer it takes to process.
-//It is essentially a pre-processing step.
-//Ideally this new class will do that processing. 
-//But for now we will just implement a method to pass in the chunked pedestal values directly (I have my own script which does it for now)
-//I've cut this down a lot, knowing full well it'll need changing if we want to merge it with main (happy to do that once I get it work for what I need)
-
-namespace aare {
-
-/**
- * @brief Calculate the pedestal of a series of frames. Can be used as
- * standalone but mostly used in the ClusterFinder.
- *
- * @tparam SUM_TYPE type of the sum
- */
-template <typename SUM_TYPE = double> class ChunkedPedestal {
-    uint32_t m_rows;
-    uint32_t m_cols;
-    uint32_t m_n_chunks;
-    uint64_t m_current_frame_number;
-    uint64_t m_current_chunk_number;
-
-    NDArray<SUM_TYPE, 3> m_mean;
-    NDArray<SUM_TYPE, 3> m_std;
-    uint32_t m_chunk_size;
-
-  public:
-    ChunkedPedestal(uint32_t rows, uint32_t cols, uint32_t chunk_size = 50000, uint32_t n_chunks = 10)
-        : m_rows(rows), m_cols(cols), m_chunk_size(chunk_size), m_n_chunks(n_chunks),    
-          m_mean(NDArray<SUM_TYPE, 3>({n_chunks, rows, cols})), m_std(NDArray<SUM_TYPE, 3>({n_chunks, rows, cols})) {
-        assert(rows > 0 && cols > 0 && chunk_size > 0);
-        m_mean = 0;
-        m_std = 0;
-        m_current_frame_number = 0;
-        m_current_chunk_number = 0;
-    }
-    ~ChunkedPedestal() = default;
-
-    NDArray<SUM_TYPE, 3> mean() { return m_mean; }
-    NDArray<SUM_TYPE, 3> std() { return m_std; }
-
-    void set_frame_number (uint64_t frame_number) {
-        m_current_frame_number = frame_number;
-        m_current_chunk_number = std::floor(frame_number / m_chunk_size);
-
-        //Debug
-        // if (frame_number % 10000 == 0)
-        // {
-        //     std::cout << "frame_number: " << frame_number << " -> chunk_number: " << m_current_chunk_number << " pedestal at (100, 100): " << m_mean(m_current_chunk_number, 100, 100) << std::endl;
-        // }
-
-        if (m_current_chunk_number >= m_n_chunks) 
-        {
-            m_current_chunk_number = 0;
-            throw std::runtime_error(
-                "Chunk number exceeds the number of chunks");
-        }
-    }
-
-    SUM_TYPE mean(const uint32_t row, const uint32_t col) const {    
-        return m_mean(m_current_chunk_number, row, col);
-    }
-
-    SUM_TYPE std(const uint32_t row, const uint32_t col) const {
-        return m_std(m_current_chunk_number, row, col);
-    }
-
-    SUM_TYPE* get_mean_chunk_ptr() {
-        return &m_mean(m_current_chunk_number, 0, 0);
-    }
-
-    SUM_TYPE* get_std_chunk_ptr() {
-        return &m_std(m_current_chunk_number, 0, 0);
-    }
-
-    void clear() {
-        m_mean = 0;
-        m_std = 0;
-        m_n_chunks = 0;
-    }
-
-    //Probably don't need to do this one at a time, but let's keep it simple for now
-    template <typename T> void push_mean(NDView<T, 2> frame, uint32_t chunk_number) {
-        assert(frame.size() == m_rows * m_cols);
-
-        if (chunk_number >= m_n_chunks)
-            throw std::runtime_error(
-                "Chunk number is larger than the number of chunks");
-
-        // TODO! move away from 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");
-        }
-
-        for (size_t row = 0; row < m_rows; row++) {
-            for (size_t col = 0; col < m_cols; col++) {
-                push_mean<T>(row, col, chunk_number, frame(row, col));
-            }
-        }
-       
-    }
-    
-    template <typename T> void push_std(NDView<T, 2> frame, uint32_t chunk_number) {
-        assert(frame.size() == m_rows * m_cols);
-
-        if (chunk_number >= m_n_chunks)
-            throw std::runtime_error(
-                "Chunk number is larger than the number of chunks");
-
-        // TODO! move away from 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");
-        }
-
-        for (size_t row = 0; row < m_rows; row++) {
-            for (size_t col = 0; col < m_cols; col++) {
-                push_std<T>(row, col, chunk_number, frame(row, col));
-            }
-        }
-        
-    }
-
-    // pixel level operations (should be refactored to allow users to implement
-    // their own pixel level operations)
-    template <typename T>
-    void push_mean(const uint32_t row, const uint32_t col, const uint32_t chunk_number, const T val_) {        
-        m_mean(chunk_number, row, col) = val_;
-    }
-
-    template <typename T>
-    void push_std(const uint32_t row, const uint32_t col, const uint32_t chunk_number, const T val_) {        
-        m_std(chunk_number, row, col) = val_;
-    }
-
-    // getter functions
-    uint32_t rows() const { return m_rows; }
-    uint32_t cols() const { return m_cols; }
-
-};
-
-} // namespace aare

include/aare/Cluster.hpp

@@ -18,7 +18,7 @@ namespace aare {
 
 // requires clause c++20 maybe update
 template <typename T, uint8_t ClusterSizeX, uint8_t ClusterSizeY,
-          typename CoordType = int16_t>
+          typename CoordType = uint16_t>
 struct Cluster {
 
     static_assert(std::is_arithmetic_v<T>, "T needs to be an arithmetic type");
@@ -38,6 +38,13 @@ struct Cluster {
 
     T sum() const { return std::accumulate(data.begin(), data.end(), T{}); }
 
+    // TODO: handle 1 dimensional clusters
+    // TODO: change int to corner
+    /**
+     * @brief sum of 2x2 subcluster with highest energy
+     * @return photon energy of subcluster, 2x2 subcluster index relative to
+     * cluster center
+     */
     std::pair<T, int> max_sum_2x2() const {
 
         if constexpr (cluster_size_x == 3 && cluster_size_y == 3) {
@@ -53,17 +60,38 @@ struct Cluster {
         } 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);
+            constexpr size_t cluster_center_index =
+                (ClusterSizeX / 2) + (ClusterSizeY / 2) * ClusterSizeX;
 
-            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];
+            std::array<T, 4> sum_2x2_subcluster{0};
+            // subcluster top left from center
+            sum_2x2_subcluster[0] =
+                data[cluster_center_index] + data[cluster_center_index - 1] +
+                data[cluster_center_index - ClusterSizeX] +
+                data[cluster_center_index - 1 - ClusterSizeX];
+            // subcluster top right from center
+            if (ClusterSizeX > 2) {
+                sum_2x2_subcluster[1] =
+                    data[cluster_center_index] +
+                    data[cluster_center_index + 1] +
+                    data[cluster_center_index - ClusterSizeX] +
+                    data[cluster_center_index - ClusterSizeX + 1];
+            }
+            // subcluster bottom left from center
+            if (ClusterSizeY > 2) {
+                sum_2x2_subcluster[2] =
+                    data[cluster_center_index] +
+                    data[cluster_center_index - 1] +
+                    data[cluster_center_index + ClusterSizeX] +
+                    data[cluster_center_index + ClusterSizeX - 1];
+            }
+            // subcluster bottom right from center
+            if (ClusterSizeX > 2 && ClusterSizeY > 2) {
+                sum_2x2_subcluster[3] =
+                    data[cluster_center_index] +
+                    data[cluster_center_index + 1] +
+                    data[cluster_center_index + ClusterSizeX] +
+                    data[cluster_center_index + ClusterSizeX + 1];
             }
 
             int index = std::max_element(sum_2x2_subcluster.begin(),
@@ -74,6 +102,163 @@ struct Cluster {
     }
 };
 
+/**
+ * @brief Reduce a cluster to a 2x2 cluster by selecting the 2x2 block with the
+ * highest sum.
+ * @param c Cluster to reduce
+ * @return reduced cluster
+ */
+template <typename T, uint8_t ClusterSizeX, uint8_t ClusterSizeY,
+          typename CoordType = int16_t>
+Cluster<T, 2, 2, CoordType>
+reduce_to_2x2(const Cluster<T, ClusterSizeX, ClusterSizeY, CoordType> &c) {
+
+    static_assert(ClusterSizeX >= 2 && ClusterSizeY >= 2,
+                  "Cluster sizes must be at least 2x2 for reduction to 2x2");
+
+    // TODO maybe add sanity check and check that center is in max subcluster
+    Cluster<T, 2, 2, CoordType> result;
+
+    auto [sum, index] = c.max_sum_2x2();
+
+    int16_t cluster_center_index =
+        (ClusterSizeX / 2) + (ClusterSizeY / 2) * ClusterSizeX;
+
+    int16_t index_bottom_left_max_2x2_subcluster =
+        (int(index / (ClusterSizeX - 1))) * ClusterSizeX +
+        index % (ClusterSizeX - 1);
+
+    result.x =
+        c.x + (index_bottom_left_max_2x2_subcluster - cluster_center_index) %
+                  ClusterSizeX;
+
+    result.y =
+        c.y - (index_bottom_left_max_2x2_subcluster - cluster_center_index) /
+                  ClusterSizeX;
+    result.data = {
+        c.data[index_bottom_left_max_2x2_subcluster],
+        c.data[index_bottom_left_max_2x2_subcluster + 1],
+        c.data[index_bottom_left_max_2x2_subcluster + ClusterSizeX],
+        c.data[index_bottom_left_max_2x2_subcluster + ClusterSizeX + 1]};
+    return result;
+}
+
+template <typename T>
+Cluster<T, 2, 2, int16_t> reduce_to_2x2(const Cluster<T, 3, 3, int16_t> &c) {
+    Cluster<T, 2, 2, int16_t> result;
+
+    auto [s, i] = c.max_sum_2x2();
+    switch (i) {
+    case 0:
+        result.x = c.x - 1;
+        result.y = c.y + 1;
+        result.data = {c.data[0], c.data[1], c.data[3], c.data[4]};
+        break;
+    case 1:
+        result.x = c.x;
+        result.y = c.y + 1;
+        result.data = {c.data[1], c.data[2], c.data[4], c.data[5]};
+        break;
+    case 2:
+        result.x = c.x - 1;
+        result.y = c.y;
+        result.data = {c.data[3], c.data[4], c.data[6], c.data[7]};
+        break;
+    case 3:
+        result.x = c.x;
+        result.y = c.y;
+        result.data = {c.data[4], c.data[5], c.data[7], c.data[8]};
+        break;
+    }
+
+    return result;
+}
+
+template <typename T, uint8_t ClusterSizeX, uint8_t ClusterSizeY,
+          typename CoordType = int16_t>
+inline std::pair<T, uint16_t>
+max_3x3_sum(const Cluster<T, ClusterSizeX, ClusterSizeY, CoordType> &cluster) {
+
+    if constexpr (ClusterSizeX == 3 && ClusterSizeY == 3) {
+        return std::make_pair(cluster.sum(), 0);
+    } else {
+
+        size_t index = 0;
+        T max_3x3_subcluster_sum = 0;
+        for (size_t i = 0; i < ClusterSizeY - 2; ++i) {
+            for (size_t j = 0; j < ClusterSizeX - 2; ++j) {
+
+                T sum = cluster.data[i * ClusterSizeX + j] +
+                        cluster.data[i * ClusterSizeX + j + 1] +
+                        cluster.data[i * ClusterSizeX + j + 2] +
+                        cluster.data[(i + 1) * ClusterSizeX + j] +
+                        cluster.data[(i + 1) * ClusterSizeX + j + 1] +
+                        cluster.data[(i + 1) * ClusterSizeX + j + 2] +
+                        cluster.data[(i + 2) * ClusterSizeX + j] +
+                        cluster.data[(i + 2) * ClusterSizeX + j + 1] +
+                        cluster.data[(i + 2) * ClusterSizeX + j + 2];
+                if (sum > max_3x3_subcluster_sum) {
+                    max_3x3_subcluster_sum = sum;
+                    index = i * (ClusterSizeX - 2) + j;
+                }
+            }
+        }
+
+        return std::make_pair(max_3x3_subcluster_sum, index);
+    }
+}
+
+/**
+ * @brief Reduce a cluster to a 3x3 cluster by selecting the 3x3 block with the
+ * highest sum.
+ * @param c Cluster to reduce
+ * @return reduced cluster
+ */
+template <typename T, uint8_t ClusterSizeX, uint8_t ClusterSizeY,
+          typename CoordType = int16_t>
+Cluster<T, 3, 3, CoordType>
+reduce_to_3x3(const Cluster<T, ClusterSizeX, ClusterSizeY, CoordType> &c) {
+
+    static_assert(ClusterSizeX >= 3 && ClusterSizeY >= 3,
+                  "Cluster sizes must be at least 3x3 for reduction to 3x3");
+
+    Cluster<T, 3, 3, CoordType> result;
+
+    // TODO maybe add sanity check and check that center is in max subcluster
+
+    auto [sum, index] = max_3x3_sum(c);
+
+    int16_t cluster_center_index =
+        (ClusterSizeX / 2) + (ClusterSizeY / 2) * ClusterSizeX;
+
+    int16_t index_center_max_3x3_subcluster =
+        (int(index / (ClusterSizeX - 2))) * ClusterSizeX + ClusterSizeX +
+        index % (ClusterSizeX - 2) + 1;
+
+    int16_t index_3x3_subcluster_cluster_center =
+        int((cluster_center_index - 1 - ClusterSizeX) / ClusterSizeX) *
+            (ClusterSizeX - 2) +
+        (cluster_center_index - 1 - ClusterSizeX) % ClusterSizeX;
+
+    result.x =
+        c.x + (index % (ClusterSizeX - 2) -
+               (index_3x3_subcluster_cluster_center % (ClusterSizeX - 2)));
+    result.y =
+        c.y - (index / (ClusterSizeX - 2) -
+               (index_3x3_subcluster_cluster_center / (ClusterSizeX - 2)));
+
+    result.data = {c.data[index_center_max_3x3_subcluster - ClusterSizeX - 1],
+                   c.data[index_center_max_3x3_subcluster - ClusterSizeX],
+                   c.data[index_center_max_3x3_subcluster - ClusterSizeX + 1],
+                   c.data[index_center_max_3x3_subcluster - 1],
+                   c.data[index_center_max_3x3_subcluster],
+                   c.data[index_center_max_3x3_subcluster + 1],
+                   c.data[index_center_max_3x3_subcluster + ClusterSizeX - 1],
+                   c.data[index_center_max_3x3_subcluster + ClusterSizeX],
+                   c.data[index_center_max_3x3_subcluster + ClusterSizeX + 1]};
+    return result;
+}
+
 // Type Traits for is_cluster_type
 template <typename T>
 struct is_cluster : std::false_type {}; // Default case: Not a Cluster

include/aare/ClusterFinder.hpp

@@ -4,11 +4,9 @@
 #include "aare/Dtype.hpp"
 #include "aare/NDArray.hpp"
 #include "aare/NDView.hpp"
-// #include "aare/Pedestal.hpp"
-#include "aare/ChunkedPedestal.hpp"
+#include "aare/Pedestal.hpp"
 #include "aare/defs.hpp"
 #include <cstddef>
-#include <iostream>
 
 namespace aare {
 
@@ -19,13 +17,11 @@ class ClusterFinder {
     const PEDESTAL_TYPE m_nSigma;
     const PEDESTAL_TYPE c2;
     const PEDESTAL_TYPE c3;
-    ChunkedPedestal<PEDESTAL_TYPE> m_pedestal;
+    Pedestal<PEDESTAL_TYPE> m_pedestal;
     ClusterVector<ClusterType> m_clusters;
-    const uint32_t ClusterSizeX;
-    const uint32_t ClusterSizeY;
 
-    static const uint8_t SavedClusterSizeX = ClusterType::cluster_size_x;
-    static const uint8_t SavedClusterSizeY = ClusterType::cluster_size_y;
+    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:
@@ -38,36 +34,25 @@ class ClusterFinder {
      *
      */
     ClusterFinder(Shape<2> image_size, PEDESTAL_TYPE nSigma = 5.0,
-                  size_t capacity = 1000000,
-                  uint32_t chunk_size = 50000, uint32_t n_chunks = 10,
-                  uint32_t cluster_size_x = 3, uint32_t cluster_size_y = 3)
+                  size_t capacity = 1000000)
         : m_image_size(image_size), m_nSigma(nSigma),
-          c2(sqrt((cluster_size_y + 1) / 2 * (cluster_size_x + 1) / 2)),
-          c3(sqrt(cluster_size_x * cluster_size_y)),
-          ClusterSizeX(cluster_size_x), ClusterSizeY(cluster_size_y),
-          m_pedestal(image_size[0], image_size[1], chunk_size, n_chunks), m_clusters(capacity) {
+          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);
-    // }
-    void push_pedestal_mean(NDView<PEDESTAL_TYPE, 2> frame, uint32_t chunk_number) {
-        m_pedestal.push_mean(frame, chunk_number);
+    void push_pedestal_frame(NDView<FRAME_TYPE, 2> frame) {
+        m_pedestal.push(frame);
     }
-    void push_pedestal_std(NDView<PEDESTAL_TYPE, 2> frame, uint32_t chunk_number) {
-        m_pedestal.push_std(frame, chunk_number);
-    }
-    //This is here purely to keep the compiler happy for now
-    void push_pedestal_frame(NDView<FRAME_TYPE, 2> frame) {}
 
     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
      * new ClusterVector and return it.
      * @param realloc_same_capacity if true the new ClusterVector will have the
@@ -84,13 +69,11 @@ class ClusterFinder {
         return tmp;
     }
     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 = ClusterSizeY / 2;
         int dx = ClusterSizeX / 2;
-        int dy2 = SavedClusterSizeY / 2;
-        int dx2 = SavedClusterSizeX / 2;
-
         int has_center_pixel_x =
             ClusterSizeX %
             2; // for even sized clusters there is no proper cluster center and
@@ -98,39 +81,27 @@ class ClusterFinder {
         int has_center_pixel_y = ClusterSizeY % 2;
 
         m_clusters.set_frame_number(frame_number);
-        m_pedestal.set_frame_number(frame_number);
-        auto mean_ptr = m_pedestal.get_mean_chunk_ptr();
-        auto std_ptr = m_pedestal.get_std_chunk_ptr();
-
         for (int iy = 0; iy < frame.shape(0); iy++) {
-            size_t row_offset = iy * frame.shape(1); 
             for (int ix = 0; ix < frame.shape(1); ix++) {
 
-                // PEDESTAL_TYPE rms = m_pedestal.std(iy, ix);
-                PEDESTAL_TYPE rms = std_ptr[row_offset + ix];
-                if (rms == 0) continue;
-
                 PEDESTAL_TYPE max = std::numeric_limits<FRAME_TYPE>::min();
                 PEDESTAL_TYPE total = 0;
 
-                // What can we short circuit here?                
-                // PEDESTAL_TYPE value = (frame(iy, ix) - m_pedestal.mean(iy, ix));
-                PEDESTAL_TYPE value = (frame(iy, ix) - mean_ptr[row_offset + ix]);
+                // What can we short circuit here?
+                PEDESTAL_TYPE rms = m_pedestal.std(iy, ix);
+                PEDESTAL_TYPE value = (frame(iy, ix) - m_pedestal.mean(iy, ix));
 
                 if (value < -m_nSigma * rms)
                     continue; // NEGATIVE_PEDESTAL go to next pixel
                               // TODO! No pedestal update???
 
                 for (int ir = -dy; ir < dy + has_center_pixel_y; ir++) {
-                    size_t inner_row_offset = row_offset + (ir * frame.shape(1));
                     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)) {
-                            // if (m_pedestal.std(iy + ir, ix + ic) == 0) continue;
-                            if (std_ptr[inner_row_offset + ix + ic] == 0) continue;
-
-                            // PEDESTAL_TYPE val = frame(iy + ir, ix + ic) - m_pedestal.mean(iy + ir, ix + ic);
-                            PEDESTAL_TYPE val = frame(iy + ir, ix + ic) - mean_ptr[inner_row_offset + ix + ic];
+                            PEDESTAL_TYPE val =
+                                frame(iy + ir, ix + ic) -
+                                m_pedestal.mean(iy + ir, ix + ic);
 
                             total += val;
                             max = std::max(max, val);
@@ -138,64 +109,24 @@ class ClusterFinder {
                     }
                 }
 
-//                 if (frame_number < 1)
-//                     if ( (ix == 115 && iy == 122) )
-//                     if ( (ix == 175 && iy == 175) )
-//                     {
-// //                         std::cout << std::endl;
-// //                         std::cout << std::endl;
-// //                         std::cout << "frame_number: " << frame_number << std::endl;
-// //                         std::cout << "(" << ix << ", " << iy << "): " << std::endl;
-// //                         std::cout << "frame.shape: (" << frame.shape(0) << ", " << frame.shape(1) << "): " << std::endl;
-// //                         std::cout << "frame(175, 175): " << frame(175, 175) << std::endl;
-// //                         std::cout << "frame(77, 98): " << frame(77, 98) << std::endl;
-// //                         std::cout << "frame(82, 100): " << frame(82, 100) << std::endl;
-// //                         std::cout << "frame(iy, ix): " << frame(iy, ix) << std::endl;
-// //                         std::cout << "mean_ptr[row_offset + ix]: " << mean_ptr[row_offset + ix] << std::endl;
-// //                         std::cout << "total: " << total << std::endl;
-
-//                         std::cout << "(" << ix << ", " << iy << "): " << frame(iy, ix) << std::endl;
-//                     }
-
-                // if ((max > m_nSigma * rms)) {
-                //     if (value < max)
-                //         continue; // Not max go to the next pixel
-                //                   // but also no pedestal update
-                // } else 
-                if (total > c3 * m_nSigma * rms) {
+                if ((max > m_nSigma * rms)) {
+                    if (value < max)
+                        continue; // Not max go to the next pixel
+                                  // but also no pedestal update
+                } else if (total > c3 * m_nSigma * rms) {
                     // pass
                 } else {
                     // m_pedestal.push(iy, ix, frame(iy, ix));   // Safe option
-
-                    //Not needed for chunked pedestal
-                    // m_pedestal.push_fast(
-                    //     iy, ix,
-                    //     frame(iy,
-                    //           ix)); // Assume we have reached n_samples in the
-                    //                 // pedestal, slight performance improvement
+                    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) {
-                    
-                    // if (total < 0)
-                    // {
-                    //     std::cout << "" << std::endl;   
-                    //     std::cout << "frame_number: " << frame_number << std::endl;                                        
-                    //     std::cout << "ix: " << ix << std::endl;
-                    //     std::cout << "iy: " << iy << std::endl;
-                    //     std::cout << "frame(iy, ix): " << frame(iy, ix) << std::endl;
-                    //     std::cout << "m_pedestal.mean(iy, ix): " << m_pedestal.mean(iy, ix) << std::endl;
-                    //     std::cout << "m_pedestal.std(iy, ix): " << m_pedestal.std(iy, ix) << std::endl;
-                    //     std::cout << "max: " << max << std::endl;
-                    //     std::cout << "value: " << value << std::endl;
-                    //     std::cout << "m_nSigma * rms: " << (m_nSigma * rms) << std::endl;
-                    //     std::cout << "total: " << total << std::endl;
-                    //     std::cout << "c3 * m_nSigma * rms: " << (c3 * m_nSigma * rms) << std::endl;
-                    // }                    
-
                     ClusterType cluster{};
                     cluster.x = ix;
                     cluster.y = iy;
@@ -204,22 +135,16 @@ class ClusterFinder {
                     // It's worth redoing the look since most of the time we
                     // don't have a photon
                     int i = 0;
-                    for (int ir = -dy2; ir < dy2 + has_center_pixel_y; ir++) {
-                        size_t inner_row_offset = row_offset + (ir * frame.shape(1));
-                        for (int ic = -dx2; ic < dx2 + has_center_pixel_y; 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)) {
-                                // if (m_pedestal.std(iy + ir, ix + ic) == 0) continue;
-                                // if (std_ptr[inner_row_offset + ix + ic] == 0) continue;
-
-                                // CT tmp = static_cast<CT>(frame(iy + ir, ix + ic)) - static_cast<CT>(m_pedestal.mean(iy + ir, ix + ic));
-
-                                // CT tmp = 0;
-                                if (std_ptr[inner_row_offset + ix + ic] != 0)
-                                {                                
-                                    CT tmp = static_cast<CT>(frame(iy + ir, ix + ic)) - static_cast<CT>(mean_ptr[inner_row_offset + ix + ic]);                                
-                                    cluster.data[i] = tmp; // Watch for out of bounds access                                
-                                }
+                                CT tmp =
+                                    static_cast<CT>(frame(iy + ir, ix + ic)) -
+                                    static_cast<CT>(
+                                        m_pedestal.mean(iy + ir, ix + ic));
+                                cluster.data[i] =
+                                    tmp; // Watch for out of bounds access
                             }
                             i++;
                         }
@@ -233,4 +158,4 @@ class ClusterFinder {
     }
 };
 
-} // namespace aare
+} // namespace aare

include/aare/ClusterFinderMT.hpp

@@ -20,15 +20,9 @@ enum class FrameType {
 struct FrameWrapper {
     FrameType type;
     uint64_t frame_number;
-    // NDArray<T, 2> data;
     NDArray<uint16_t, 2> data;
-    // NDArray<double, 2> data;
-    // void* data_ptr;
-    // std::type_index data_type;
-    uint32_t chunk_number;
 };
 
-
 /**
  * @brief ClusterFinderMT is a multi-threaded version of ClusterFinder. It uses
  * a producer-consumer queue to distribute the frames to the threads. The
@@ -74,7 +68,6 @@ class ClusterFinderMT {
         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);
@@ -128,9 +121,7 @@ class ClusterFinderMT {
      * @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,
-                    uint32_t chunk_size = 50000, uint32_t n_chunks = 10,
-                    uint32_t cluster_size_x = 3, uint32_t cluster_size_y = 3)
+                    size_t capacity = 2000, size_t n_threads = 3)
         : m_n_threads(n_threads) {
 
         LOG(logDEBUG1) << "ClusterFinderMT: "
@@ -143,7 +134,7 @@ class ClusterFinderMT {
             m_cluster_finders.push_back(
                 std::make_unique<
                     ClusterFinder<ClusterType, FRAME_TYPE, PEDESTAL_TYPE>>(
-                    image_size, nSigma, capacity, chunk_size, n_chunks, cluster_size_x, cluster_size_y));
+                    image_size, nSigma, capacity));
         }
         for (size_t i = 0; i < n_threads; i++) {
             m_input_queues.emplace_back(std::make_unique<InputQueue>(200));
@@ -217,7 +208,7 @@ class ClusterFinderMT {
      */
     void push_pedestal_frame(NDView<FRAME_TYPE, 2> frame) {
         FrameWrapper fw{FrameType::PEDESTAL, 0,
-                        NDArray(frame), 0}; // TODO! copies the data!
+                        NDArray(frame)}; // TODO! copies the data!
 
         for (auto &queue : m_input_queues) {
             while (!queue->write(fw)) {
@@ -226,23 +217,6 @@ class ClusterFinderMT {
         }
     }
 
-    void push_pedestal_mean(NDView<PEDESTAL_TYPE, 2> frame, uint32_t chunk_number) {
-        if (!m_processing_threads_stopped) {
-            throw std::runtime_error("ClusterFinderMT is still running");
-        }
-        for (auto &cf : m_cluster_finders) {
-            cf->push_pedestal_mean(frame, chunk_number);
-        }
-    }
-    void push_pedestal_std(NDView<PEDESTAL_TYPE, 2> frame, uint32_t chunk_number) {
-        if (!m_processing_threads_stopped) {
-            throw std::runtime_error("ClusterFinderMT is still running");
-        }
-        for (auto &cf : m_cluster_finders) {
-            cf->push_pedestal_std(frame, chunk_number);
-        }
-    }
-
     /**
      * @brief Push the frame to the queue of the next available thread. Function
      * returns once the frame is in a queue.
@@ -250,10 +224,7 @@ class ClusterFinderMT {
      */
     void find_clusters(NDView<FRAME_TYPE, 2> frame, uint64_t frame_number = 0) {
         FrameWrapper fw{FrameType::DATA, frame_number,
-                        NDArray(frame), 0}; // TODO! copies the data!
-
-        // std::cout << "frame(122, 115): " << frame(122, 115) << std::endl;
-
+                        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);
         }
@@ -310,4 +281,4 @@ class ClusterFinderMT {
     // }
 };
 
-} // namespace aare
+} // namespace aare

include/aare/ClusterVector.hpp

@@ -32,8 +32,7 @@ class ClusterVector; // Forward declaration
  */
 template <typename T, uint8_t ClusterSizeX, uint8_t ClusterSizeY,
           typename CoordType>
-class ClusterVector<Cluster<T, ClusterSizeX, ClusterSizeY, 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
@@ -173,4 +172,40 @@ class ClusterVector<Cluster<T, ClusterSizeX, ClusterSizeY, CoordType>>
     }
 };
 
+/**
+ * @brief Reduce a cluster to a 2x2 cluster by selecting the 2x2 block with the
+ * highest sum.
+ * @param cv Clustervector containing clusters to reduce
+ * @return Clustervector with reduced clusters
+ */
+template <typename T, uint8_t ClusterSizeX, uint8_t ClusterSizeY,
+          typename CoordType = uint16_t>
+ClusterVector<Cluster<T, 2, 2, CoordType>> reduce_to_2x2(
+    const ClusterVector<Cluster<T, ClusterSizeX, ClusterSizeY, CoordType>>
+        &cv) {
+    ClusterVector<Cluster<T, 2, 2, CoordType>> result;
+    for (const auto &c : cv) {
+        result.push_back(reduce_to_2x2(c));
+    }
+    return result;
+}
+
+/**
+ * @brief Reduce a cluster to a 3x3 cluster by selecting the 3x3 block with the
+ * highest sum.
+ * @param cv Clustervector containing clusters to reduce
+ * @return Clustervector with reduced clusters
+ */
+template <typename T, uint8_t ClusterSizeX, uint8_t ClusterSizeY,
+          typename CoordType = uint16_t>
+ClusterVector<Cluster<T, 3, 3, CoordType>> reduce_to_3x3(
+    const ClusterVector<Cluster<T, ClusterSizeX, ClusterSizeY, CoordType>>
+        &cv) {
+    ClusterVector<Cluster<T, 3, 3, CoordType>> result;
+    for (const auto &c : cv) {
+        result.push_back(reduce_to_3x3(c));
+    }
+    return result;
+}
+
 } // namespace aare

include/aare/Frame.hpp

@@ -105,7 +105,7 @@ class Frame {
      * @tparam T type of the pixels
      * @return NDView<T, 2>
      */
-    template <typename T> NDView<T, 2> view() {
+    template <typename T> NDView<T, 2> view() & {
         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);

include/aare/Interpolator.hpp

@@ -69,26 +69,27 @@ Interpolator::interpolate(const ClusterVector<ClusterType> &clusters) {
             // cBottomRight = 1,
             // cTopLeft = 2,
             // cTopRight = 3
+            // TODO: could also chaneg the sign of the eta calculation
             switch (static_cast<corner>(eta.c)) {
             case corner::cTopLeft:
-                dX = -1.;
-                dY = 0;
+                dX = 0.0;
+                dY = 0.0;
                 break;
             case corner::cTopRight:;
-                dX = 0;
-                dY = 0;
+                dX = 1.0;
+                dY = 0.0;
                 break;
             case corner::cBottomLeft:
-                dX = -1.;
-                dY = -1.;
+                dX = 0.0;
+                dY = 1.0;
                 break;
             case corner::cBottomRight:
-                dX = 0.;
-                dY = -1.;
+                dX = 1.0;
+                dY = 1.0;
                 break;
             }
-            photon.x += m_ietax(ix, iy, ie) * 2 + dX;
-            photon.y += m_ietay(ix, iy, ie) * 2 + dY;
+            photon.x -= m_ietax(ix, iy, ie) - dX;
+            photon.y -= m_ietay(ix, iy, ie) - dY;
             photons.push_back(photon);
         }
     } else if (clusters.cluster_size_x() == 2 ||
@@ -112,10 +113,11 @@ Interpolator::interpolate(const ClusterVector<ClusterType> &clusters) {
             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;
+            // TODO: why 2?
+            photon.x -=
+                m_ietax(ix, iy, ie); // eta goes between 0 and 1 but we could
+                                     // move the hit anywhere in the 2x2
+            photon.y -= m_ietay(ix, iy, ie);
             photons.push_back(photon);
         }
 

include/aare/RawMasterFile.hpp

@@ -42,14 +42,16 @@ class RawFileNameComponents {
 
 class ScanParameters {
     bool m_enabled = false;
-    std::string m_dac;
+    DACIndex m_dac{};
     int m_start = 0;
     int m_stop = 0;
     int m_step = 0;
-    // TODO! add settleTime, requires string to time conversion
+    int64_t m_settleTime = 0; // [ns]
 
   public:
     ScanParameters(const std::string &par);
+    ScanParameters(const bool enabled, const DACIndex dac, const int start,
+                   const int stop, const int step, const int64_t settleTime);
     ScanParameters() = default;
     ScanParameters(const ScanParameters &) = default;
     ScanParameters &operator=(const ScanParameters &) = default;
@@ -57,8 +59,9 @@ class ScanParameters {
     int start() const;
     int stop() const;
     int step() const;
-    const std::string &dac() const;
+    DACIndex dac() const;
     bool enabled() const;
+    int64_t settleTime() const;
     void increment_stop();
 };
 

include/aare/defs.hpp

@@ -215,6 +215,122 @@ enum class DetectorType {
     Unknown
 };
 
+/**
+ * @brief Enum class to define the Digital to Analog converter
+ * The values are the same as in slsDetectorPackage
+ */
+enum DACIndex {
+    DAC_0,
+    DAC_1,
+    DAC_2,
+    DAC_3,
+    DAC_4,
+    DAC_5,
+    DAC_6,
+    DAC_7,
+    DAC_8,
+    DAC_9,
+    DAC_10,
+    DAC_11,
+    DAC_12,
+    DAC_13,
+    DAC_14,
+    DAC_15,
+    DAC_16,
+    DAC_17,
+    VSVP,
+    VTRIM,
+    VRPREAMP,
+    VRSHAPER,
+    VSVN,
+    VTGSTV,
+    VCMP_LL,
+    VCMP_LR,
+    VCAL,
+    VCMP_RL,
+    RXB_RB,
+    RXB_LB,
+    VCMP_RR,
+    VCP,
+    VCN,
+    VISHAPER,
+    VTHRESHOLD,
+    IO_DELAY,
+    VREF_DS,
+    VOUT_CM,
+    VIN_CM,
+    VREF_COMP,
+    VB_COMP,
+    VDD_PROT,
+    VIN_COM,
+    VREF_PRECH,
+    VB_PIXBUF,
+    VB_DS,
+    VREF_H_ADC,
+    VB_COMP_FE,
+    VB_COMP_ADC,
+    VCOM_CDS,
+    VREF_RSTORE,
+    VB_OPA_1ST,
+    VREF_COMP_FE,
+    VCOM_ADC1,
+    VREF_L_ADC,
+    VREF_CDS,
+    VB_CS,
+    VB_OPA_FD,
+    VCOM_ADC2,
+    VCASSH,
+    VTH2,
+    VRSHAPER_N,
+    VIPRE_OUT,
+    VTH3,
+    VTH1,
+    VICIN,
+    VCAS,
+    VCAL_N,
+    VIPRE,
+    VCAL_P,
+    VDCSH,
+    VBP_COLBUF,
+    VB_SDA,
+    VCASC_SFP,
+    VIPRE_CDS,
+    IBIAS_SFP,
+    ADC_VPP,
+    HIGH_VOLTAGE,
+    TEMPERATURE_ADC,
+    TEMPERATURE_FPGA,
+    TEMPERATURE_FPGAEXT,
+    TEMPERATURE_10GE,
+    TEMPERATURE_DCDC,
+    TEMPERATURE_SODL,
+    TEMPERATURE_SODR,
+    TEMPERATURE_FPGA2,
+    TEMPERATURE_FPGA3,
+    TRIMBIT_SCAN,
+    V_POWER_A = 100,
+    V_POWER_B = 101,
+    V_POWER_C = 102,
+    V_POWER_D = 103,
+    V_POWER_IO = 104,
+    V_POWER_CHIP = 105,
+    I_POWER_A = 106,
+    I_POWER_B = 107,
+    I_POWER_C = 108,
+    I_POWER_D = 109,
+    I_POWER_IO = 110,
+    V_LIMIT = 111,
+    SLOW_ADC0 = 1000,
+    SLOW_ADC1,
+    SLOW_ADC2,
+    SLOW_ADC3,
+    SLOW_ADC4,
+    SLOW_ADC5,
+    SLOW_ADC6,
+    SLOW_ADC7,
+    SLOW_ADC_TEMP
+};
+
 enum class TimingMode { Auto, Trigger };
 enum class FrameDiscardPolicy { NoDiscard, Discard, DiscardPartial };
 
@@ -231,6 +347,15 @@ template <> FrameDiscardPolicy StringTo(const std::string & /*mode*/);
 
 using DataTypeVariants = std::variant<uint16_t, uint32_t>;
 
-constexpr uint16_t ADC_MASK = 0x3FFF; // used to mask out the gain bits in Jungfrau
+constexpr uint16_t ADC_MASK =
+    0x3FFF; // used to mask out the gain bits in Jungfrau
+
+/**
+ * @brief Convert a string to a DACIndex
+ * @param arg string representation of the dacIndex
+ * @return DACIndex
+ * @throw invalid argument error if the string does not match any DACIndex
+ */
+template <> DACIndex StringTo(const std::string &arg);
 
 } // namespace aare

python/aare/ClusterFinder.py

@@ -26,34 +26,33 @@ def _get_class(name, cluster_size, dtype):
 
 
 
-def ClusterFinder(image_size, saved_cluster_size, checked_cluster_size, n_sigma=5, dtype = np.int32, capacity = 1024, chunk_size=50000, n_chunks = 10):
+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", saved_cluster_size, dtype)
-    return cls(image_size, n_sigma=n_sigma, capacity=capacity, chunk_size=chunk_size, n_chunks=n_chunks, cluster_size_x=checked_cluster_size[0], cluster_size_y=checked_cluster_size[1])
+    cls = _get_class("ClusterFinder", cluster_size, dtype)
+    return cls(image_size, n_sigma=n_sigma, capacity=capacity)
 
 
 
-def ClusterFinderMT(image_size, saved_cluster_size = (3,3), checked_cluster_size = (3,3), dtype=np.int32, n_sigma=5, capacity = 1024, n_threads = 3, chunk_size=50000, n_chunks = 10): 
+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", saved_cluster_size, dtype)
-    return cls(image_size, n_sigma=n_sigma, capacity=capacity, n_threads=n_threads, chunk_size=chunk_size, n_chunks=n_chunks,  cluster_size_x=checked_cluster_size[0], cluster_size_y=checked_cluster_size[1])
+    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): 
+def ClusterCollector(clusterfindermt, 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)
+    
+    cls = _get_class("ClusterCollector", clusterfindermt.cluster_size, dtype)
     return cls(clusterfindermt)
 
 def ClusterFileSink(clusterfindermt, cluster_file, dtype=np.int32): 

python/aare/__init__.py

@@ -17,7 +17,7 @@ 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 reduce_to_2x2, reduce_to_3x3
 
 from ._aare import apply_custom_weights
 

python/src/bind_Cluster.hpp

@@ -24,7 +24,8 @@ void define_Cluster(py::module &m, const std::string &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) {
+        .def(py::init([](CoordType x, CoordType y,
+                         py::array_t<Type, py::array::forcecast> data) {
             py::buffer_info buf_info = data.request();
             Cluster<Type, ClusterSizeX, ClusterSizeY, CoordType> cluster;
             cluster.x = x;
@@ -34,31 +35,58 @@ void define_Cluster(py::module &m, const std::string &typestr) {
                 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)
-                ));
+        // TODO! Review if to keep or not
+        .def_property_readonly(
+            "data",
+            [](Cluster<Type, ClusterSizeX, ClusterSizeY, CoordType> &c)
+                -> py::array {
+                return py::array(py::buffer_info(
+                    c.data.data(), sizeof(Type),
+                    py::format_descriptor<Type>::format(), // Type
+                                                           // format
+                    2, // Number of dimensions
+                    {static_cast<ssize_t>(ClusterSizeX),
+                     static_cast<ssize_t>(ClusterSizeY)}, // Shape (flattened)
+                    {sizeof(Type) * ClusterSizeY, sizeof(Type)}
+                    // Stride (step size between elements)
+                    ));
+            })
+
+        .def_readonly("x",
+                      &Cluster<Type, ClusterSizeX, ClusterSizeY, CoordType>::x)
+
+        .def_readonly("y",
+                      &Cluster<Type, ClusterSizeX, ClusterSizeY, CoordType>::y);
+}
+
+template <typename T, uint8_t ClusterSizeX, uint8_t ClusterSizeY,
+          typename CoordType = int16_t>
+void reduce_to_3x3(py::module &m) {
+
+    m.def(
+        "reduce_to_3x3",
+        [](const Cluster<T, ClusterSizeX, ClusterSizeY, CoordType> &cl) {
+            return reduce_to_3x3(cl);
         },
-        [](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!!!
+        py::return_value_policy::move,
+        "Reduce cluster to 3x3 subcluster by taking the 3x3 subcluster with "
+        "the highest photon energy.");
+}
 
-        });
-    */
+template <typename T, uint8_t ClusterSizeX, uint8_t ClusterSizeY,
+          typename CoordType = int16_t>
+void reduce_to_2x2(py::module &m) {
+
+    m.def(
+        "reduce_to_2x2",
+        [](const Cluster<T, ClusterSizeX, ClusterSizeY, CoordType> &cl) {
+            return reduce_to_2x2(cl);
+        },
+        py::return_value_policy::move,
+        "Reduce cluster to 2x2 subcluster by taking the 2x2 subcluster with "
+        "the highest photon energy.");
 }
 
 #pragma GCC diagnostic pop

python/src/bind_ClusterFile.hpp

@@ -44,14 +44,14 @@ void define_ClusterFile(py::module &m, const std::string &typestr) {
                  auto v = new ClusterVector<ClusterType>(self.read_frame());
                  return v;
              })
-        .def("set_roi", &ClusterFile<ClusterType>::set_roi,
-             py::arg("roi"))
+        .def("set_roi", &ClusterFile<ClusterType>::set_roi, py::arg("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);
-            }, py::arg("noise_map"))
+            },
+            py::arg("noise_map"))
 
         .def("set_gain_map",
              [](ClusterFile<ClusterType> &self, py::array_t<double> gain_map) {
@@ -84,11 +84,19 @@ 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);
           });
+
+    m.def("calculate_eta2", [](const aare::Cluster<Type, CoordSizeX, CoordSizeY,
+                                                   CoordType> &cluster) {
+        auto eta2 = calculate_eta2(cluster);
+        // TODO return proper eta class
+        return py::make_tuple(eta2.x, eta2.y, eta2.sum);
+    });
 }
 
 #pragma GCC diagnostic pop

python/src/bind_ClusterFinder.hpp

@@ -30,30 +30,14 @@ void define_ClusterFinder(py::module &m, const std::string &typestr) {
 
     py::class_<ClusterFinder<ClusterType, uint16_t, pd_type>>(
         m, class_name.c_str())
-        .def(py::init<Shape<2>, pd_type, size_t, uint32_t, uint32_t, uint32_t, uint32_t>(),
-             py::arg("image_size"), py::arg("n_sigma") = 5.0, py::arg("capacity") = 1'000'000,
-             py::arg("chunk_size") = 50'000, py::arg("n_chunks") = 10,
-             py::arg("cluster_size_x") = 3, py::arg("cluster_size_y") = 3)
+        .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("push_pedestal_mean",
-             [](ClusterFinder<ClusterType, uint16_t, pd_type> &self,
-                py::array_t<double> frame, uint32_t chunk_number) {            
-                 auto view = make_view_2d(frame);             
-                 self.push_pedestal_mean(view, chunk_number);
-             })
-        .def("push_pedestal_std",
-             [](ClusterFinder<ClusterType, uint16_t, pd_type> &self,
-                py::array_t<double> frame, uint32_t chunk_number) {
-                 auto view = make_view_2d(frame);
-                 self.push_pedestal_std(view, chunk_number);
-             })
-
         .def("clear_pedestal",
              &ClusterFinder<ClusterType, uint16_t, pd_type>::clear_pedestal)
         .def_property_readonly(

python/src/bind_ClusterFinderMT.hpp

@@ -30,31 +30,15 @@ void define_ClusterFinderMT(py::module &m, const std::string &typestr) {
 
     py::class_<ClusterFinderMT<ClusterType, uint16_t, pd_type>>(
         m, class_name.c_str())
-        .def(py::init<Shape<2>, pd_type, size_t, size_t, uint32_t, uint32_t, uint32_t, uint32_t>(),
+        .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,
-             py::arg("chunk_size") = 50'000, py::arg("n_chunks") = 10,
-             py::arg("cluster_size_x") = 3, py::arg("cluster_size_y") = 3)
+             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("push_pedestal_mean",
-             [](ClusterFinderMT<ClusterType, uint16_t, pd_type> &self,
-                py::array_t<double> frame, uint32_t chunk_number) {
-                 auto view = make_view_2d(frame);                 
-                 self.push_pedestal_mean(view, chunk_number);
-             })
-        .def("push_pedestal_std",
-             [](ClusterFinderMT<ClusterType, uint16_t, pd_type> &self,
-                py::array_t<double> frame, uint32_t chunk_number) {
-                 auto view = make_view_2d(frame);
-                 self.push_pedestal_std(view, chunk_number);
-             })
-
         .def(
             "find_clusters",
             [](ClusterFinderMT<ClusterType, uint16_t, pd_type> &self,

python/src/bind_ClusterVector.hpp

@@ -104,4 +104,47 @@ void define_ClusterVector(py::module &m, const std::string &typestr) {
           });
 }
 
+template <typename Type, uint8_t ClusterSizeX, uint8_t ClusterSizeY,
+          typename CoordType = uint16_t>
+void define_2x2_reduction(py::module &m) {
+    m.def(
+        "reduce_to_2x2",
+        [](const ClusterVector<
+            Cluster<Type, ClusterSizeX, ClusterSizeY, CoordType>> &cv) {
+            return new ClusterVector<Cluster<Type, 2, 2, CoordType>>(
+                reduce_to_2x2(cv));
+        },
+        R"(
+        
+        Reduce cluster to 2x2 subcluster by taking the 2x2 subcluster with 
+        the highest photon energy."
+        Parameters
+        ----------
+        cv : ClusterVector   
+        )",
+        py::arg("clustervector"));
+}
+
+template <typename Type, uint8_t ClusterSizeX, uint8_t ClusterSizeY,
+          typename CoordType = uint16_t>
+void define_3x3_reduction(py::module &m) {
+
+    m.def(
+        "reduce_to_3x3",
+        [](const ClusterVector<
+            Cluster<Type, ClusterSizeX, ClusterSizeY, CoordType>> &cv) {
+            return new ClusterVector<Cluster<Type, 3, 3, CoordType>>(
+                reduce_to_3x3(cv));
+        },
+        R"(
+        
+        Reduce cluster to 3x3 subcluster by taking the 3x3 subcluster with 
+        the highest photon energy."
+        Parameters
+        ----------
+        cv : ClusterVector   
+        )",
+        py::arg("clustervector"));
+}
+
 #pragma GCC diagnostic pop

python/src/module.cpp

@@ -47,7 +47,9 @@ double, 'f' for float)
     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);
+    register_calculate_eta<T, N, M, U>(m);                                     \
+    define_2x2_reduction<T, N, M, U>(m);                                       \
+    reduce_to_2x2<T, N, M, U>(m);
 
 PYBIND11_MODULE(_aare, m) {
     define_file_io_bindings(m);
@@ -84,4 +86,32 @@ PYBIND11_MODULE(_aare, m) {
     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);
+
+    // DEFINE_CLUSTER_BINDINGS(double, 2, 1, uint16_t, d);
+
+    define_3x3_reduction<int, 3, 3, uint16_t>(m);
+    define_3x3_reduction<double, 3, 3, uint16_t>(m);
+    define_3x3_reduction<float, 3, 3, uint16_t>(m);
+    define_3x3_reduction<int, 5, 5, uint16_t>(m);
+    define_3x3_reduction<double, 5, 5, uint16_t>(m);
+    define_3x3_reduction<float, 5, 5, uint16_t>(m);
+    define_3x3_reduction<int, 7, 7, uint16_t>(m);
+    define_3x3_reduction<double, 7, 7, uint16_t>(m);
+    define_3x3_reduction<float, 7, 7, uint16_t>(m);
+    define_3x3_reduction<int, 9, 9, uint16_t>(m);
+    define_3x3_reduction<double, 9, 9, uint16_t>(m);
+    define_3x3_reduction<float, 9, 9, uint16_t>(m);
+
+    reduce_to_3x3<int, 3, 3, uint16_t>(m);
+    reduce_to_3x3<double, 3, 3, uint16_t>(m);
+    reduce_to_3x3<float, 3, 3, uint16_t>(m);
+    reduce_to_3x3<int, 5, 5, uint16_t>(m);
+    reduce_to_3x3<double, 5, 5, uint16_t>(m);
+    reduce_to_3x3<float, 5, 5, uint16_t>(m);
+    reduce_to_3x3<int, 7, 7, uint16_t>(m);
+    reduce_to_3x3<double, 7, 7, uint16_t>(m);
+    reduce_to_3x3<float, 7, 7, uint16_t>(m);
+    reduce_to_3x3<int, 9, 9, uint16_t>(m);
+    reduce_to_3x3<double, 9, 9, uint16_t>(m);
+    reduce_to_3x3<float, 9, 9, uint16_t>(m);
 }

python/tests/test_Cluster.py

@@ -53,8 +53,8 @@ def test_Interpolator():
 
     assert interpolated_photons.size == 1
 
-    assert interpolated_photons[0]["x"] == -1 
-    assert interpolated_photons[0]["y"] == -1
+    assert interpolated_photons[0]["x"] == 0 
+    assert interpolated_photons[0]["y"] == 0
     assert interpolated_photons[0]["energy"] == 4 #eta_sum = 4, dx, dy = -1,-1 m_ietax = 0, m_ietay = 0
 
     clustervector = _aare.ClusterVector_Cluster2x2i()
@@ -84,7 +84,7 @@ def test_calculate_eta():
     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
+    assert eta2[1,1] == 0.4 #2/5
 
 def test_cluster_finder(): 
     """Test ClusterFinder""" 
@@ -101,6 +101,27 @@ def test_cluster_finder():
     assert clusters.size == 0
 
 
+def test_2x2_reduction(): 
+    """Test 2x2 Reduction"""
+    cluster = _aare.Cluster3x3i(5,5,np.array([1, 1, 1, 2, 3, 1, 2, 2, 1], dtype=np.int32))
+
+    reduced_cluster = _aare.reduce_to_2x2(cluster) 
+
+    assert reduced_cluster.x == 4
+    assert reduced_cluster.y == 5
+    assert (reduced_cluster.data == np.array([[2, 3], [2, 2]], dtype=np.int32)).all()
+    
+    
+def test_3x3_reduction(): 
+    """Test 3x3 Reduction"""
+    cluster = _aare.Cluster5x5d(5,5,np.array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 2.0, 2.0, 3.0,
+                                   1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], dtype=np.double))
+
+    reduced_cluster = _aare.reduce_to_3x3(cluster) 
+
+    assert reduced_cluster.x == 4
+    assert reduced_cluster.y == 5
+    assert (reduced_cluster.data == np.array([[1.0, 2.0, 1.0], [2.0, 2.0, 3.0], [1.0, 2.0, 1.0]], dtype=np.double)).all()
 
 
 

python/tests/test_ClusterVector.py

@@ -5,7 +5,7 @@ import time
 from pathlib import Path
 import pickle
 
-from aare import ClusterFile
+from aare import ClusterFile, ClusterVector
 from aare import _aare
 from conftest import test_data_path
 
@@ -51,4 +51,36 @@ def test_make_a_hitmap_from_cluster_vector():
     # print(img)
     # print(ref)
     assert (img == ref).all()
-    
+
+
+def test_2x2_reduction(): 
+    cv = ClusterVector((3,3))
+
+    cv.push_back(_aare.Cluster3x3i(5, 5, np.array([1, 1, 1, 2, 3, 1, 2, 2, 1], dtype=np.int32)))
+    cv.push_back(_aare.Cluster3x3i(5, 5, np.array([2, 2, 1, 2, 3, 1, 1, 1, 1], dtype=np.int32)))
+
+    reduced_cv = np.array(_aare.reduce_to_2x2(cv), copy=False) 
+
+    assert reduced_cv.size == 2
+    assert reduced_cv[0]["x"] == 4
+    assert reduced_cv[0]["y"] == 5
+    assert (reduced_cv[0]["data"] == np.array([[2, 3], [2, 2]], dtype=np.int32)).all()
+    assert reduced_cv[1]["x"] == 4
+    assert reduced_cv[1]["y"] == 6
+    assert (reduced_cv[1]["data"] == np.array([[2, 2], [2, 3]], dtype=np.int32)).all()
+    
+    
+def test_3x3_reduction(): 
+    cv = _aare.ClusterVector_Cluster5x5d()
+    
+    cv.push_back(_aare.Cluster5x5d(5,5,np.array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 2.0, 2.0, 3.0,
+                                   1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], dtype=np.double)))
+    cv.push_back(_aare.Cluster5x5d(5,5,np.array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 2.0, 2.0, 3.0,
+                                   1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], dtype=np.double)))
+    
+    reduced_cv = np.array(_aare.reduce_to_3x3(cv), copy=False)  
+
+    assert reduced_cv.size == 2
+    assert reduced_cv[0]["x"] == 4
+    assert reduced_cv[0]["y"] == 5
+    assert (reduced_cv[0]["data"] == np.array([[1.0, 2.0, 1.0], [2.0, 2.0, 3.0], [1.0, 2.0, 1.0]], dtype=np.double)).all()

python/tests/test_Interpolation.py

@@ -0,0 +1,148 @@
+import pytest
+import numpy as np 
+import boost_histogram as bh 
+import pickle
+from scipy.stats import multivariate_normal
+
+from aare import Interpolator, calculate_eta2
+from aare._aare import ClusterVector_Cluster2x2d, Cluster2x2d, Cluster3x3d, ClusterVector_Cluster3x3d
+
+from conftest import test_data_path
+
+pixel_width = 1e-4
+values = np.arange(0.5*pixel_width, 0.1, pixel_width)
+num_pixels = values.size
+X, Y = np.meshgrid(values, values)
+data_points = np.stack([X.ravel(), Y.ravel()], axis=1)
+variance = 10*pixel_width
+covariance_matrix = np.array([[variance, 0],[0, variance]])
+
+
+
+def create_photon_hit_with_gaussian_distribution(mean, covariance_matrix, data_points):
+    gaussian = multivariate_normal(mean=mean, cov=covariance_matrix)
+    probability_values = gaussian.pdf(data_points)
+    return (probability_values.reshape(X.shape)).round() #python bindings only support frame types of uint16_t
+
+def photon_hit_in_euclidean_space(cluster_center, pixels_per_superpixel, photon_hit): 
+    scaled_photon_hit_x = cluster_center - (1 - photon_hit[0][0])*pixels_per_superpixel*pixel_width 
+    scaled_photon_hit_y = cluster_center - (1 - photon_hit[0][1])*pixels_per_superpixel*pixel_width
+    return (scaled_photon_hit_x, scaled_photon_hit_y)
+
+def create_2x2cluster_from_frame(frame, pixels_per_superpixel):
+    return Cluster2x2d(1, 1, np.array([frame[0:pixels_per_superpixel, 0:pixels_per_superpixel].sum(), 
+                                            frame[0:pixels_per_superpixel, pixels_per_superpixel:2*pixels_per_superpixel].sum(), 
+                                            frame[pixels_per_superpixel:2*pixels_per_superpixel, 0:pixels_per_superpixel].sum(), 
+                                            frame[pixels_per_superpixel:2*pixels_per_superpixel, pixels_per_superpixel:2*pixels_per_superpixel].sum()], dtype=np.float64))
+
+
+def create_3x3cluster_from_frame(frame, pixels_per_superpixel):
+    return Cluster3x3d(1, 1, np.array([frame[0:pixels_per_superpixel, 0:pixels_per_superpixel].sum(), 
+                                    frame[0:pixels_per_superpixel, pixels_per_superpixel:2*pixels_per_superpixel].sum(), 
+                                    frame[0:pixels_per_superpixel, 2*pixels_per_superpixel:3*pixels_per_superpixel].sum(), 
+                                    frame[pixels_per_superpixel:2*pixels_per_superpixel, 0:pixels_per_superpixel].sum(), 
+                                    frame[pixels_per_superpixel:2*pixels_per_superpixel, pixels_per_superpixel:2*pixels_per_superpixel].sum(), 
+                                    frame[pixels_per_superpixel:2*pixels_per_superpixel, 2*pixels_per_superpixel:3*pixels_per_superpixel].sum(), 
+                                    frame[2*pixels_per_superpixel:3*pixels_per_superpixel, 0:pixels_per_superpixel].sum(), 
+                                    frame[2*pixels_per_superpixel:3*pixels_per_superpixel, pixels_per_superpixel:2*pixels_per_superpixel].sum(), 
+                                    frame[2*pixels_per_superpixel:3*pixels_per_superpixel, 2*pixels_per_superpixel:3*pixels_per_superpixel].sum()], dtype=np.float64))
+
+
+def calculate_eta_distribution(num_frames, pixels_per_superpixel, random_number_generator, cluster_2x2 = True): 
+    hist = bh.Histogram(
+    bh.axis.Regular(100, -0.2, 1.2), 
+    bh.axis.Regular(100, -0.2, 1.2), bh.axis.Regular(1, 0, num_pixels*num_pixels*1/(variance*2*np.pi))) 
+
+    for _ in range(0, num_frames):
+        mean_x = random_number_generator.uniform(pixels_per_superpixel*pixel_width, 2*pixels_per_superpixel*pixel_width)
+        mean_y = random_number_generator.uniform(pixels_per_superpixel*pixel_width, 2*pixels_per_superpixel*pixel_width)
+        frame = create_photon_hit_with_gaussian_distribution(np.array([mean_x, mean_y]), variance, data_points)
+
+        cluster = None
+
+        if cluster_2x2:
+            cluster = create_2x2cluster_from_frame(frame, pixels_per_superpixel)
+        else: 
+            cluster = create_3x3cluster_from_frame(frame, pixels_per_superpixel)
+
+        eta2 = calculate_eta2(cluster)
+        hist.fill(eta2[0], eta2[1], eta2[2])
+
+    return hist
+
+@pytest.mark.withdata
+def test_interpolation_of_2x2_cluster(test_data_path): 
+    """Test Interpolation of 2x2 cluster from Photon hit with Gaussian Distribution""" 
+
+    #TODO maybe better to compute in test instead of loading - depends on eta 
+    """ 
+    filename = test_data_path/"eta_distributions"/"eta_distribution_2x2cluster_gaussian.pkl"
+    with open(filename, "rb") as f:
+        eta_distribution = pickle.load(f)
+    """
+
+    num_frames = 1000
+    pixels_per_superpixel = int(num_pixels*0.5)
+    random_number_generator = np.random.default_rng(42)
+
+    eta_distribution = calculate_eta_distribution(num_frames, pixels_per_superpixel, random_number_generator)
+
+    interpolation = Interpolator(eta_distribution, eta_distribution.axes[0].edges[:-1], eta_distribution.axes[1].edges[:-1], eta_distribution.axes[2].edges[:-1])
+
+    #actual photon hit
+    mean = 1.2*pixels_per_superpixel*pixel_width
+    mean = np.array([mean, mean])
+    frame = create_photon_hit_with_gaussian_distribution(mean, covariance_matrix, data_points)
+    cluster = create_2x2cluster_from_frame(frame, pixels_per_superpixel)
+
+    clustervec = ClusterVector_Cluster2x2d()
+    clustervec.push_back(cluster)
+
+    interpolated_photon = interpolation.interpolate(clustervec)
+
+    assert interpolated_photon.size == 1
+
+    cluster_center = 1.5*pixels_per_superpixel*pixel_width
+
+    scaled_photon_hit = photon_hit_in_euclidean_space(cluster_center, pixels_per_superpixel, interpolated_photon)
+
+    assert (np.linalg.norm(scaled_photon_hit - mean) < np.linalg.norm(np.array([cluster_center, cluster_center] - mean)))
+
+
+@pytest.mark.withdata
+def test_interpolation_of_3x3_cluster(test_data_path): 
+    """Test Interpolation of 3x3 Cluster from Photon hit with Gaussian Distribution""" 
+
+    #TODO maybe better to compute in test instead of loading - depends on eta 
+    """ 
+    filename = test_data_path/"eta_distributions"/"eta_distribution_3x3cluster_gaussian.pkl"
+    with open(filename, "rb") as f:
+        eta_distribution = pickle.load(f)
+    """
+
+    num_frames = 1000
+    pixels_per_superpixel = int(num_pixels/3)
+    random_number_generator = np.random.default_rng(42)
+    eta_distribution = calculate_eta_distribution(num_frames, pixels_per_superpixel, random_number_generator, False)
+
+    interpolation = Interpolator(eta_distribution, eta_distribution.axes[0].edges[:-1], eta_distribution.axes[1].edges[:-1], eta_distribution.axes[2].edges[:-1])
+
+    #actual photon hit
+    mean = 1.2*pixels_per_superpixel*pixel_width
+    mean = np.array([mean, mean])
+    frame = create_photon_hit_with_gaussian_distribution(mean, covariance_matrix, data_points)
+    cluster = create_3x3cluster_from_frame(frame, pixels_per_superpixel)
+
+    clustervec = ClusterVector_Cluster3x3d()
+    clustervec.push_back(cluster)
+
+    interpolated_photon = interpolation.interpolate(clustervec)
+
+    assert interpolated_photon.size == 1
+
+    cluster_center = 1.5*pixels_per_superpixel*pixel_width
+
+    scaled_photon_hit = photon_hit_in_euclidean_space(cluster_center, pixels_per_superpixel, interpolated_photon)
+
+    assert (np.linalg.norm(scaled_photon_hit - mean) < np.linalg.norm(np.array([cluster_center, cluster_center] - mean)))
+

src/Cluster.test.cpp

@@ -18,4 +18,86 @@ TEST_CASE("Test sum of Cluster", "[.cluster]") {
     Cluster<int, 2, 2> cluster{0, 0, {1, 2, 3, 4}};
 
     CHECK(cluster.sum() == 10);
+}
+
+using ClusterTypes = std::variant<Cluster<int, 2, 2>, Cluster<int, 3, 3>,
+                                  Cluster<int, 5, 5>, Cluster<int, 2, 3>>;
+
+using ClusterTypesLargerThan2x2 =
+    std::variant<Cluster<int, 3, 3>, Cluster<int, 4, 4>, Cluster<int, 5, 5>>;
+
+TEST_CASE("Test reduce to 2x2 Cluster", "[.cluster]") {
+    auto [cluster, expected_reduced_cluster] = GENERATE(
+        std::make_tuple(ClusterTypes{Cluster<int, 2, 2>{5, 5, {1, 2, 3, 4}}},
+                        Cluster<int, 2, 2>{4, 6, {1, 2, 3, 4}}),
+        std::make_tuple(
+            ClusterTypes{Cluster<int, 3, 3>{5, 5, {1, 1, 1, 1, 3, 2, 1, 2, 2}}},
+            Cluster<int, 2, 2>{5, 5, {3, 2, 2, 2}}),
+        std::make_tuple(
+            ClusterTypes{Cluster<int, 3, 3>{5, 5, {1, 1, 1, 2, 3, 1, 2, 2, 1}}},
+            Cluster<int, 2, 2>{4, 5, {2, 3, 2, 2}}),
+        std::make_tuple(
+            ClusterTypes{Cluster<int, 3, 3>{5, 5, {2, 2, 1, 2, 3, 1, 1, 1, 1}}},
+            Cluster<int, 2, 2>{4, 6, {2, 2, 2, 3}}),
+        std::make_tuple(
+            ClusterTypes{Cluster<int, 3, 3>{5, 5, {1, 2, 2, 1, 3, 2, 1, 1, 1}}},
+            Cluster<int, 2, 2>{5, 6, {2, 2, 3, 2}}),
+        std::make_tuple(ClusterTypes{Cluster<int, 5, 5>{
+                            5, 5, {1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 3,
+                                   2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}}},
+                        Cluster<int, 2, 2>{5, 6, {2, 2, 3, 2}}),
+        std::make_tuple(ClusterTypes{Cluster<int, 5, 5>{
+                            5, 5, {1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 3,
+                                   1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}}},
+                        Cluster<int, 2, 2>{4, 6, {2, 2, 2, 3}}),
+        std::make_tuple(
+            ClusterTypes{Cluster<int, 2, 3>{5, 5, {2, 2, 3, 2, 1, 1}}},
+            Cluster<int, 2, 2>{4, 6, {2, 2, 3, 2}}));
+
+    auto reduced_cluster = std::visit(
+        [](const auto &clustertype) { return reduce_to_2x2(clustertype); },
+        cluster);
+
+    CHECK(reduced_cluster.x == expected_reduced_cluster.x);
+    CHECK(reduced_cluster.y == expected_reduced_cluster.y);
+    CHECK(std::equal(reduced_cluster.data.begin(),
+                     reduced_cluster.data.begin() + 4,
+                     expected_reduced_cluster.data.begin()));
+}
+
+TEST_CASE("Test reduce to 3x3 Cluster", "[.cluster]") {
+    auto [cluster, expected_reduced_cluster] = GENERATE(
+        std::make_tuple(ClusterTypesLargerThan2x2{Cluster<int, 3, 3>{
+                            5, 5, {1, 1, 1, 1, 3, 1, 1, 1, 1}}},
+                        Cluster<int, 3, 3>{5, 5, {1, 1, 1, 1, 3, 1, 1, 1, 1}}),
+        std::make_tuple(
+            ClusterTypesLargerThan2x2{Cluster<int, 4, 4>{
+                5, 5, {2, 2, 1, 1, 2, 2, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1}}},
+            Cluster<int, 3, 3>{4, 6, {2, 2, 1, 2, 2, 1, 1, 1, 3}}),
+        std::make_tuple(
+            ClusterTypesLargerThan2x2{Cluster<int, 4, 4>{
+                5, 5, {1, 1, 2, 2, 1, 1, 2, 2, 1, 1, 3, 1, 1, 1, 1, 1}}},
+            Cluster<int, 3, 3>{5, 6, {1, 2, 2, 1, 2, 2, 1, 3, 1}}),
+        std::make_tuple(
+            ClusterTypesLargerThan2x2{Cluster<int, 4, 4>{
+                5, 5, {1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 2, 1, 1, 2, 2}}},
+            Cluster<int, 3, 3>{5, 5, {1, 1, 1, 1, 3, 2, 1, 2, 2}}),
+        std::make_tuple(
+            ClusterTypesLargerThan2x2{Cluster<int, 4, 4>{
+                5, 5, {1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 1, 2, 2, 1, 1}}},
+            Cluster<int, 3, 3>{4, 5, {1, 1, 1, 2, 2, 3, 2, 2, 1}}),
+        std::make_tuple(ClusterTypesLargerThan2x2{Cluster<int, 5, 5>{
+                            5, 5, {1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 2, 3,
+                                   1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1}}},
+                        Cluster<int, 3, 3>{4, 5, {1, 2, 1, 2, 2, 3, 1, 2, 1}}));
+
+    auto reduced_cluster = std::visit(
+        [](const auto &clustertype) { return reduce_to_3x3(clustertype); },
+        cluster);
+
+    CHECK(reduced_cluster.x == expected_reduced_cluster.x);
+    CHECK(reduced_cluster.y == expected_reduced_cluster.y);
+    CHECK(std::equal(reduced_cluster.data.begin(),
+                     reduced_cluster.data.begin() + 9,
+                     expected_reduced_cluster.data.begin()));
 }

src/ClusterFile.test.cpp

@@ -279,7 +279,8 @@ TEST_CASE("Read cluster from multiple frame file", "[.with-data]") {
     }
 }
 
-TEST_CASE("Write cluster with potential padding", "[.with-data][.ClusterFile]") {
+TEST_CASE("Write cluster with potential padding",
+          "[.with-data][.ClusterFile]") {
 
     using ClusterType = Cluster<double, 3, 3>;
 
@@ -290,7 +291,7 @@ TEST_CASE("Write cluster with potential padding", "[.with-data][.ClusterFile]")
     ClusterFile<ClusterType> file(fpath, 1000, "w");
 
     ClusterVector<ClusterType> clustervec(2);
-    int16_t coordinate = 5;
+    uint16_t coordinate = 5;
     clustervec.push_back(ClusterType{
         coordinate, coordinate, {0., 0., 0., 0., 0., 0., 0., 0., 0.}});
     clustervec.push_back(ClusterType{

src/ClusterFinderMT.test.cpp

@@ -57,6 +57,7 @@ class ClusterFinderMTWrapper
     size_t m_sink_size() const { return this->m_sink.sizeGuess(); }
 };
 
+
 TEST_CASE("multithreaded cluster finder", "[.with-data]") {
     auto fpath =
         test_data_path() / "raw/moench03/cu_half_speed_master_4.json";
@@ -81,7 +82,8 @@ TEST_CASE("multithreaded cluster finder", "[.with-data]") {
     CHECK(cf.m_input_queues_are_empty() == true);
 
     for (size_t i = 0; i < n_frames_pd; ++i) {
-        cf.find_clusters(file.read_frame().view<uint16_t>());
+        auto frame = file.read_frame();
+        cf.find_clusters(frame.view<uint16_t>());
     }
 
     cf.stop();

src/RawFile.test.cpp

@@ -99,7 +99,8 @@ TEST_CASE("Read data from a jungfrau 500k single port raw file",
 }
 
 TEST_CASE("Read frame numbers from a raw file", "[.with-data]") {
-    auto fpath = test_data_path() / "raw/eiger" / "eiger_500k_16bit_master_0.json";
+    auto fpath =
+        test_data_path() / "raw/eiger" / "eiger_500k_16bit_master_0.json";
     REQUIRE(std::filesystem::exists(fpath));
 
     // we know this file has 3 frames with frame numbers 14, 15, 16
@@ -288,8 +289,7 @@ TEST_CASE("check find_geometry", "[.with-data]") {
     }
 }
 
-TEST_CASE("Open multi module file with ROI",
-          "[.with-data]") {
+TEST_CASE("Open multi module file with ROI", "[.with-data]") {
 
     auto fpath = test_data_path() / "raw/SingleChipROI/Data_master_0.json";
     REQUIRE(std::filesystem::exists(fpath));
@@ -319,4 +319,4 @@ TEST_CASE("Read file with unordered frames", "[.with-data]") {
     REQUIRE(std::filesystem::exists(fpath));
     File f(fpath);
     REQUIRE_THROWS((f.read_frame()));
-}
+}

src/RawMasterFile.cpp

@@ -64,6 +64,12 @@ const std::string &RawFileNameComponents::base_name() const {
 const std::string &RawFileNameComponents::ext() const { return m_ext; }
 int RawFileNameComponents::file_index() const { return m_file_index; }
 
+ScanParameters::ScanParameters(const bool enabled, const DACIndex dac,
+                               const int start, const int stop, const int step,
+                               const int64_t settleTime)
+    : m_enabled(enabled), m_dac(dac), m_start(start), m_stop(stop),
+      m_step(step), m_settleTime(settleTime){};
+
 // "[enabled\ndac dac 4\nstart 500\nstop 2200\nstep 5\nsettleTime 100us\n]"
 ScanParameters::ScanParameters(const std::string &par) {
     std::istringstream iss(par.substr(1, par.size() - 2));
@@ -72,7 +78,7 @@ ScanParameters::ScanParameters(const std::string &par) {
         if (line == "enabled") {
             m_enabled = true;
         } else if (line.find("dac") != std::string::npos) {
-            m_dac = line.substr(4);
+            m_dac = StringTo<DACIndex>(line.substr(4));
         } else if (line.find("start") != std::string::npos) {
             m_start = std::stoi(line.substr(6));
         } else if (line.find("stop") != std::string::npos) {
@@ -87,8 +93,9 @@ int ScanParameters::start() const { return m_start; }
 int ScanParameters::stop() const { return m_stop; }
 void ScanParameters::increment_stop() { m_stop += 1; }
 int ScanParameters::step() const { return m_step; }
-const std::string &ScanParameters::dac() const { return m_dac; }
+DACIndex ScanParameters::dac() const { return m_dac; }
 bool ScanParameters::enabled() const { return m_enabled; }
+int64_t ScanParameters::settleTime() const { return m_settleTime; }
 
 RawMasterFile::RawMasterFile(const std::filesystem::path &fpath)
     : m_fnc(fpath) {
@@ -170,6 +177,7 @@ void RawMasterFile::parse_json(const std::filesystem::path &fpath) {
     std::ifstream ifs(fpath);
     json j;
     ifs >> j;
+
     double v = j["Version"];
     m_version = fmt::format("{:.1f}", v);
 
@@ -181,7 +189,9 @@ void RawMasterFile::parse_json(const std::filesystem::path &fpath) {
         j["Geometry"]["x"]}; // TODO: isnt it only available for version > 7.1?
                              // - try block default should be 1x1
 
-    m_image_size_in_bytes = j["Image Size in bytes"];
+    m_image_size_in_bytes =
+        v < 8.0 ? j["Image Size in bytes"] : j["Image Size"];
+
     m_frames_in_file = j["Frames in File"];
     m_pixels_y = j["Pixels"]["y"];
     m_pixels_x = j["Pixels"]["x"];
@@ -206,7 +216,6 @@ void RawMasterFile::parse_json(const std::filesystem::path &fpath) {
     } catch (const json::out_of_range &e) {
         // keep the optional empty
     }
-
     // ----------------------------------------------------------------
     // Special treatment of analog flag because of Moench03
     try {
@@ -227,7 +236,6 @@ void RawMasterFile::parse_json(const std::filesystem::path &fpath) {
         m_analog_flag = 0;
     }
     //-----------------------------------------------------------------
-
     try {
         m_quad = j.at("Quad");
     } catch (const json::out_of_range &e) {
@@ -239,7 +247,6 @@ void RawMasterFile::parse_json(const std::filesystem::path &fpath) {
     // }catch (const json::out_of_range &e) {
     //     m_adc_mask = 0;
     // }
-
     try {
         int digital_flag = j.at("Digital Flag");
         if (digital_flag) {
@@ -248,7 +255,6 @@ void RawMasterFile::parse_json(const std::filesystem::path &fpath) {
     } catch (const json::out_of_range &e) {
         // keep the optional empty
     }
-
     try {
         m_transceiver_flag = j.at("Transceiver Flag");
         if (m_transceiver_flag) {
@@ -257,10 +263,20 @@ void RawMasterFile::parse_json(const std::filesystem::path &fpath) {
     } catch (const json::out_of_range &e) {
         // keep the optional empty
     }
-
     try {
-        std::string scan_parameters = j.at("Scan Parameters");
-        m_scan_parameters = ScanParameters(scan_parameters);
+        if (v < 8.0) {
+            std::string scan_parameters = j.at("Scan Parameters");
+            m_scan_parameters = ScanParameters(scan_parameters);
+        } else {
+            auto json_obj = j.at("Scan Parameters");
+            m_scan_parameters = ScanParameters(
+                json_obj.at("enable").get<int>(),
+                static_cast<DACIndex>(json_obj.at("dacInd").get<int>()),
+                json_obj.at("start offset").get<int>(),
+                json_obj.at("stop offset").get<int>(),
+                json_obj.at("step size").get<int>(),
+                json_obj.at("dac settle time ns").get<int>());
+        }
         if (v < 7.21) {
             m_scan_parameters
                 .increment_stop(); // adjust for endpoint being included
@@ -268,6 +284,7 @@ void RawMasterFile::parse_json(const std::filesystem::path &fpath) {
     } catch (const json::out_of_range &e) {
         // not a scan
     }
+
     try {
         m_udp_interfaces_per_module = {j.at("Number of UDP Interfaces"), 1};
     } catch (const json::out_of_range &e) {
@@ -277,35 +294,36 @@ void RawMasterFile::parse_json(const std::filesystem::path &fpath) {
             m_udp_interfaces_per_module = {1, 2};
         }
     }
-
     try {
         ROI tmp_roi;
-        auto obj = j.at("Receiver Roi");
-        tmp_roi.xmin = obj.at("xmin");
-        tmp_roi.xmax = obj.at("xmax");
-        tmp_roi.ymin = obj.at("ymin");
-        tmp_roi.ymax = obj.at("ymax");
+        if (v < 8.0) {
+            auto obj = j.at("Receiver Roi");
+            tmp_roi.xmin = obj.at("xmin");
+            tmp_roi.xmax = obj.at("xmax");
+            tmp_roi.ymin = obj.at("ymin");
+            tmp_roi.ymax = obj.at("ymax");
+        } else {
+            // TODO: for now only handle single ROI
+            auto obj = j.at("Receiver Rois");
+            tmp_roi.xmin = obj[0].at("xmin");
+            tmp_roi.xmax = obj[0].at("xmax");
+            tmp_roi.ymin = obj[0].at("ymin");
+            tmp_roi.ymax = obj[0].at("ymax");
+        }
 
         // if any of the values are set update the roi
         if (tmp_roi.xmin != 4294967295 || tmp_roi.xmax != 4294967295 ||
             tmp_roi.ymin != 4294967295 || tmp_roi.ymax != 4294967295) {
-
-            if (v < 7.21) {
-                tmp_roi.xmax++; // why is it updated
-                tmp_roi.ymax++;
-            }
+            tmp_roi.xmax++;
+            tmp_roi.ymax++;
             m_roi = tmp_roi;
         }
 
     } catch (const json::out_of_range &e) {
-        std::cout << e.what() << std::endl;
+        LOG(TLogLevel::logERROR) << e.what() << std::endl;
         // leave the optional empty
     }
 
-    // if we have an roi we need to update the geometry for the subfiles
-    if (m_roi) {
-    }
-
 // Update detector type for Moench
 // TODO! How does this work with old .raw master files?
 #ifdef AARE_VERBOSE

src/RawMasterFile.test.cpp

@@ -51,7 +51,7 @@ TEST_CASE("Parse scan parameters") {
     ScanParameters s("[enabled\ndac dac 4\nstart 500\nstop 2200\nstep "
                      "5\nsettleTime 100us\n]");
     REQUIRE(s.enabled());
-    REQUIRE(s.dac() == "dac 4");
+    REQUIRE(s.dac() == DACIndex::DAC_4);
     REQUIRE(s.start() == 500);
     REQUIRE(s.stop() == 2200);
     REQUIRE(s.step() == 5);
@@ -60,7 +60,7 @@ TEST_CASE("Parse scan parameters") {
 TEST_CASE("A disabled scan") {
     ScanParameters s("[disabled]");
     REQUIRE_FALSE(s.enabled());
-    REQUIRE(s.dac() == "");
+    REQUIRE(s.dac() == DACIndex::DAC_0);
     REQUIRE(s.start() == 0);
     REQUIRE(s.stop() == 0);
     REQUIRE(s.step() == 0);
@@ -68,7 +68,7 @@ TEST_CASE("A disabled scan") {
 
 TEST_CASE("Parse a master file in .json format", "[.integration]") {
     auto fpath =
-        test_data_path() / "jungfrau" / "jungfrau_single_master_0.json";
+        test_data_path() / "raw" / "jungfrau" / "jungfrau_single_master_0.json";
     REQUIRE(std::filesystem::exists(fpath));
     RawMasterFile f(fpath);
 
@@ -224,6 +224,41 @@ TEST_CASE("Parse a master file in .raw format", "[.integration]") {
     // Packets Caught Mask        : 64 bytes
 }
 
+TEST_CASE("Parse a master file in new .json format",
+          "[.integration][.width-data]") {
+
+    auto file_path =
+        test_data_path() / "raw" / "newmythen03" / "run_87_master_0.json";
+    REQUIRE(std::filesystem::exists(file_path));
+
+    RawMasterFile f(file_path);
+
+    // Version                    : 8.0
+    REQUIRE(f.version() == "8.0");
+
+    REQUIRE(f.detector_type() == DetectorType::Mythen3);
+    // Timing Mode                : auto
+    REQUIRE(f.timing_mode() == TimingMode::Auto);
+    // Geometry                   : [2, 1]
+    REQUIRE(f.geometry().col == 2);
+    REQUIRE(f.geometry().row == 1);
+    // Image Size                 : 5120 bytes
+    REQUIRE(f.image_size_in_bytes() == 5120);
+
+    REQUIRE(f.scan_parameters().enabled() == false);
+    REQUIRE(f.scan_parameters().dac() == DACIndex::DAC_0);
+    REQUIRE(f.scan_parameters().start() == 0);
+    REQUIRE(f.scan_parameters().stop() == 0);
+    REQUIRE(f.scan_parameters().step() == 0);
+    REQUIRE(f.scan_parameters().settleTime() == 0);
+
+    auto roi = f.roi().value();
+    REQUIRE(roi.xmin == 0);
+    REQUIRE(roi.xmax == 2559);
+    REQUIRE(roi.ymin == -1);
+    REQUIRE(roi.ymax == -1);
+}
+
 TEST_CASE("Read eiger master file", "[.integration]") {
     auto fpath = test_data_path() / "eiger" / "eiger_500k_32bit_master_0.json";
     REQUIRE(std::filesystem::exists(fpath));
@@ -292,4 +327,4 @@ TEST_CASE("Read eiger master file", "[.integration]") {
     //         "Packets Caught Mask": "64 bytes"
     //     }
     // }
-}
+}

src/defs.cpp

@@ -115,4 +115,186 @@ template <> FrameDiscardPolicy StringTo(const std::string &arg) {
 
 // template <> TimingMode StringTo<TimingMode>(std::string mode);
 
+template <> DACIndex StringTo(const std::string &arg) {
+    if (arg == "dac 0")
+        return DACIndex::DAC_0;
+    else if (arg == "dac 1")
+        return DACIndex::DAC_1;
+    else if (arg == "dac 2")
+        return DACIndex::DAC_2;
+    else if (arg == "dac 3")
+        return DACIndex::DAC_3;
+    else if (arg == "dac 4")
+        return DACIndex::DAC_4;
+    else if (arg == "dac 5")
+        return DACIndex::DAC_5;
+    else if (arg == "dac 6")
+        return DACIndex::DAC_6;
+    else if (arg == "dac 7")
+        return DACIndex::DAC_7;
+    else if (arg == "dac 8")
+        return DACIndex::DAC_8;
+    else if (arg == "dac 9")
+        return DACIndex::DAC_9;
+    else if (arg == "dac 10")
+        return DACIndex::DAC_10;
+    else if (arg == "dac 11")
+        return DACIndex::DAC_11;
+    else if (arg == "dac 12")
+        return DACIndex::DAC_12;
+    else if (arg == "dac 13")
+        return DACIndex::DAC_13;
+    else if (arg == "dac 14")
+        return DACIndex::DAC_14;
+    else if (arg == "dac 15")
+        return DACIndex::DAC_15;
+    else if (arg == "dac 16")
+        return DACIndex::DAC_16;
+    else if (arg == "dac 17")
+        return DACIndex::DAC_17;
+    else if (arg == "vsvp")
+        return DACIndex::VSVP;
+    else if (arg == "vtrim")
+        return DACIndex::VTRIM;
+    else if (arg == "vrpreamp")
+        return DACIndex::VRPREAMP;
+    else if (arg == "vrshaper")
+        return DACIndex::VRSHAPER;
+    else if (arg == "vsvn")
+        return DACIndex::VSVN;
+    else if (arg == "vtgstv")
+        return DACIndex::VTGSTV;
+    else if (arg == "vcmp_ll")
+        return DACIndex::VCMP_LL;
+    else if (arg == "vcmp_lr")
+        return DACIndex::VCMP_LR;
+    else if (arg == "vcal")
+        return DACIndex::VCAL;
+    else if (arg == "vcmp_rl")
+        return DACIndex::VCMP_RL;
+    else if (arg == "rxb_rb")
+        return DACIndex::RXB_RB;
+    else if (arg == "rxb_lb")
+        return DACIndex::RXB_LB;
+    else if (arg == "vcmp_rr")
+        return DACIndex::VCMP_RR;
+    else if (arg == "vcp")
+        return DACIndex::VCP;
+    else if (arg == "vcn")
+        return DACIndex::VCN;
+    else if (arg == "vishaper")
+        return DACIndex::VISHAPER;
+    else if (arg == "vthreshold")
+        return DACIndex::VTHRESHOLD;
+    else if (arg == "vref_ds")
+        return DACIndex::VREF_DS;
+    else if (arg == "vout_cm")
+        return DACIndex::VOUT_CM;
+    else if (arg == "vin_cm")
+        return DACIndex::VIN_CM;
+    else if (arg == "vref_comp")
+        return DACIndex::VREF_COMP;
+    else if (arg == "vb_comp")
+        return DACIndex::VB_COMP;
+    else if (arg == "vdd_prot")
+        return DACIndex::VDD_PROT;
+    else if (arg == "vin_com")
+        return DACIndex::VIN_COM;
+    else if (arg == "vref_prech")
+        return DACIndex::VREF_PRECH;
+    else if (arg == "vb_pixbuf")
+        return DACIndex::VB_PIXBUF;
+    else if (arg == "vb_ds")
+        return DACIndex::VB_DS;
+    else if (arg == "vref_h_adc")
+        return DACIndex::VREF_H_ADC;
+    else if (arg == "vb_comp_fe")
+        return DACIndex::VB_COMP_FE;
+    else if (arg == "vb_comp_adc")
+        return DACIndex::VB_COMP_ADC;
+    else if (arg == "vcom_cds")
+        return DACIndex::VCOM_CDS;
+    else if (arg == "vref_rstore")
+        return DACIndex::VREF_RSTORE;
+    else if (arg == "vb_opa_1st")
+        return DACIndex::VB_OPA_1ST;
+    else if (arg == "vref_comp_fe")
+        return DACIndex::VREF_COMP_FE;
+    else if (arg == "vcom_adc1")
+        return DACIndex::VCOM_ADC1;
+    else if (arg == "vref_l_adc")
+        return DACIndex::VREF_L_ADC;
+    else if (arg == "vref_cds")
+        return DACIndex::VREF_CDS;
+    else if (arg == "vb_cs")
+        return DACIndex::VB_CS;
+    else if (arg == "vb_opa_fd")
+        return DACIndex::VB_OPA_FD;
+    else if (arg == "vcom_adc2")
+        return DACIndex::VCOM_ADC2;
+    else if (arg == "vcassh")
+        return DACIndex::VCASSH;
+    else if (arg == "vth2")
+        return DACIndex::VTH2;
+    else if (arg == "vrshaper_n")
+        return DACIndex::VRSHAPER_N;
+    else if (arg == "vipre_out")
+        return DACIndex::VIPRE_OUT;
+    else if (arg == "vth3")
+        return DACIndex::VTH3;
+    else if (arg == "vth1")
+        return DACIndex::VTH1;
+    else if (arg == "vicin")
+        return DACIndex::VICIN;
+    else if (arg == "vcas")
+        return DACIndex::VCAS;
+    else if (arg == "vcal_n")
+        return DACIndex::VCAL_N;
+    else if (arg == "vipre")
+        return DACIndex::VIPRE;
+    else if (arg == "vcal_p")
+        return DACIndex::VCAL_P;
+    else if (arg == "vdcsh")
+        return DACIndex::VDCSH;
+    else if (arg == "vbp_colbuf")
+        return DACIndex::VBP_COLBUF;
+    else if (arg == "vb_sda")
+        return DACIndex::VB_SDA;
+    else if (arg == "vcasc_sfp")
+        return DACIndex::VCASC_SFP;
+    else if (arg == "vipre_cds")
+        return DACIndex::VIPRE_CDS;
+    else if (arg == "ibias_sfp")
+        return DACIndex::IBIAS_SFP;
+    else if (arg == "trimbits")
+        return DACIndex::TRIMBIT_SCAN;
+    else if (arg == "highvoltage")
+        return DACIndex::HIGH_VOLTAGE;
+    else if (arg == "iodelay")
+        return DACIndex::IO_DELAY;
+    else if (arg == "temp_adc")
+        return DACIndex::TEMPERATURE_ADC;
+    else if (arg == "temp_fpga")
+        return DACIndex::TEMPERATURE_FPGA;
+    else if (arg == "temp_fpgaext")
+        return DACIndex::TEMPERATURE_FPGAEXT;
+    else if (arg == "temp_10ge")
+        return DACIndex::TEMPERATURE_10GE;
+    else if (arg == "temp_dcdc")
+        return DACIndex::TEMPERATURE_DCDC;
+    else if (arg == "temp_sodl")
+        return DACIndex::TEMPERATURE_SODL;
+    else if (arg == "temp_sodr")
+        return DACIndex::TEMPERATURE_SODR;
+    else if (arg == "temp_fpgafl")
+        return DACIndex::TEMPERATURE_FPGA2;
+    else if (arg == "temp_fpgafr")
+        return DACIndex::TEMPERATURE_FPGA3;
+    else if (arg == "temp_slowadc")
+        return DACIndex::SLOW_ADC_TEMP;
+    else
+        throw std::invalid_argument("Could not decode DACIndex from: \"" + arg +
+                                    "\"");
+}
+
 } // namespace aare

update_version.py

@@ -7,6 +7,7 @@ Script to update VERSION file with semantic versioning if provided as an argumen
 import sys
 import os
 import re
+from datetime import datetime
 
 from packaging.version import Version, InvalidVersion
 
@@ -26,9 +27,9 @@ def get_version():
 
     # Check at least one argument is passed
     if len(sys.argv) < 2:
-        return "0.0.0"
-
-    version = sys.argv[1]
+        version =   datetime.today().strftime('%Y.%-m.%-d')
+    else: 
+        version = sys.argv[1]
 
     try:
         v = Version(version)  # normalize check if version follows PEP 440 specification
@@ -54,4 +55,4 @@ def write_version_to_file(version):
 if __name__ == "__main__":
 
     version = get_version()
-    write_version_to_file(version)
+    write_version_to_file(version)