add stride-aware constructor for NDView

MasterFile supports reading new json file format (backwards compatible
for older versions)
Multiple ROI's not supported yet

CMakeLists.txt

@@ -368,6 +368,7 @@ set(PUBLICHEADERS
 
 
 set(SourceFiles
+    ${CMAKE_CURRENT_SOURCE_DIR}/src/calibration.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/src/CtbRawFile.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/src/decode.cpp
     ${CMAKE_CURRENT_SOURCE_DIR}/src/defs.cpp
@@ -387,7 +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 
@@ -411,6 +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()
@@ -430,13 +433,10 @@ 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
+            ${CMAKE_CURRENT_SOURCE_DIR}/src/calibration.test.cpp
             ${CMAKE_CURRENT_SOURCE_DIR}/src/defs.test.cpp
             ${CMAKE_CURRENT_SOURCE_DIR}/src/decode.test.cpp
             ${CMAKE_CURRENT_SOURCE_DIR}/src/Dtype.test.cpp
@@ -463,6 +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}"
@@ -472,7 +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

@@ -1,7 +1,27 @@
 # Release notes
 
+### 2025.10.1
 
-### 2025.07.18
+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
+
+
+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:
 
@@ -15,7 +35,7 @@ Bugfixes:
 - Removed unused file: ClusterFile.cpp 
 
 
-### 2025.05.22
+### 2025.5.22
 
 Features:
 
@@ -28,3 +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

@@ -3,3 +3,14 @@ python:
     - 3.12
     - 3.13
 
+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

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.

docs/src/pycalibration.rst

@@ -17,8 +17,24 @@ Functions for applying calibration to data.
     # Apply calibration to raw data to convert from raw ADC values to keV
     data = aare.apply_calibration(raw_data, pd=pedestal, cal=calibration)
 
+    # If you pass a 2D pedestal and calibration only G0 will be used for the conversion
+    # Pixels that switched to G1 or G2 will be set to 0
+    data = aare.apply_calibration(raw_data, pd=pedestal[0], cal=calibration[0])
+
+    
+
 .. py:currentmodule:: aare
 
 .. autofunction:: apply_calibration
 
 .. autofunction:: load_calibration
+
+.. autofunction:: calculate_pedestal
+
+.. autofunction:: calculate_pedestal_float
+
+.. autofunction:: calculate_pedestal_g0
+
+.. autofunction:: calculate_pedestal_g0_float
+
+.. autofunction:: count_switching_pixels

include/aare/CalculateEta.hpp

@@ -28,7 +28,7 @@ enum class pixel : int {
 template <typename T> struct Eta2 {
     double x;
     double y;
-    int c;
+    int c{0};
     T sum;
 };
 
@@ -70,6 +70,8 @@ calculate_eta2(const Cluster<T, ClusterSizeX, ClusterSizeY, CoordType> &cl) {
     size_t index_bottom_left_max_2x2_subcluster =
         (int(c / (ClusterSizeX - 1))) * ClusterSizeX + c % (ClusterSizeX - 1);
 
+    // calculate direction of gradient
+
     // 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 &&
@@ -128,12 +130,15 @@ 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[1]) /
+                (cl.data[0] + cl.data[1]); // 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[2]) /
+                (cl.data[0] + cl.data[2]); // 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;
 }
 
@@ -150,13 +155,11 @@ 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/Cluster.hpp

@@ -8,6 +8,7 @@
 
 #pragma once
 
+#include "logger.hpp"
 #include <algorithm>
 #include <array>
 #include <cstdint>
@@ -74,6 +75,163 @@ 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

@@ -144,9 +144,9 @@ class ClusterFinder {
                                     static_cast<CT>(
                                         m_pedestal.mean(iy + ir, ix + ic));
                                 cluster.data[i] =
-                                    tmp; // Watch for out of bounds access
-                                i++;
+                                    tmp; // Watch for out of bounds access                                
                             }
+                            i++;
                         }
                     }
 

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/NDArray.hpp

@@ -25,7 +25,7 @@ template <typename T, ssize_t Ndim = 2>
 class NDArray : public ArrayExpr<NDArray<T, Ndim>, Ndim> {
     std::array<ssize_t, Ndim> shape_;
     std::array<ssize_t, Ndim> strides_;
-    size_t size_{};
+    size_t size_{}; //TODO! do we need to store size when we have shape?
     T *data_;
 
   public:
@@ -33,7 +33,7 @@ class NDArray : public ArrayExpr<NDArray<T, Ndim>, Ndim> {
      * @brief Default constructor. Will construct an empty NDArray.
      *
      */
-    NDArray() : shape_(), strides_(c_strides<Ndim>(shape_)), data_(nullptr){};
+    NDArray() : shape_(), strides_(c_strides<Ndim>(shape_)), data_(nullptr) {};
 
     /**
      * @brief Construct a new NDArray object with a given shape.
@@ -43,8 +43,7 @@ class NDArray : public ArrayExpr<NDArray<T, Ndim>, Ndim> {
      */
     explicit NDArray(std::array<ssize_t, Ndim> shape)
         : shape_(shape), strides_(c_strides<Ndim>(shape_)),
-          size_(std::accumulate(shape_.begin(), shape_.end(), 1,
-                                std::multiplies<>())),
+          size_(num_elements(shape_)),
           data_(new T[size_]) {}
 
     /**
@@ -79,6 +78,24 @@ class NDArray : public ArrayExpr<NDArray<T, Ndim>, Ndim> {
         other.reset(); // TODO! is this necessary?
     }
 
+
+    //Move constructor from an an array with Ndim + 1
+    template <ssize_t M, typename = std::enable_if_t<(M == Ndim + 1)>>
+    NDArray(NDArray<T, M> &&other) 
+        : shape_(drop_first_dim(other.shape())),
+          strides_(c_strides<Ndim>(shape_)), size_(num_elements(shape_)),
+          data_(other.data()) {
+
+            // For now only allow move if the size matches, to avoid unreachable data
+            // if the use case arises we can remove this check
+            if(size() != other.size()) {
+                data_ = nullptr; // avoid double free, other will clean up the memory in it's destructor
+                throw std::runtime_error(LOCATION +
+                                         "Size mismatch in move constructor of NDArray<T, Ndim-1>");
+            }
+        other.reset();
+    }
+
     // Copy constructor
     NDArray(const NDArray &other)
         : shape_(other.shape_), strides_(c_strides<Ndim>(shape_)),
@@ -380,12 +397,6 @@ NDArray<T, Ndim> NDArray<T, Ndim>::operator*(const T &value) {
     result *= value;
     return result;
 }
-// template <typename T, ssize_t Ndim> void NDArray<T, Ndim>::Print() {
-//     if (shape_[0] < 20 && shape_[1] < 20)
-//         Print_all();
-//     else
-//         Print_some();
-// }
 
 template <typename T, ssize_t Ndim>
 std::ostream &operator<<(std::ostream &os, const NDArray<T, Ndim> &arr) {
@@ -437,4 +448,23 @@ NDArray<T, Ndim> load(const std::string &pathname,
     return img;
 }
 
+template <typename RT, typename NT, typename DT, ssize_t Ndim>
+NDArray<RT, Ndim> safe_divide(const NDArray<NT, Ndim> &numerator,
+                              const NDArray<DT, Ndim> &denominator) {
+    if (numerator.shape() != denominator.shape()) {
+        throw std::runtime_error(
+            "Shapes of numerator and denominator must match");
+    }
+    NDArray<RT, Ndim> result(numerator.shape());
+    for (ssize_t i = 0; i < numerator.size(); ++i) {
+        if (denominator[i] != 0) {
+            result[i] =
+                static_cast<RT>(numerator[i]) / static_cast<RT>(denominator[i]);
+        } else {
+            result[i] = RT{0}; // or handle division by zero as needed
+        }
+    }
+    return result;
+}
+
 } // namespace aare

include/aare/NDView.hpp

@@ -26,6 +26,33 @@ Shape<Ndim> make_shape(const std::vector<size_t> &shape) {
     return arr;
 }
 
+
+/**
+ * @brief Helper function to drop the first dimension of a shape.
+ * This is useful when you want to create a 2D view from a 3D array.
+ * @param shape The shape to drop the first dimension from.
+ * @return A new shape with the first dimension dropped.
+ */
+template<size_t Ndim>
+Shape<Ndim-1> drop_first_dim(const Shape<Ndim> &shape) {
+    static_assert(Ndim > 1, "Cannot drop first dimension from a 1D shape");
+    Shape<Ndim - 1> new_shape;
+    std::copy(shape.begin() + 1, shape.end(), new_shape.begin());
+    return new_shape;
+}
+
+/**
+ * @brief Helper function when constructing NDArray/NDView. Calculates the number
+ * of elements in the resulting array from a shape.
+ * @param shape The shape to calculate the number of elements for.
+ * @return The number of elements in and NDArray/NDView of that shape.
+ */
+template <size_t Ndim>
+size_t num_elements(const Shape<Ndim> &shape) {
+    return std::accumulate(shape.begin(), shape.end(), 1,
+                           std::multiplies<size_t>());
+}
+
 template <ssize_t Dim = 0, typename Strides>
 ssize_t element_offset(const Strides & /*unused*/) {
     return 0;
@@ -67,16 +94,33 @@ class NDView : public ArrayExpr<NDView<T, Ndim>, Ndim> {
           size_(std::accumulate(std::begin(shape), std::end(shape), 1,
                                 std::multiplies<>())) {}
 
+    // stride-aware constructor
+    NDView(T* buffer, std::array<ssize_t, Ndim> shape, std::array<ssize_t, Ndim> strides)
+        : buffer_(buffer), shape_(shape), strides_(strides),
+          size_(std::accumulate(std::begin(shape), std::end(shape), 1,
+                                std::multiplies<>())) {}
+                   
     template <typename... Ix>
     std::enable_if_t<sizeof...(Ix) == Ndim, T &> operator()(Ix... index) {
         return buffer_[element_offset(strides_, index...)];
     }
 
     template <typename... Ix>
-    const std::enable_if_t<sizeof...(Ix) == Ndim, T &> operator()(Ix... index) const {
+    std::enable_if_t<sizeof...(Ix) == 1 && (Ndim > 1), NDView<T, Ndim - 1>> operator()(Ix... index) {
+        // return a view of the next dimension
+        std::array<ssize_t, Ndim - 1> new_shape{};
+        std::copy_n(shape_.begin() + 1, Ndim - 1, new_shape.begin());
+        return NDView<T, Ndim - 1>(&buffer_[element_offset(strides_, index...)],
+                                   new_shape);
+        
+    }
+
+    template <typename... Ix>
+    std::enable_if_t<sizeof...(Ix) == Ndim, const T &> operator()(Ix... index) const {
         return buffer_[element_offset(strides_, index...)];
     }
 
+
     ssize_t size() const { return static_cast<ssize_t>(size_); }
     size_t total_bytes() const { return size_ * sizeof(T); }
     std::array<ssize_t, Ndim> strides() const noexcept { return strides_; }
@@ -85,9 +129,19 @@ class NDView : public ArrayExpr<NDView<T, Ndim>, Ndim> {
     T *end() { return buffer_ + size_; }
     T const *begin() const { return buffer_; }
     T const *end() const { return buffer_ + size_; }
-    T &operator()(ssize_t i)  { return buffer_[i]; }
+    
+    
+
+
+
+    /**
+     * @brief Access element at index i.
+     */
     T &operator[](ssize_t i)  { return buffer_[i]; }
-    const T &operator()(ssize_t i) const { return buffer_[i]; }
+
+    /**
+     * @brief Access element at index i.
+     */
     const T &operator[](ssize_t i) const { return buffer_[i]; }
 
     bool operator==(const NDView &other) const {
@@ -157,6 +211,22 @@ class NDView : public ArrayExpr<NDView<T, Ndim>, Ndim> {
     const T *data() const { return buffer_; }
     void print_all() const;
 
+    /**
+     * @brief Create a subview of a range of the first dimension. 
+     * This is useful for splitting a batches of frames in parallel processing.
+     * @param first The first index of the subview (inclusive).
+     * @param last The last index of the subview (exclusive).
+     * @return A new NDView that is a subview of the current view.
+     * @throws std::runtime_error if the range is invalid.
+     */
+    NDView sub_view(ssize_t first, ssize_t last) const {
+        if (first < 0 || last > shape_[0] || first >= last)
+            throw std::runtime_error(LOCATION + "Invalid sub_view range");
+        auto new_shape = shape_;
+        new_shape[0] = last - first;
+        return NDView(buffer_ + first * strides_[0], new_shape);
+    }
+
   private:
     T *buffer_{nullptr};
     std::array<ssize_t, Ndim> strides_{};

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/VarClusterFinder.hpp

@@ -240,14 +240,14 @@ template <typename T> void VarClusterFinder<T>::first_pass() {
 
     for (ssize_t i = 0; i < original_.size(); ++i) {
         if (use_noise_map)
-            threshold_ = 5 * noiseMap(i);
-        binary_(i) = (original_(i) > threshold_);
+            threshold_ = 5 * noiseMap[i];
+        binary_[i] = (original_[i] > threshold_);
     }
 
     for (int i = 0; i < shape_[0]; ++i) {
         for (int j = 0; j < shape_[1]; ++j) {
 
-            // do we have someting to process?
+            // do we have something to process?
             if (binary_(i, j)) {
                 auto tmp = check_neighbours(i, j);
                 if (tmp != 0) {

include/aare/calibration.hpp

@@ -1,6 +1,9 @@
 #pragma once
 
+#include "aare/NDArray.hpp"
+#include "aare/NDView.hpp"
 #include "aare/defs.hpp"
+#include "aare/utils/par.hpp"
 #include "aare/utils/task.hpp"
 #include <cstdint>
 #include <future>
@@ -55,32 +58,152 @@ ALWAYS_INLINE std::pair<uint16_t, int16_t> get_value_and_gain(uint16_t raw) {
 
 template <class T>
 void apply_calibration_impl(NDView<T, 3> res, NDView<uint16_t, 3> raw_data,
-                       NDView<T, 3> ped, NDView<T, 3> cal, int start,
-                       int stop) {
+                            NDView<T, 3> ped, NDView<T, 3> cal, int start,
+                            int stop) {
 
     for (int frame_nr = start; frame_nr != stop; ++frame_nr) {
         for (int row = 0; row != raw_data.shape(1); ++row) {
             for (int col = 0; col != raw_data.shape(2); ++col) {
-                auto [value, gain] = get_value_and_gain(raw_data(frame_nr, row, col));
+                auto [value, gain] =
+                    get_value_and_gain(raw_data(frame_nr, row, col));
+
+                // Using multiplication does not seem to speed up the code here
+                // ADU/keV is the standard unit for the calibration which
+                // means rewriting the formula is not worth it.
                 res(frame_nr, row, col) =
-                    (value - ped(gain, row, col)) / cal(gain, row, col); //TODO! use multiplication
+                    (value - ped(gain, row, col)) / cal(gain, row, col);
             }
         }
     }
 }
 
 template <class T>
+void apply_calibration_impl(NDView<T, 3> res, NDView<uint16_t, 3> raw_data,
+                            NDView<T, 2> ped, NDView<T, 2> cal, int start,
+                            int stop) {
+
+    for (int frame_nr = start; frame_nr != stop; ++frame_nr) {
+        for (int row = 0; row != raw_data.shape(1); ++row) {
+            for (int col = 0; col != raw_data.shape(2); ++col) {
+                auto [value, gain] =
+                    get_value_and_gain(raw_data(frame_nr, row, col));
+
+                // Using multiplication does not seem to speed up the code here
+                // ADU/keV is the standard unit for the calibration which
+                // means rewriting the formula is not worth it.
+
+                // Set the value to 0 if the gain is not 0
+                if (gain == 0)
+                    res(frame_nr, row, col) =
+                        (value - ped(row, col)) / cal(row, col);
+                else
+                    res(frame_nr, row, col) = 0;
+            }
+        }
+    }
+}
+
+template <class T, ssize_t Ndim = 3>
 void apply_calibration(NDView<T, 3> res, NDView<uint16_t, 3> raw_data,
-                       NDView<T, 3> ped, NDView<T, 3> cal,
+                       NDView<T, Ndim> ped, NDView<T, Ndim> cal,
                        ssize_t n_threads = 4) {
     std::vector<std::future<void>> futures;
     futures.reserve(n_threads);
     auto limits = split_task(0, raw_data.shape(0), n_threads);
     for (const auto &lim : limits)
-        futures.push_back(std::async(&apply_calibration_impl<T>, res, raw_data, ped, cal,
-                                     lim.first, lim.second));
+        futures.push_back(std::async(
+            static_cast<void (*)(NDView<T, 3>, NDView<uint16_t, 3>,
+                                 NDView<T, Ndim>, NDView<T, Ndim>, int, int)>(
+                apply_calibration_impl),
+            res, raw_data, ped, cal, lim.first, lim.second));
     for (auto &f : futures)
         f.get();
 }
 
+template <bool only_gain0>
+std::pair<NDArray<size_t, 3>, NDArray<size_t, 3>>
+sum_and_count_per_gain(NDView<uint16_t, 3> raw_data) {
+    constexpr ssize_t num_gains = only_gain0 ? 1 : 3;
+    NDArray<size_t, 3> accumulator(
+        std::array<ssize_t, 3>{num_gains, raw_data.shape(1), raw_data.shape(2)},
+        0);
+    NDArray<size_t, 3> count(
+        std::array<ssize_t, 3>{num_gains, raw_data.shape(1), raw_data.shape(2)},
+        0);
+    for (int frame_nr = 0; frame_nr != raw_data.shape(0); ++frame_nr) {
+        for (int row = 0; row != raw_data.shape(1); ++row) {
+            for (int col = 0; col != raw_data.shape(2); ++col) {
+                auto [value, gain] =
+                    get_value_and_gain(raw_data(frame_nr, row, col));
+                if (gain != 0 && only_gain0)
+                    continue;
+                accumulator(gain, row, col) += value;
+                count(gain, row, col) += 1;
+            }
+        }
+    }
+
+    return {std::move(accumulator), std::move(count)};
+}
+
+template <typename T, bool only_gain0 = false>
+NDArray<T, 3 - static_cast<ssize_t>(only_gain0)>
+calculate_pedestal(NDView<uint16_t, 3> raw_data, ssize_t n_threads) {
+
+    constexpr ssize_t num_gains = only_gain0 ? 1 : 3;
+    std::vector<std::future<std::pair<NDArray<size_t, 3>, NDArray<size_t, 3>>>>
+        futures;
+    futures.reserve(n_threads);
+
+    auto subviews = make_subviews(raw_data, n_threads);
+
+    for (auto view : subviews) {
+        futures.push_back(std::async(
+            static_cast<std::pair<NDArray<size_t, 3>, NDArray<size_t, 3>> (*)(
+                NDView<uint16_t, 3>)>(&sum_and_count_per_gain<only_gain0>),
+            view));
+    }
+    Shape<3> shape{num_gains, raw_data.shape(1), raw_data.shape(2)};
+    NDArray<size_t, 3> accumulator(shape, 0);
+    NDArray<size_t, 3> count(shape, 0);
+
+    // Combine the results from the futures
+    for (auto &f : futures) {
+        auto [acc, cnt] = f.get();
+        accumulator += acc;
+        count += cnt;
+    }
+
+
+    // Will move to a NDArray<T, 3 - static_cast<ssize_t>(only_gain0)>
+    // if only_gain0 is true
+    return safe_divide<T>(accumulator, count);
+
+}
+
+/**
+ * @brief Count the number of switching pixels in the raw data.
+ * This function counts the number of pixels that switch between G1 and G2 gain.
+ * It returns an NDArray with the number of switching pixels per pixel.
+ * @param raw_data The NDView containing the raw data
+ * @return An NDArray with the number of switching pixels per pixel
+ */
+NDArray<int, 2> count_switching_pixels(NDView<uint16_t, 3> raw_data);
+
+/**
+ * @brief Count the number of switching pixels in the raw data.
+ * This function counts the number of pixels that switch between G1 and G2 gain.
+ * It returns an NDArray with the number of switching pixels per pixel.
+ * @param raw_data The NDView containing the raw data
+ * @param n_threads The number of threads to use for parallel processing
+ * @return An NDArray with the number of switching pixels per pixel
+ */
+NDArray<int, 2> count_switching_pixels(NDView<uint16_t, 3> raw_data,
+                                       ssize_t n_threads);
+
+template <typename T>
+auto calculate_pedestal_g0(NDView<uint16_t, 3> raw_data, ssize_t n_threads) {
+    return calculate_pedestal<T, true>(raw_data, n_threads);
+}
+
 } // namespace aare

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

include/aare/utils/par.hpp

@@ -1,7 +1,10 @@
+#pragma once
 #include <thread>
 #include <utility>
 #include <vector>
 
+#include "aare/utils/task.hpp"
+
 namespace aare {
 
 template <typename F>
@@ -15,4 +18,17 @@ void RunInParallel(F func, const std::vector<std::pair<int, int>> &tasks) {
         thread.join();
     }
 }
+
+
+template <typename T>
+std::vector<NDView<T,3>> make_subviews(NDView<T, 3> &data, ssize_t n_threads) {
+    std::vector<NDView<T, 3>> subviews;
+    subviews.reserve(n_threads);
+    auto limits = split_task(0, data.shape(0), n_threads);
+    for (const auto &lim : limits) {
+        subviews.push_back(data.sub_view(lim.first, lim.second));
+    }
+    return subviews;
+}
+
 } // namespace aare

include/aare/utils/task.hpp

@@ -1,4 +1,4 @@
-
+#pragma once
 #include <utility>
 #include <vector>
 

python/aare/ClusterFinder.py

@@ -46,14 +46,13 @@ def ClusterFinderMT(image_size, cluster_size = (3,3), dtype=np.int32, n_sigma=5,
     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
 
@@ -32,6 +32,7 @@ from .utils import random_pixels, random_pixel, flat_list, add_colorbar
 from .func import *
 
 from .calibration import *
-from ._aare import apply_calibration
+from ._aare import apply_calibration, count_switching_pixels
+from ._aare import calculate_pedestal, calculate_pedestal_float, calculate_pedestal_g0, calculate_pedestal_g0_float
 
 from ._aare import VarClusterFinder

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([](uint8_t x, uint8_t 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_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/bind_calibration.hpp

@@ -17,27 +17,137 @@ py::array_t<DataType> pybind_apply_calibration(
         calibration,
     int n_threads = 4) {
 
-    auto data_span = make_view_3d(data);
-    auto ped = make_view_3d(pedestal);
-    auto cal = make_view_3d(calibration);
-
+    auto data_span = make_view_3d(data); // data is always 3D
     /* No pointer is passed, so NumPy will allocate the buffer */
     auto result = py::array_t<DataType>(data_span.shape());
     auto res = make_view_3d(result);
-
-    aare::apply_calibration<DataType>(res, data_span, ped, cal, n_threads);
-
+    if (data.ndim() == 3 && pedestal.ndim() == 3 && calibration.ndim() == 3) {
+        auto ped = make_view_3d(pedestal);
+        auto cal = make_view_3d(calibration);
+        aare::apply_calibration<DataType, 3>(res, data_span, ped, cal,
+                                             n_threads);
+    } else if (data.ndim() == 3 && pedestal.ndim() == 2 &&
+               calibration.ndim() == 2) {
+        auto ped = make_view_2d(pedestal);
+        auto cal = make_view_2d(calibration);
+        aare::apply_calibration<DataType, 2>(res, data_span, ped, cal,
+                                             n_threads);
+    } else {
+        throw std::runtime_error(
+            "Invalid number of dimensions for data, pedestal or calibration");
+    }
     return result;
 }
 
+py::array_t<int> pybind_count_switching_pixels(
+    py::array_t<uint16_t, py::array::c_style | py::array::forcecast> data,
+    ssize_t n_threads = 4) {
+
+    auto data_span = make_view_3d(data);
+    auto arr = new NDArray<int, 2>{};
+    *arr = aare::count_switching_pixels(data_span, n_threads);
+    return return_image_data(arr);
+}
+
+template <typename T>
+py::array_t<T> pybind_calculate_pedestal(
+    py::array_t<uint16_t, py::array::c_style | py::array::forcecast> data,
+    ssize_t n_threads) {
+
+    auto data_span = make_view_3d(data);
+    auto arr = new NDArray<T, 3>{};
+    *arr = aare::calculate_pedestal<T, false>(data_span, n_threads);
+    return return_image_data(arr);
+}
+
+template <typename T>
+py::array_t<T> pybind_calculate_pedestal_g0(
+    py::array_t<uint16_t, py::array::c_style | py::array::forcecast> data,
+    ssize_t n_threads) {
+
+    auto data_span = make_view_3d(data);
+    auto arr = new NDArray<T, 2>{};
+    *arr = aare::calculate_pedestal<T, true>(data_span, n_threads);
+    return return_image_data(arr);
+}
+
 void bind_calibration(py::module &m) {
+    m.def("apply_calibration", &pybind_apply_calibration<double>,
+          py::arg("raw_data").noconvert(), py::kw_only(),
+          py::arg("pd").noconvert(), py::arg("cal").noconvert(),
+          py::arg("n_threads") = 4);
+
     m.def("apply_calibration", &pybind_apply_calibration<float>,
           py::arg("raw_data").noconvert(), py::kw_only(),
           py::arg("pd").noconvert(), py::arg("cal").noconvert(),
           py::arg("n_threads") = 4);
 
-    m.def("apply_calibration", &pybind_apply_calibration<double>,
+    m.def("count_switching_pixels", &pybind_count_switching_pixels,
+          R"(
+        Count the number of time each pixel switches to G1 or G2.
+
+        Parameters
+        ----------
+        raw_data : array_like
+            3D array of shape (frames, rows, cols) to count the switching pixels from.
+        n_threads : int 
+            The number of threads to use for the calculation.
+        )",
           py::arg("raw_data").noconvert(), py::kw_only(),
-          py::arg("pd").noconvert(), py::arg("cal").noconvert(),
           py::arg("n_threads") = 4);
+
+    m.def("calculate_pedestal", &pybind_calculate_pedestal<double>,
+          R"(
+        Calculate the pedestal for all three gains and return the result as a 3D array of doubles.
+
+        Parameters
+        ----------
+        raw_data : array_like
+            3D array of shape (frames, rows, cols) to calculate the pedestal from.
+            Needs to contain data for all three gains (G0, G1, G2).
+        n_threads : int 
+            The number of threads to use for the calculation.
+        )",
+          py::arg("raw_data").noconvert(), py::arg("n_threads") = 4);
+
+    m.def("calculate_pedestal_float", &pybind_calculate_pedestal<float>,
+          R"(
+        Same as `calculate_pedestal` but returns a 3D array of floats.
+
+        Parameters
+        ----------
+        raw_data : array_like
+            3D array of shape (frames, rows, cols) to calculate the pedestal from.
+            Needs to contain data for all three gains (G0, G1, G2).
+        n_threads : int 
+            The number of threads to use for the calculation.
+        )",
+          py::arg("raw_data").noconvert(), py::arg("n_threads") = 4);
+
+    m.def("calculate_pedestal_g0", &pybind_calculate_pedestal_g0<double>,
+          R"(
+        Calculate the pedestal for G0 and return the result as a 2D array of doubles.
+        Pixels in G1 and G2 are ignored.
+
+        Parameters
+        ----------
+        raw_data : array_like
+            3D array of shape (frames, rows, cols) to calculate the pedestal from.
+        n_threads : int 
+            The number of threads to use for the calculation.
+        )",
+          py::arg("raw_data").noconvert(), py::arg("n_threads") = 4);
+
+    m.def("calculate_pedestal_g0_float", &pybind_calculate_pedestal_g0<float>,
+          R"(
+        Same as `calculate_pedestal_g0` but returns a 2D array of floats.
+
+        Parameters
+        ----------
+        raw_data : array_like
+            3D array of shape (frames, rows, cols) to calculate the pedestal from.
+        n_threads : int 
+            The number of threads to use for the calculation.
+        )",
+          py::arg("raw_data").noconvert(), py::arg("n_threads") = 4);
 }

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 +86,30 @@ 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_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

@@ -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_calibration.py

@@ -1,6 +1,7 @@
 import pytest 
 import numpy as np
-from aare import apply_calibration
+
+import aare
 
 def test_apply_calibration_small_data():
     # The raw data consists of 10 4x5 images
@@ -27,7 +28,7 @@ def test_apply_calibration_small_data():
 
     
 
-    data = apply_calibration(raw, pd = pedestal, cal = calibration)
+    data = aare.apply_calibration(raw, pd = pedestal, cal = calibration)
 
 
     # The formula that is applied is:
@@ -41,3 +42,94 @@ def test_apply_calibration_small_data():
     assert data[2,2,2] == 0
     assert data[0,1,1] == 0
     assert data[1,3,0] == 0
+
+
+@pytest.fixture
+def raw_data_3x2x2():
+    raw = np.zeros((3, 2, 2), dtype=np.uint16)
+    raw[0, 0, 0] = 100
+    raw[1,0, 0] =  200
+    raw[2, 0, 0] = 300
+
+    raw[0, 0, 1] = (1<<14) + 100
+    raw[1, 0, 1] = (1<<14) + 200
+    raw[2, 0, 1] = (1<<14) + 300
+
+    raw[0, 1, 0] = (1<<14) + 37
+    raw[1, 1, 0] =  38
+    raw[2, 1, 0] = (3<<14) + 39
+
+    raw[0, 1, 1] = (3<<14) + 100
+    raw[1, 1, 1] = (3<<14) + 200
+    raw[2, 1, 1] = (3<<14) + 300
+    return raw
+
+def test_calculate_pedestal(raw_data_3x2x2):
+    # Calculate the pedestal
+    pd = aare.calculate_pedestal(raw_data_3x2x2)
+    assert pd.shape == (3, 2, 2)
+    assert pd.dtype == np.float64
+    assert pd[0, 0, 0] == 200
+    assert pd[1, 0, 0] == 0
+    assert pd[2, 0, 0] == 0
+
+    assert pd[0, 0, 1] == 0
+    assert pd[1, 0, 1] == 200
+    assert pd[2, 0, 1] == 0
+
+    assert pd[0, 1, 0] == 38
+    assert pd[1, 1, 0] == 37
+    assert pd[2, 1, 0] == 39
+
+    assert pd[0, 1, 1] == 0
+    assert pd[1, 1, 1] == 0
+    assert pd[2, 1, 1] == 200
+
+def test_calculate_pedestal_float(raw_data_3x2x2):
+    #results should be the same for float
+    pd2 = aare.calculate_pedestal_float(raw_data_3x2x2)
+    assert pd2.shape == (3, 2, 2)
+    assert pd2.dtype == np.float32
+    assert pd2[0, 0, 0] == 200
+    assert pd2[1, 0, 0] == 0
+    assert pd2[2, 0, 0] == 0
+
+    assert pd2[0, 0, 1] == 0
+    assert pd2[1, 0, 1] == 200
+    assert pd2[2, 0, 1] == 0
+
+    assert pd2[0, 1, 0] == 38
+    assert pd2[1, 1, 0] == 37
+    assert pd2[2, 1, 0] == 39
+
+    assert pd2[0, 1, 1] == 0
+    assert pd2[1, 1, 1] == 0
+    assert pd2[2, 1, 1] == 200
+
+def test_calculate_pedestal_g0(raw_data_3x2x2):
+    pd = aare.calculate_pedestal_g0(raw_data_3x2x2)
+    assert pd.shape == (2, 2)
+    assert pd.dtype == np.float64
+    assert pd[0, 0] == 200
+    assert pd[1, 0] == 38
+    assert pd[0, 1] == 0
+    assert pd[1, 1] == 0
+
+def test_calculate_pedestal_g0_float(raw_data_3x2x2):
+    pd = aare.calculate_pedestal_g0_float(raw_data_3x2x2)
+    assert pd.shape == (2, 2)
+    assert pd.dtype == np.float32
+    assert pd[0, 0] == 200
+    assert pd[1, 0] == 38
+    assert pd[0, 1] == 0
+    assert pd[1, 1] == 0
+
+def test_count_switching_pixels(raw_data_3x2x2):
+    # Count the number of pixels that switched gain
+    count = aare.count_switching_pixels(raw_data_3x2x2)
+    assert count.shape == (2, 2)
+    assert count.sum() == 8
+    assert count[0, 0] == 0 
+    assert count[1, 0] == 2 
+    assert count[0, 1] == 3 
+    assert count[1, 1] == 3

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/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/NDArray.test.cpp

@@ -25,13 +25,13 @@ TEST_CASE("Construct from an NDView") {
     REQUIRE(image.data() != view.data());
 
     for (uint32_t i = 0; i < image.size(); ++i) {
-        REQUIRE(image(i) == view(i));
+        REQUIRE(image[i] == view[i]);
     }
 
     // Changing the image doesn't change the view
     image = 43;
     for (uint32_t i = 0; i < image.size(); ++i) {
-        REQUIRE(image(i) != view(i));
+        REQUIRE(image[i] != view[i]);
     }
 }
 
@@ -427,4 +427,30 @@ TEST_CASE("Construct an NDArray from an std::array") {
     for (uint32_t i = 0; i < a.size(); ++i) {
         REQUIRE(a(i) == b[i]);
     }
-}
+}
+
+
+
+TEST_CASE("Move construct from an array with Ndim + 1") {
+    NDArray<int, 3> a({{1,2,2}}, 0);
+    a(0, 0, 0) = 1;
+    a(0, 0, 1) = 2;
+    a(0, 1, 0) = 3;
+    a(0, 1, 1) = 4;
+
+
+    NDArray<int, 2> b(std::move(a));
+    REQUIRE(b.shape() == Shape<2>{2,2});
+    REQUIRE(b.size() == 4);
+    REQUIRE(b(0, 0) == 1);
+    REQUIRE(b(0, 1) == 2);
+    REQUIRE(b(1, 0) == 3);
+    REQUIRE(b(1, 1) == 4);
+
+}
+
+TEST_CASE("Move construct from an array with Ndim + 1 throws on size mismatch") {
+    NDArray<int, 3> a({{2,2,2}}, 0);
+    REQUIRE_THROWS(NDArray<int, 2>(std::move(a)));
+}
+

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) {
@@ -277,14 +294,22 @@ 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 ||
@@ -298,14 +323,10 @@ void RawMasterFile::parse_json(const std::filesystem::path &fpath) {
         }
 
     } 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/calibration.cpp

@@ -0,0 +1,44 @@
+#include "aare/calibration.hpp"
+
+namespace aare {
+
+NDArray<int, 2> count_switching_pixels(NDView<uint16_t, 3> raw_data) {
+    NDArray<int, 2> switched(
+        std::array<ssize_t, 2>{raw_data.shape(1), raw_data.shape(2)}, 0);
+    for (int frame_nr = 0; frame_nr != raw_data.shape(0); ++frame_nr) {
+        for (int row = 0; row != raw_data.shape(1); ++row) {
+            for (int col = 0; col != raw_data.shape(2); ++col) {
+                auto [value, gain] =
+                    get_value_and_gain(raw_data(frame_nr, row, col));
+                if (gain != 0) {
+                    switched(row, col) += 1;
+                }
+            }
+        }
+    }
+    return switched;
+}
+
+NDArray<int, 2> count_switching_pixels(NDView<uint16_t, 3> raw_data,
+                                       ssize_t n_threads) {
+    NDArray<int, 2> switched(
+        std::array<ssize_t, 2>{raw_data.shape(1), raw_data.shape(2)}, 0);
+    std::vector<std::future<NDArray<int, 2>>> futures;
+    futures.reserve(n_threads);
+
+    auto subviews = make_subviews(raw_data, n_threads);
+
+    for (auto view : subviews) {
+        futures.push_back(
+            std::async(static_cast<NDArray<int, 2> (*)(NDView<uint16_t, 3>)>(
+                           &count_switching_pixels),
+                       view));
+    }
+
+    for (auto &f : futures) {
+        switched += f.get();
+    }
+    return switched;
+}
+
+} // namespace aare

src/calibration.test.cpp

@@ -0,0 +1,49 @@
+/************************************************
+ * @file test-Cluster.cpp
+ * @short test case for generic Cluster, ClusterVector, and calculate_eta2
+ ***********************************************/
+
+#include "aare/calibration.hpp"
+
+// #include "catch.hpp"
+#include <array>
+#include <catch2/catch_all.hpp>
+#include <catch2/catch_test_macros.hpp>
+
+using namespace aare;
+
+TEST_CASE("Test Pedestal Generation", "[.calibration]") {
+    NDArray<uint16_t, 3> raw(std::array<ssize_t, 3>{3, 2, 2}, 0);
+
+    // gain 0
+    raw(0, 0, 0) = 100;
+    raw(1, 0, 0) = 200;
+    raw(2, 0, 0) = 300;
+
+    // gain 1
+    raw(0, 0, 1) = (1 << 14) + 100;
+    raw(1, 0, 1) = (1 << 14) + 200;
+    raw(2, 0, 1) = (1 << 14) + 300;
+
+    raw(0, 1, 0) = (1 << 14) + 37;
+    raw(1, 1, 0) = 38;
+    raw(2, 1, 0) = (3 << 14) + 39;
+
+    // gain 2
+    raw(0, 1, 1) = (3 << 14) + 100;
+    raw(1, 1, 1) = (3 << 14) + 200;
+    raw(2, 1, 1) = (3 << 14) + 300;
+
+    auto pedestal = calculate_pedestal<double>(raw.view(), 4);
+
+    REQUIRE(pedestal.size() == raw.size());
+    CHECK(pedestal(0, 0, 0) == 200);
+    CHECK(pedestal(1, 0, 0) == 0);
+    CHECK(pedestal(1, 0, 1) == 200);
+
+    auto pedestal_gain0 = calculate_pedestal_g0<double>(raw.view(), 4);
+
+    REQUIRE(pedestal_gain0.size() == 4);
+    CHECK(pedestal_gain0(0, 0) == 200);
+    CHECK(pedestal_gain0(1, 0) == 38);
+}

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)