Merge branch 'main' into dev/reduce

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
@@ -437,6 +438,7 @@ 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

RELEASE.md

@@ -1,7 +1,22 @@
 # Release notes
 
 
-### head
+### 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 +30,7 @@ Bugfixes:
 - Removed unused file: ClusterFile.cpp 
 
 
-### 2025.05.22
+### 2025.5.22
 
 Features:
 

VERSION

@@ -1 +1 @@
-2025.5.22
+2025.8.22

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/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/ClusterFinder.hpp

@@ -145,8 +145,8 @@ class ClusterFinder {
                                         m_pedestal.mean(iy + ir, ix + ic));
                                 cluster.data[i] =
                                     tmp; // Watch for out of bounds access                                
-                                i++;
                             }
+                            i++;
                         }
                     }
 

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,
+          size_(num_elements(shape_)),
-                                std::multiplies<>())),
           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;
@@ -73,10 +100,21 @@ class NDView : public ArrayExpr<NDView<T, Ndim>, Ndim> {
     }
 
     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 +123,19 @@ 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 +205,22 @@ 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/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);
+            threshold_ = 5 * noiseMap[i];
-        binary_(i) = (original_(i) > threshold_);
+        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>
@@ -61,26 +64,146 @@ void apply_calibration_impl(NDView<T, 3> res, NDView<uint16_t, 3> raw_data,
     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,
+        futures.push_back(std::async(
-                                     lim.first, lim.second));
+            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/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, dtype=np.int32): 
-def ClusterCollector(clusterfindermt, cluster_size = (3,3), dtype=np.int32): 
     """ 
     Factory function to create a ClusterCollector object. Provides a cleaner syntax for 
     the templated ClusterCollector in C++.
     """
     
-    cls = _get_class("ClusterCollector", cluster_size, dtype)
+    cls = _get_class("ClusterCollector", clusterfindermt.cluster_size, dtype)
     return cls(clusterfindermt)
 
 def ClusterFileSink(clusterfindermt, cluster_file, dtype=np.int32): 

python/aare/__init__.py

@@ -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_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 data_span = make_view_3d(data); // data is always 3D
-    auto ped = make_view_3d(pedestal);
-    auto cal = make_view_3d(calibration);
-
     /* 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);
-
+    if (data.ndim() == 3 && pedestal.ndim() == 3 && calibration.ndim() == 3) {
-    aare::apply_calibration<DataType>(res, data_span, ped, cal, n_threads);
+        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/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/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]);
     }
 }
 
@@ -428,3 +428,29 @@ TEST_CASE("Construct an NDArray from an std::array") {
         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/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);
+}

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
 
@@ -26,8 +27,8 @@ def get_version():
 
     # Check at least one argument is passed
     if len(sys.argv) < 2:
-        return "0.0.0"
+        version =   datetime.today().strftime('%Y.%-m.%-d')
-
+    else: 
         version = sys.argv[1]
 
     try: