mirror of
https://github.com/slsdetectorgroup/aare.git
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195 lines
8.4 KiB
C++
195 lines
8.4 KiB
C++
#include "aare/ClusterCollector.hpp"
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#include "aare/ClusterFileSink.hpp"
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#include "aare/ClusterFinder.hpp"
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#include "aare/ClusterFinderMT.hpp"
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#include "aare/ClusterVector.hpp"
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#include "aare/NDView.hpp"
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#include "aare/Pedestal.hpp"
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#include "np_helper.hpp"
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#include <cstdint>
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#include <filesystem>
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#include <pybind11/pybind11.h>
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#include <pybind11/stl.h>
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#include <pybind11/stl_bind.h>
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namespace py = pybind11;
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using pd_type = double;
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template <typename T>
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void define_cluster_vector(py::module &m, const std::string &typestr) {
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auto class_name = fmt::format("ClusterVector_{}", typestr);
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py::class_<ClusterVector<T>>(m, class_name.c_str(), py::buffer_protocol())
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.def(py::init<int, int>())
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.def_property_readonly("size", &ClusterVector<T>::size)
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.def("item_size", &ClusterVector<T>::item_size)
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.def_property_readonly("fmt",
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[typestr](ClusterVector<T> &self) {
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return fmt::format(
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self.fmt_base(), self.cluster_size_x(),
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self.cluster_size_y(), typestr);
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})
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.def("sum",
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[](ClusterVector<T> &self) {
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auto *vec = new std::vector<T>(self.sum());
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return return_vector(vec);
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})
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.def_property_readonly("capacity", &ClusterVector<T>::capacity)
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.def_property("frame_number", &ClusterVector<T>::frame_number,
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&ClusterVector<T>::set_frame_number)
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.def_buffer([typestr](ClusterVector<T> &self) -> py::buffer_info {
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return py::buffer_info(
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self.data(), /* Pointer to buffer */
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self.item_size(), /* Size of one scalar */
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fmt::format(self.fmt_base(), self.cluster_size_x(),
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self.cluster_size_y(),
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typestr), /* Format descriptor */
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1, /* Number of dimensions */
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{self.size()}, /* Buffer dimensions */
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{self.item_size()} /* Strides (in bytes) for each index */
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);
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});
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}
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void define_cluster_finder_mt_bindings(py::module &m) {
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py::class_<ClusterFinderMT<uint16_t, pd_type>>(m, "ClusterFinderMT")
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.def(py::init<Shape<2>, Shape<2>, pd_type, size_t, size_t>(),
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py::arg("image_size"), py::arg("cluster_size"),
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py::arg("n_sigma") = 5.0, py::arg("capacity") = 2048,
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py::arg("n_threads") = 3)
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.def("push_pedestal_frame",
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[](ClusterFinderMT<uint16_t, pd_type> &self,
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py::array_t<uint16_t> frame) {
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auto view = make_view_2d(frame);
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self.push_pedestal_frame(view);
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})
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.def(
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"find_clusters",
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[](ClusterFinderMT<uint16_t, pd_type> &self,
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py::array_t<uint16_t> frame, uint64_t frame_number) {
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auto view = make_view_2d(frame);
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self.find_clusters(view, frame_number);
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return;
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},
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py::arg(), py::arg("frame_number") = 0)
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.def("sync", &ClusterFinderMT<uint16_t, pd_type>::sync)
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.def("stop", &ClusterFinderMT<uint16_t, pd_type>::stop)
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.def("start", &ClusterFinderMT<uint16_t, pd_type>::start)
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.def_property_readonly("pedestal",
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[](ClusterFinderMT<uint16_t, pd_type> &self) {
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auto pd = new NDArray<pd_type, 2>{};
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*pd = self.pedestal();
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return return_image_data(pd);
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})
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.def_property_readonly("noise",
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[](ClusterFinderMT<uint16_t, pd_type> &self) {
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auto arr = new NDArray<pd_type, 2>{};
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*arr = self.noise();
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return return_image_data(arr);
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});
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}
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void define_cluster_collector_bindings(py::module &m) {
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py::class_<ClusterCollector>(m, "ClusterCollector")
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.def(py::init<ClusterFinderMT<uint16_t, double, int32_t> *>())
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.def("stop", &ClusterCollector::stop)
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.def(
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"steal_clusters",
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[](ClusterCollector &self) {
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auto v =
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new std::vector<ClusterVector<int>>(self.steal_clusters());
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return v;
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},
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py::return_value_policy::take_ownership);
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}
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void define_cluster_file_sink_bindings(py::module &m) {
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py::class_<ClusterFileSink>(m, "ClusterFileSink")
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.def(py::init<ClusterFinderMT<uint16_t, double, int32_t> *,
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const std::filesystem::path &>())
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.def("stop", &ClusterFileSink::stop);
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}
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void define_cluster_finder_bindings(py::module &m) {
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py::class_<ClusterFinder<uint16_t, pd_type>>(m, "ClusterFinder")
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.def(py::init<Shape<2>, Shape<2>, pd_type, size_t>(),
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py::arg("image_size"), py::arg("cluster_size"),
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py::arg("n_sigma") = 5.0, py::arg("capacity") = 1'000'000)
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.def("push_pedestal_frame",
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[](ClusterFinder<uint16_t, pd_type> &self,
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py::array_t<uint16_t> frame) {
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auto view = make_view_2d(frame);
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self.push_pedestal_frame(view);
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})
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.def_property_readonly("pedestal",
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[](ClusterFinder<uint16_t, pd_type> &self) {
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auto pd = new NDArray<pd_type, 2>{};
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*pd = self.pedestal();
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return return_image_data(pd);
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})
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.def_property_readonly("noise",
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[](ClusterFinder<uint16_t, pd_type> &self) {
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auto arr = new NDArray<pd_type, 2>{};
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*arr = self.noise();
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return return_image_data(arr);
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})
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.def(
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"steal_clusters",
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[](ClusterFinder<uint16_t, pd_type> &self,
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bool realloc_same_capacity) {
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auto v = new ClusterVector<int>(
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self.steal_clusters(realloc_same_capacity));
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return v;
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},
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py::arg("realloc_same_capacity") = false)
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.def(
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"find_clusters",
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[](ClusterFinder<uint16_t, pd_type> &self,
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py::array_t<uint16_t> frame, uint64_t frame_number) {
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auto view = make_view_2d(frame);
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self.find_clusters(view);
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return;
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},
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py::arg(), py::arg("frame_number") = 0);
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m.def("hitmap",
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[](std::array<size_t, 2> image_size, ClusterVector<int32_t> &cv) {
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py::array_t<int32_t> hitmap(image_size);
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auto r = hitmap.mutable_unchecked<2>();
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// Initialize hitmap to 0
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for (py::ssize_t i = 0; i < r.shape(0); i++)
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for (py::ssize_t j = 0; j < r.shape(1); j++)
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r(i, j) = 0;
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size_t stride = cv.item_size();
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auto ptr = cv.data();
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for (size_t i = 0; i < cv.size(); i++) {
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auto x = *reinterpret_cast<int16_t *>(ptr);
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auto y = *reinterpret_cast<int16_t *>(ptr + sizeof(int16_t));
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r(y, x) += 1;
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ptr += stride;
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}
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return hitmap;
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});
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define_cluster_vector<int>(m, "i");
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define_cluster_vector<double>(m, "d");
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define_cluster_vector<float>(m, "f");
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py::class_<DynamicCluster>(m, "DynamicCluster", py::buffer_protocol())
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.def(py::init<int, int, Dtype>())
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.def("size", &DynamicCluster::size)
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.def("begin", &DynamicCluster::begin)
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.def("end", &DynamicCluster::end)
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.def_readwrite("x", &DynamicCluster::x)
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.def_readwrite("y", &DynamicCluster::y)
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.def_buffer([](DynamicCluster &c) -> py::buffer_info {
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return py::buffer_info(c.data(), c.dt.bytes(), c.dt.format_descr(),
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1, {c.size()}, {c.dt.bytes()});
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})
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.def("__repr__", [](const DynamicCluster &a) {
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return "<DynamicCluster: x: " + std::to_string(a.x) +
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", y: " + std::to_string(a.y) + ">";
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});
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} |