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Dev/interpolation documentation (#255)
- added transform_eta_values for easier debugging more control for the user - updated Documentation
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181
python/src/bind_Interpolator.hpp
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181
python/src/bind_Interpolator.hpp
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// SPDX-License-Identifier: MPL-2.0
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#include "aare/CalculateEta.hpp"
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#include "aare/Interpolator.hpp"
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#include "aare/NDArray.hpp"
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#include "aare/NDView.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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namespace py = pybind11;
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#define REGISTER_INTERPOLATOR_ETA2(T, N, M, U) \
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register_interpolate<T, N, M, U, aare::calculate_full_eta2<T, N, M, U>>( \
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interpolator, "_full_eta2", "full eta2"); \
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register_interpolate<T, N, M, U, aare::calculate_eta2<T, N, M, U>>( \
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interpolator, "", "eta2");
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#define REGISTER_INTERPOLATOR_ETA3(T, N, M, U) \
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register_interpolate<T, N, M, U, aare::calculate_eta3<T, N, M, U>>( \
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interpolator, "_eta3", "full eta3"); \
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register_interpolate<T, N, M, U, aare::calculate_cross_eta3<T, N, M, U>>( \
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interpolator, "_cross_eta3", "cross eta3");
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template <typename Type, uint8_t CoordSizeX, uint8_t CoordSizeY,
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typename CoordType = uint16_t, auto EtaFunction>
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void register_interpolate(py::class_<aare::Interpolator> &interpolator,
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const std::string &typestr = "",
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const std::string &doc_string_etatype = "eta2x2") {
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using ClusterType = Cluster<Type, CoordSizeX, CoordSizeY, CoordType>;
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const std::string docstring = "interpolation based on " +
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doc_string_etatype +
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"\n\nReturns:\n interpolated photons";
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auto function_name = fmt::format("interpolate{}", typestr);
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interpolator.def(
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function_name.c_str(),
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[](aare::Interpolator &self,
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const ClusterVector<ClusterType> &clusters) {
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auto photons = self.interpolate<EtaFunction, ClusterType>(clusters);
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auto *ptr = new std::vector<Photon>{photons};
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return return_vector(ptr);
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},
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docstring.c_str(), py::arg("cluster_vector"));
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}
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template <typename Type>
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void register_transform_eta_values(
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py::class_<aare::Interpolator> &interpolator) {
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interpolator.def(
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"transform_eta_values",
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[](Interpolator &self, const Eta2<Type> &eta) {
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auto uniform_coord = self.transform_eta_values(eta);
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return py::make_tuple(uniform_coord.x, uniform_coord.y);
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},
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R"(eta.x eta.y transformed to uniform coordinates based on CDF ietax, ietay)",
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py::arg("Eta"));
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}
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void define_interpolation_bindings(py::module &m) {
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PYBIND11_NUMPY_DTYPE(aare::Photon, x, y, energy);
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auto interpolator =
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py::class_<aare::Interpolator>(m, "Interpolator")
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.def(py::init(
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[](py::array_t<double,
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py::array::c_style | py::array::forcecast>
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etacube,
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py::array_t<double> xbins, py::array_t<double> ybins,
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py::array_t<double> ebins) {
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return Interpolator(
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make_view_3d(etacube), make_view_1d(xbins),
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make_view_1d(ybins), make_view_1d(ebins));
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}),
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R"doc(
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Constructor
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Args:
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etacube:
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joint distribution of eta_x, eta_y and photon energy (**Note:** for the joint distribution first dimension is eta_x, second: eta_y, third: energy bins.)
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xbins:
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bin edges of etax
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ybins:
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bin edges of etay
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ebins:
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bin edges of photon energy
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)doc",
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py::arg("etacube"),
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py::arg("xbins"), py::arg("ybins"),
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py::arg("ebins"))
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.def(py::init(
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[](py::array_t<double> xbins, py::array_t<double> ybins,
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py::array_t<double> ebins) {
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return Interpolator(make_view_1d(xbins),
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make_view_1d(ybins),
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make_view_1d(ebins));
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}),
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R"(
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Constructor
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Args:
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xbins:
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bin edges of etax
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ybins:
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bin edges of etay
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ebins:
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bin edges of photon energy
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)", py::arg("xbins"),
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py::arg("ybins"), py::arg("ebins"))
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.def(
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"rosenblatttransform",
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[](Interpolator &self,
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py::array_t<double,
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py::array::c_style | py::array::forcecast>
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etacube) {
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return self.rosenblatttransform(make_view_3d(etacube));
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},
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R"(
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calculated the rosenblatttransform for the given distribution
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etacube:
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joint distribution of eta_x, eta_y and photon energy (**Note:** for the joint distribution first dimension is eta_x, second: eta_y, third: energy bins.)
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)",
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py::arg("etacube"))
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.def("get_ietax",
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[](Interpolator &self) {
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auto *ptr = new NDArray<double, 3>{};
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*ptr = self.get_ietax();
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return return_image_data(ptr);
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}, R"(conditional CDF of etax conditioned on etay, marginal CDF of etax (if rosenblatt transform applied))")
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.def("get_ietay", [](Interpolator &self) {
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auto *ptr = new NDArray<double, 3>{};
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*ptr = self.get_ietay();
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return return_image_data(ptr);
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}, R"(conditional CDF of etay conditioned on etax)");
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REGISTER_INTERPOLATOR_ETA3(int, 3, 3, uint16_t);
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REGISTER_INTERPOLATOR_ETA3(float, 3, 3, uint16_t);
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REGISTER_INTERPOLATOR_ETA3(double, 3, 3, uint16_t);
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REGISTER_INTERPOLATOR_ETA2(int, 3, 3, uint16_t);
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REGISTER_INTERPOLATOR_ETA2(float, 3, 3, uint16_t);
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REGISTER_INTERPOLATOR_ETA2(double, 3, 3, uint16_t);
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REGISTER_INTERPOLATOR_ETA2(int, 2, 2, uint16_t);
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REGISTER_INTERPOLATOR_ETA2(float, 2, 2, uint16_t);
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REGISTER_INTERPOLATOR_ETA2(double, 2, 2, uint16_t);
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register_transform_eta_values<int>(interpolator);
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register_transform_eta_values<float>(interpolator);
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register_transform_eta_values<double>(interpolator);
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// TODO! Evaluate without converting to double
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m.def(
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"hej",
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[]() {
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// auto boost_histogram = py::module_::import("boost_histogram");
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// py::object axis =
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// boost_histogram.attr("axis").attr("Regular")(10, 0.0, 10.0);
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// py::object histogram = boost_histogram.attr("Histogram")(axis);
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// return histogram;
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// return h;
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},
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R"(
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Evaluate a 1D Gaussian function for all points in x using parameters par.
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Parameters
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----------
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x : array_like
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The points at which to evaluate the Gaussian function.
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par : array_like
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The parameters of the Gaussian function. The first element is the amplitude, the second element is the mean, and the third element is the standard deviation.
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)");
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}
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