mirror of
https://github.com/slsdetectorgroup/aare.git
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Added fitting, fixed roi etc (#129)
Co-authored-by: Patrick <patrick.sieberer@psi.ch> Co-authored-by: JulianHeymes <julian.heymes@psi.ch>
This commit is contained in:
Erik Fröjdh
223
python/src/fit.hpp
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223
python/src/fit.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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#include "aare/Fit.hpp"
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namespace py = pybind11;
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void define_fit_bindings(py::module &m) {
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// TODO! Evaluate without converting to double
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m.def(
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"gaus",
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[](py::array_t<double, py::array::c_style | py::array::forcecast> x,
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py::array_t<double, py::array::c_style | py::array::forcecast> par) {
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auto x_view = make_view_1d(x);
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auto par_view = make_view_1d(par);
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auto y = new NDArray<double, 1>{aare::func::gaus(x_view, par_view)};
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return return_image_data(y);
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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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)", py::arg("x"), py::arg("par"));
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m.def(
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"pol1",
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[](py::array_t<double, py::array::c_style | py::array::forcecast> x,
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py::array_t<double, py::array::c_style | py::array::forcecast> par) {
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auto x_view = make_view_1d(x);
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auto par_view = make_view_1d(par);
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auto y = new NDArray<double, 1>{aare::func::pol1(x_view, par_view)};
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return return_image_data(y);
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},
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R"(
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Evaluate a 1D polynomial function for all points in x using parameters par. (p0+p1*x)
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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 polynomial function.
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par : array_like
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The parameters of the polynomial function. The first element is the intercept, and the second element is the slope.
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)", py::arg("x"), py::arg("par"));
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m.def(
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"fit_gaus",
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[](py::array_t<double, py::array::c_style | py::array::forcecast> x,
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py::array_t<double, py::array::c_style | py::array::forcecast> y,
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int n_threads) {
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if (y.ndim() == 3) {
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auto par = new NDArray<double, 3>{};
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auto y_view = make_view_3d(y);
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auto x_view = make_view_1d(x);
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*par = aare::fit_gaus(x_view, y_view, n_threads);
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return return_image_data(par);
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} else if (y.ndim() == 1) {
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auto par = new NDArray<double, 1>{};
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auto y_view = make_view_1d(y);
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auto x_view = make_view_1d(x);
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*par = aare::fit_gaus(x_view, y_view);
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return return_image_data(par);
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} else {
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throw std::runtime_error("Data must be 1D or 3D");
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}
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},
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R"(
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Fit a 1D Gaussian to data.
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Parameters
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----------
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x : array_like
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The x values.
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y : array_like
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The y values.
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n_threads : int, optional
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The number of threads to use. Default is 4.
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)",
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py::arg("x"), py::arg("y"), py::arg("n_threads") = 4);
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m.def(
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"fit_gaus",
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[](py::array_t<double, py::array::c_style | py::array::forcecast> x,
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py::array_t<double, py::array::c_style | py::array::forcecast> y,
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py::array_t<double, py::array::c_style | py::array::forcecast>
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y_err, int n_threads) {
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if (y.ndim() == 3) {
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// Allocate memory for the output
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// Need to have pointers to allow python to manage
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// the memory
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auto par = new NDArray<double, 3>({y.shape(0), y.shape(1), 3});
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auto par_err =
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new NDArray<double, 3>({y.shape(0), y.shape(1), 3});
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auto y_view = make_view_3d(y);
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auto y_view_err = make_view_3d(y_err);
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auto x_view = make_view_1d(x);
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aare::fit_gaus(x_view, y_view, y_view_err, par->view(),
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par_err->view(), n_threads);
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// return return_image_data(par);
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return py::make_tuple(return_image_data(par),
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return_image_data(par_err));
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} else if (y.ndim() == 1) {
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// Allocate memory for the output
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// Need to have pointers to allow python to manage
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// the memory
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auto par = new NDArray<double, 1>({3});
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auto par_err = new NDArray<double, 1>({3});
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// Decode the numpy arrays
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auto y_view = make_view_1d(y);
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auto y_view_err = make_view_1d(y_err);
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auto x_view = make_view_1d(x);
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aare::fit_gaus(x_view, y_view, y_view_err, par->view(),
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par_err->view());
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return py::make_tuple(return_image_data(par),
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return_image_data(par_err));
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} else {
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throw std::runtime_error("Data must be 1D or 3D");
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}
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},
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R"(
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Fit a 1D Gaussian to data with error estimates.
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Parameters
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----------
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x : array_like
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The x values.
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y : array_like
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The y values.
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y_err : array_like
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The error in the y values.
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n_threads : int, optional
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The number of threads to use. Default is 4.
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)",
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py::arg("x"), py::arg("y"), py::arg("y_err"), py::arg("n_threads") = 4);
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m.def(
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"fit_pol1",
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[](py::array_t<double, py::array::c_style | py::array::forcecast> x,
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py::array_t<double, py::array::c_style | py::array::forcecast> y,
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int n_threads) {
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if (y.ndim() == 3) {
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auto par = new NDArray<double, 3>{};
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auto x_view = make_view_1d(x);
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auto y_view = make_view_3d(y);
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*par = aare::fit_pol1(x_view, y_view, n_threads);
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return return_image_data(par);
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} else if (y.ndim() == 1) {
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auto par = new NDArray<double, 1>{};
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auto x_view = make_view_1d(x);
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auto y_view = make_view_1d(y);
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*par = aare::fit_pol1(x_view, y_view);
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return return_image_data(par);
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} else {
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throw std::runtime_error("Data must be 1D or 3D");
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}
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},
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py::arg("x"), py::arg("y"), py::arg("n_threads") = 4);
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m.def(
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"fit_pol1",
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[](py::array_t<double, py::array::c_style | py::array::forcecast> x,
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py::array_t<double, py::array::c_style | py::array::forcecast> y,
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py::array_t<double, py::array::c_style | py::array::forcecast>
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y_err, int n_threads) {
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if (y.ndim() == 3) {
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auto par =
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new NDArray<double, 3>({y.shape(0), y.shape(1), 2});
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auto par_err =
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new NDArray<double, 3>({y.shape(0), y.shape(1), 2});
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auto y_view = make_view_3d(y);
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auto y_view_err = make_view_3d(y_err);
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auto x_view = make_view_1d(x);
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aare::fit_pol1(x_view, y_view,y_view_err, par->view(),
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par_err->view(), n_threads);
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return py::make_tuple(return_image_data(par),
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return_image_data(par_err));
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} else if (y.ndim() == 1) {
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auto par = new NDArray<double, 1>({2});
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auto par_err = new NDArray<double, 1>({2});
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auto y_view = make_view_1d(y);
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auto y_view_err = make_view_1d(y_err);
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auto x_view = make_view_1d(x);
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aare::fit_pol1(x_view, y_view, y_view_err, par->view(),
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par_err->view());
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return py::make_tuple(return_image_data(par),
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return_image_data(par_err));
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} else {
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throw std::runtime_error("Data must be 1D or 3D");
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}
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},
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R"(
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Fit a 1D polynomial to data with error estimates.
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Parameters
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----------
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x : array_like
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The x values.
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y : array_like
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The y values.
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y_err : array_like
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The error in the y values.
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n_threads : int, optional
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The number of threads to use. Default is 4.
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)",
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py::arg("x"), py::arg("y"), py::arg("y_err"), py::arg("n_threads") = 4);
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}
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