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formatted main branch

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mazzol_a
2025-06-10 16:09:06 +02:00
parent efd2338f54
commit f9751902a2
87 changed files with 1710 additions and 1639 deletions
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29
python/src/fit.hpp
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@ -9,7 +9,6 @@
namespace py = pybind11;
using namespace pybind11::literals;
void define_fit_bindings(py::module &m) {
// TODO! Evaluate without converting to double
@ -61,7 +60,8 @@ void define_fit_bindings(py::module &m) {
py::array_t<double, py::array::c_style | py::array::forcecast> par) {
auto x_view = make_view_1d(x);
auto par_view = make_view_1d(par);
auto y = new NDArray<double, 1>{aare::func::scurve(x_view, par_view)};
auto y =
new NDArray<double, 1>{aare::func::scurve(x_view, par_view)};
return return_image_data(y);
},
R"(
@ -82,7 +82,8 @@ void define_fit_bindings(py::module &m) {
py::array_t<double, py::array::c_style | py::array::forcecast> par) {
auto x_view = make_view_1d(x);
auto par_view = make_view_1d(par);
auto y = new NDArray<double, 1>{aare::func::scurve2(x_view, par_view)};
auto y =
new NDArray<double, 1>{aare::func::scurve2(x_view, par_view)};
return return_image_data(y);
},
R"(
@ -139,7 +140,6 @@ n_threads : int, optional
py::array_t<double, py::array::c_style | py::array::forcecast> y,
py::array_t<double, py::array::c_style | py::array::forcecast> y_err,
int n_threads) {
if (y.ndim() == 3) {
// Allocate memory for the output
// Need to have pointers to allow python to manage
@ -173,7 +173,6 @@ n_threads : int, optional
auto y_view_err = make_view_1d(y_err);
auto x_view = make_view_1d(x);
double chi2 = 0;
aare::fit_gaus(x_view, y_view, y_view_err, par->view(),
par_err->view(), chi2);
@ -248,11 +247,10 @@ n_threads : int, optional
aare::fit_pol1(x_view, y_view, y_view_err, par->view(),
par_err->view(), chi2->view(), n_threads);
return py::dict("par"_a = return_image_data(par),
"par_err"_a = return_image_data(par_err),
"par_err"_a = return_image_data(par_err),
"chi2"_a = return_image_data(chi2),
"Ndf"_a = y.shape(2) - 2);
} else if (y.ndim() == 1) {
auto par = new NDArray<double, 1>({2});
auto par_err = new NDArray<double, 1>({2});
@ -289,7 +287,7 @@ n_threads : int, optional
)",
py::arg("x"), py::arg("y"), py::arg("y_err"), py::arg("n_threads") = 4);
//=========
//=========
m.def(
"fit_scurve",
[](py::array_t<double, py::array::c_style | py::array::forcecast> x,
@ -333,13 +331,12 @@ n_threads : int, optional
auto chi2 = new NDArray<double, 2>({y.shape(0), y.shape(1)});
aare::fit_scurve(x_view, y_view, y_view_err, par->view(),
par_err->view(), chi2->view(), n_threads);
par_err->view(), chi2->view(), n_threads);
return py::dict("par"_a = return_image_data(par),
"par_err"_a = return_image_data(par_err),
"par_err"_a = return_image_data(par_err),
"chi2"_a = return_image_data(chi2),
"Ndf"_a = y.shape(2) - 2);
} else if (y.ndim() == 1) {
auto par = new NDArray<double, 1>({2});
auto par_err = new NDArray<double, 1>({2});
@ -351,7 +348,7 @@ n_threads : int, optional
double chi2 = 0;
aare::fit_scurve(x_view, y_view, y_view_err, par->view(),
par_err->view(), chi2);
par_err->view(), chi2);
return py::dict("par"_a = return_image_data(par),
"par_err"_a = return_image_data(par_err),
"chi2"_a = chi2, "Ndf"_a = y.size() - 2);
@ -375,7 +372,6 @@ n_threads : int, optional
The number of threads to use. Default is 4.
)",
py::arg("x"), py::arg("y"), py::arg("y_err"), py::arg("n_threads") = 4);
m.def(
"fit_scurve2",
@ -420,13 +416,12 @@ n_threads : int, optional
auto chi2 = new NDArray<double, 2>({y.shape(0), y.shape(1)});
aare::fit_scurve2(x_view, y_view, y_view_err, par->view(),
par_err->view(), chi2->view(), n_threads);
par_err->view(), chi2->view(), n_threads);
return py::dict("par"_a = return_image_data(par),
"par_err"_a = return_image_data(par_err),
"par_err"_a = return_image_data(par_err),
"chi2"_a = return_image_data(chi2),
"Ndf"_a = y.shape(2) - 2);
} else if (y.ndim() == 1) {
auto par = new NDArray<double, 1>({6});
auto par_err = new NDArray<double, 1>({6});
@ -438,7 +433,7 @@ n_threads : int, optional
double chi2 = 0;
aare::fit_scurve2(x_view, y_view, y_view_err, par->view(),
par_err->view(), chi2);
par_err->view(), chi2);
return py::dict("par"_a = return_image_data(par),
"par_err"_a = return_image_data(par_err),
"chi2"_a = chi2, "Ndf"_a = y.size() - 2);