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take into account elastic scattering in parameter estimation
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This commit is contained in:
mazzol_a
2026-07-27 10:50:23 +02:00
parent d5dfb6aa76
commit ce29d188b1
2 changed files with 77 additions and 16 deletions
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python/src/fit.hpp
+43 -4
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@@ -21,10 +21,10 @@ fit_dispatch(const aare::FitModel<Model> &model,
template <typename Model> void bind_fit_model(py::module &m, const char *name) {
using FM = aare::FitModel<Model>;
py::class_<FM>(m, name)
.def(py::init<unsigned int, unsigned int, double, bool>(),
py::arg("strategy") = 0, py::arg("max_calls") = 100,
py::arg("tolerance") = 0.5, py::arg("compute_errors") = false)
auto cls = py::class_<FM>(m, name);
cls.def(py::init<unsigned int, unsigned int, double, bool>(),
py::arg("strategy") = 0, py::arg("max_calls") = 100,
py::arg("tolerance") = 0.5, py::arg("compute_errors") = false)
.def("SetParLimits",
py::overload_cast<unsigned int, double, double>(&FM::SetParLimits),
py::arg("idx"), py::arg("lo"), py::arg("hi"))
@@ -101,6 +101,45 @@ template <typename Model> void bind_fit_model(py::module &m, const char *name) {
)doc",
py::arg("x"), py::arg("y"), py::arg("y_err") = py::none(),
py::arg("n_threads") = 4);
if constexpr (std::is_same_v<Model, model::GaussianChargeSharing> or
std::is_same_v<Model, model::GaussianChargeSharingKb>) {
cls.def(
"estimate_par",
[](const FM & /*self*/,
py::array_t<double, py::array::c_style | py::array::forcecast> x,
py::array_t<double, py::array::c_style | py::array::forcecast> y,
const bool elastic_scattering) {
auto x_view = make_view_1d(x);
auto y_view = make_view_1d(y);
return Model::estimate_par(x_view, y_view, elastic_scattering);
},
py::arg("x"), py::arg("y"), py::arg("elastic_scattering") = true,
R"doc(
Estimate starting parameters from 1D data.
Parameters
----------
elastic_scattering : bool
If true, take into account elastic scattering
effects.
)doc");
} else {
cls.def(
"estimate_par",
[](const FM & /*self*/,
py::array_t<double, py::array::c_style | py::array::forcecast> x,
py::array_t<double, py::array::c_style | py::array::forcecast>
y) {
auto x_view = make_view_1d(x);
auto y_view = make_view_1d(y);
return Model::estimate_par(x_view, y_view);
},
py::arg("x"), py::arg("y"), R"doc(
Estimate starting parameters from 1D data.
)doc");
}
}
template <typename Model>