detectors/aare
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pass Fitrange argument to fit function
Build on RHEL9 / build (push) Successful in 3m0s
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Build on RHEL8 / build (push) Successful in 3m46s
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Run tests using data on local RHEL8 / build (push) Failing after 4m36s
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mazzol_a committed 2026-10-03 17:27:55 +02:00
1 parent 6e40b9af81
commit 4c7841e630
3 files changed
+63 -25
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python/src/fit.hpp
+36 -14
View File
@@ -17,7 +17,8 @@ py::object
fit_dispatch(const aare::FitModel<Model> &model,
py::array_t<double, py::array::c_style | py::array::forcecast> x,
py::array_t<double, py::array::c_style | py::array::forcecast> y,
py::object y_err_obj, int n_threads);
py::object y_err_obj, int n_threads,
std::optional<py::array_t<size_t>> fit_range = std::nullopt);
template <typename Model> void bind_fit_model(py::module &m, const char *name) {
using FM = aare::FitModel<Model>;
@@ -117,8 +118,11 @@ template <typename Model> void bind_fit_model(py::module &m, const char *name) {
[](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,
py::object y_err_obj, int n_threads) -> py::object {
return fit_dispatch<Model>(self, x, y, y_err_obj, n_threads);
py::object y_err_obj, int n_threads,
std::optional<py::array_t<size_t>> fit_range =
std::nullopt) -> py::object {
return fit_dispatch<Model>(self, x, y, y_err_obj, n_threads,
fit_range);
},
R"doc(
Fit this model to 1D or 3D data using Minuit2.
@@ -138,9 +142,11 @@ template <typename Model> void bind_fit_model(py::module &m, const char *name) {
Per-point uncertainties. None for unweighted fit.
n_threads : int
Number of threads for 3D parallel loop.
fit_range : array_like, shape (rows, cols, 2), optional
Start and end indices of the scan points to fit for each pixel. If not provided, the full range is used.
)doc",
py::arg("x"), py::arg("y"), py::arg("y_err") = py::none(),
py::arg("n_threads") = 4);
py::arg("n_threads") = 4, py::arg("fit_range") = py::none());
if constexpr (std::is_same_v<Model, model::GaussianChargeSharing> or
std::is_same_v<Model, model::GaussianChargeSharingKb>) {
@@ -226,7 +232,8 @@ py::object
fit_dispatch(const aare::FitModel<Model> &model,
py::array_t<double, py::array::c_style | py::array::forcecast> x,
py::array_t<double, py::array::c_style | py::array::forcecast> y,
py::object y_err_obj, int n_threads) {
py::object y_err_obj, int n_threads,
std::optional<py::array_t<size_t>> fit_range) {
constexpr std::size_t npar = Model::npar;
if (y.ndim() == 3) {
@@ -237,6 +244,11 @@ fit_dispatch(const aare::FitModel<Model> &model,
auto x_view = make_view_1d(x);
auto y_view = make_view_3d(y);
const auto fit_range_view =
fit_range.has_value()
? std::optional(make_view_3d(fit_range.value()))
: std::nullopt;
if (!y_err_obj.is_none()) {
auto y_err = py::cast<
py::array_t<double, py::array::c_style | py::array::forcecast>>(
@@ -250,7 +262,8 @@ fit_dispatch(const aare::FitModel<Model> &model,
py::gil_scoped_release release; // release GIL for parallel loop
aare::fit_3d<Model>(model, x_view, y_view, y_view_err,
par_out->view(), err_out->view(),
chi2_out->view(), n_threads);
chi2_out->view(), n_threads,
fit_range_view);
}
if (model.compute_errors()) {
@@ -273,7 +286,8 @@ fit_dispatch(const aare::FitModel<Model> &model,
aare::fit_3d<Model>(model, x_view, y_view, dummy_err,
par_out->view(), err_out->view(),
chi2_out->view(), n_threads);
chi2_out->view(), n_threads,
fit_range_view);
return py::dict("par"_a = return_image_data(par_out),
"par_err"_a = return_image_data(err_out),
@@ -284,9 +298,9 @@ fit_dispatch(const aare::FitModel<Model> &model,
{
py::gil_scoped_release release; // release GIL for parallel loop
aare::fit_3d<Model>(model, x_view, y_view, dummy_err,
par_out->view(), dummy_err_out,
chi2_out->view(), n_threads);
aare::fit_3d<Model>(
model, x_view, y_view, dummy_err, par_out->view(),
dummy_err_out, chi2_out->view(), n_threads, fit_range_view);
}
return py::dict("par"_a = return_image_data(par_out),
@@ -298,6 +312,12 @@ fit_dispatch(const aare::FitModel<Model> &model,
auto x_view = make_view_1d(x);
auto y_view = make_view_1d(y);
if (fit_range.has_value()) {
throw std::runtime_error(
"fit_range is only supported for 3D data. Directly pass the "
"clipped data to fit() for 1D data.");
}
if (!y_err_obj.is_none()) {
auto y_err = py::cast<
py::array_t<double, py::array::c_style | py::array::forcecast>>(
@@ -328,13 +348,15 @@ py::object dispatch_any_model(
py::object model_obj,
py::array_t<double, py::array::c_style | py::array::forcecast> x,
py::array_t<double, py::array::c_style | py::array::forcecast> y,
py::object y_err_obj, int n_threads) {
py::object y_err_obj, int n_threads,
std::optional<py::array_t<size_t>> fit_range = std::nullopt) {
using namespace aare::model;
#define AARE_DISPATCH_MODEL(Model) \
if (py::isinstance<aare::FitModel<Model>>(model_obj)) { \
const auto &mdl = model_obj.cast<const aare::FitModel<Model> &>(); \
return fit_dispatch<Model>(mdl, x, y, y_err_obj, n_threads); \
return fit_dispatch<Model>(mdl, x, y, y_err_obj, n_threads, \
fit_range); \
}
AARE_DISPATCH_MODEL(Pol1)
@@ -423,7 +445,7 @@ void define_fit_bindings(py::module &m) {
R"(
Fit a model to 1D or 3D data with the minimizer selected by the
model's ``minimizer`` property.
Parameters
----------
model : object
@@ -439,7 +461,7 @@ void define_fit_bindings(py::module &m) {
Per-point uncertainties. Same shape as y. None → unweighted fit.
n_threads : int
Number of threads for the 3D parallel loop.
Returns
-------
dict