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Merge branch 'main' into developer
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@ -9,6 +9,7 @@
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namespace py = pybind11;
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using namespace pybind11::literals;
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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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@ -54,6 +55,7 @@ void define_fit_bindings(py::module &m) {
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)",
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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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@ -76,6 +78,7 @@ void define_fit_bindings(py::module &m) {
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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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@ -95,6 +98,7 @@ n_threads : int, optional
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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> y_err,
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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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@ -142,6 +146,7 @@ n_threads : int, optional
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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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@ -189,6 +194,7 @@ n_threads : int, optional
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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>({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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@ -221,6 +227,7 @@ n_threads : int, optional
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return py::dict("par"_a = return_image_data(par),
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"par_err"_a = return_image_data(par_err),
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"chi2"_a = chi2, "Ndf"_a = y.size() - 2);
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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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