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Merge branch 'main' into dev/jungfraucalibration

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This commit is contained in:
mazzol_a
2026-07-17 11:55:36 +02:00
parent 8681a50283 533ecb8a4a
commit 9f08b4538f
18 changed files with 483 additions and 494 deletions
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python/aare/transform.py
+9
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@@ -17,6 +17,15 @@ class AdcSar05060708Transform64to16:
return _aare.adc_sar_05_06_07_08decode64to16(data)
class Moench05Transform:
"""
Transforms Moench05 chip data from a buffer of bytes (uint8_t)
to a numpy array of uint16. Assumes data taken with analog samples and assumes adc 1, 9, 13 are enabled.
(e.g. for 10g mode adc 0,1,2,3 and 8,9,10,11 and 12,13,14,15 are enabled but only adc 1,9,13 contain relevant data)
.. note::
A moench05 chip has 160 rows and 50 cols per adc and has dynamic range 16 bit. Each adc sample is encoded in 16 bits.
The transformation thus requires 160*50*16/16 = 8000 analog samples per adc.
"""
#Could be moved to C++ without changing the interface
def __init__(self):
self.pixel_map = _aare.GenerateMoench05PixelMap()
python/src/bind_Defs.hpp
+5
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@@ -22,4 +22,9 @@ void define_defs_bindings(py::module &m) {
moench04.attr("nPixelsPerSuperColumn") = Moench04::nPixelsPerSuperColumn;
moench04.attr("superColumnWidth") = Moench04::superColumnWidth;
moench04.attr("adcNumbers") = Moench04::adcNumbers;
auto moench05 = py::class_<Moench05>(m, "Moench05");
moench05.attr("nRows") = Moench05::nRows;
moench05.attr("nCols") = Moench05::nCols;
moench05.attr("adcNumbers") = Moench05::adcNumbers;
}
python/src/fit.hpp
+24 -34
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@@ -5,7 +5,6 @@
#include <pybind11/stl.h>
#include <pybind11/stl_bind.h>
#include "aare/Chi2.hpp"
#include "aare/Fit.hpp"
#include "aare/FitModel.hpp"
#include "aare/Models.hpp"
@@ -13,7 +12,7 @@
namespace py = pybind11;
using namespace pybind11::literals;
template <typename Model, typename FCN>
template <typename Model>
py::object
fit_dispatch(const aare::FitModel<Model> &model,
py::array_t<double, py::array::c_style | py::array::forcecast> x,
@@ -22,7 +21,6 @@ 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>;
using FCN = aare::func::Chi2Model1DGrad<Model>;
py::class_<FM>(m, name)
.def(py::init<unsigned int, unsigned int, double, bool>(),
py::arg("strategy") = 0, py::arg("max_calls") = 100,
@@ -85,8 +83,7 @@ template <typename Model> void bind_fit_model(py::module &m, const char *name) {
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, FCN>(self, x, y, y_err_obj,
n_threads);
return fit_dispatch<Model>(self, x, y, y_err_obj, n_threads);
},
R"doc(
Fit this model to 1D or 3D data using Minuit2.
@@ -145,7 +142,7 @@ py::dict pack_1d_result_dict(const aare::NDArray<double, 1> &result,
}
// Helper: typed dispatch for one Model, handles 1D/3D + y_err logic
template <typename Model, typename FCN>
template <typename Model>
py::object
fit_dispatch(const aare::FitModel<Model> &model,
py::array_t<double, py::array::c_style | py::array::forcecast> x,
@@ -175,9 +172,9 @@ fit_dispatch(const aare::FitModel<Model> &model,
new NDArray<double, 3>({y.shape(0), y.shape(1), npar}, 0.0);
auto y_view_err = make_view_3d(y_err);
aare::fit_3d<Model, FCN>(model, x_view, y_view, y_view_err,
par_out->view(), err_out->view(),
chi2_out->view(), n_threads);
aare::fit_3d<Model>(model, x_view, y_view, y_view_err,
par_out->view(), err_out->view(),
chi2_out->view(), n_threads);
if (model.compute_errors()) {
return py::dict("par"_a = return_image_data(par_out),
@@ -193,9 +190,9 @@ fit_dispatch(const aare::FitModel<Model> &model,
NDView<double, 3> dummy_err{};
NDView<double, 3> dummy_err_out{};
aare::fit_3d<Model, FCN>(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);
return py::dict("par"_a = return_image_data(par_out),
"chi2"_a = return_image_data(chi2_out));
@@ -217,10 +214,9 @@ fit_dispatch(const aare::FitModel<Model> &model,
}
auto y_view_err = make_view_1d(y_err);
result =
aare::fit_pixel<Model, FCN>(model, x_view, y_view, y_view_err);
result = aare::fit_pixel<Model>(model, x_view, y_view, y_view_err);
} else {
result = aare::fit_pixel<Model, FCN>(model, x_view, y_view);
result = aare::fit_pixel<Model>(model, x_view, y_view);
}
return pack_1d_result_dict<Model>(result, model.compute_errors());
@@ -696,29 +692,26 @@ void define_fit_bindings(py::module &m) {
py::array_t<double, py::array::c_style | py::array::forcecast> y,
py::object y_err_obj, int n_threads) -> py::object {
using namespace aare::model;
using namespace aare::func;
// ── Polynomial of degree 1 ───────
if (py::isinstance<aare::FitModel<Pol1>>(model_obj)) {
const auto &mdl =
model_obj.cast<const aare::FitModel<Pol1> &>();
return fit_dispatch<Pol1, Chi2Pol1>(mdl, x, y, y_err_obj,
n_threads);
return fit_dispatch<Pol1>(mdl, x, y, y_err_obj, n_threads);
}
// ── Polynomial of degree 2 ───────
if (py::isinstance<aare::FitModel<Pol2>>(model_obj)) {
const auto &mdl =
model_obj.cast<const aare::FitModel<Pol2> &>();
return fit_dispatch<Pol2, Chi2Pol2>(mdl, x, y, y_err_obj,
n_threads);
return fit_dispatch<Pol2>(mdl, x, y, y_err_obj, n_threads);
}
// ── Gaussian ───────
if (py::isinstance<aare::FitModel<Gaussian>>(model_obj)) {
const auto &mdl =
model_obj.cast<const aare::FitModel<Gaussian> &>();
return fit_dispatch<Gaussian, Chi2Gaussian>(
mdl, x, y, y_err_obj, n_threads);
return fit_dispatch<Gaussian>(mdl, x, y, y_err_obj, n_threads);
}
// ── GaussianErfcPlateau ───────
@@ -727,9 +720,8 @@ void define_fit_bindings(py::module &m) {
const auto &mdl =
model_obj
.cast<const aare::FitModel<GaussianErfcPlateau> &>();
return fit_dispatch<GaussianErfcPlateau,
Chi2GaussianErfcPlateau>(
mdl, x, y, y_err_obj, n_threads);
return fit_dispatch<GaussianErfcPlateau>(mdl, x, y, y_err_obj,
n_threads);
}
// ── GaussianChargeSharing ───────
@@ -738,9 +730,8 @@ void define_fit_bindings(py::module &m) {
const auto &mdl =
model_obj
.cast<const aare::FitModel<GaussianChargeSharing> &>();
return fit_dispatch<GaussianChargeSharing,
Chi2GaussianChargeSharing>(
mdl, x, y, y_err_obj, n_threads);
return fit_dispatch<GaussianChargeSharing>(mdl, x, y, y_err_obj,
n_threads);
}
// ── GaussianChargeSharingKb ───────
@@ -748,8 +739,7 @@ void define_fit_bindings(py::module &m) {
model_obj)) {
const auto &mdl = model_obj.cast<
const aare::FitModel<GaussianChargeSharingKb> &>();
return fit_dispatch<GaussianChargeSharingKb,
Chi2GaussianChargeSharingKb>(
return fit_dispatch<GaussianChargeSharingKb>(
mdl, x, y, y_err_obj, n_threads);
}
@@ -757,16 +747,16 @@ void define_fit_bindings(py::module &m) {
if (py::isinstance<aare::FitModel<RisingScurve>>(model_obj)) {
const auto &mdl =
model_obj.cast<const aare::FitModel<RisingScurve> &>();
return fit_dispatch<RisingScurve, Chi2RisingScurve>(
mdl, x, y, y_err_obj, n_threads);
return fit_dispatch<RisingScurve>(mdl, x, y, y_err_obj,
n_threads);
}
// ── Falling Scurve ───────
if (py::isinstance<aare::FitModel<FallingScurve>>(model_obj)) {
const auto &mdl =
model_obj.cast<const aare::FitModel<FallingScurve> &>();
return fit_dispatch<FallingScurve, Chi2FallingScurve>(
mdl, x, y, y_err_obj, n_threads);
return fit_dispatch<FallingScurve>(mdl, x, y, y_err_obj,
n_threads);
}
throw std::runtime_error(
python/tests/GaussainErfcPlateau.ipynb
+9 -19
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python/tests/GaussianChargeSharingKb.ipynb
+1 -1
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@@ -396,7 +396,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.5"
"version": "3.11.15"
}
},
"nbformat": 4,
python/tests/test_Minuit2_gauss.ipynb
+17 -67
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python/tests/test_Minuit2_scurve_3d.ipynb
+23 -24
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