mirror of
https://github.com/slsdetectorgroup/aare.git
synced 2026-07-12 11:17:02 +02:00
f670ba77a2
Multi threaded filling of per pixel histograms for example for detector calibration 1. PixelHistogram - Generic variant expects already pedestal subtracted data 2. PedestalTrackingHistogram - Terrible name, useful class. Keeps it's own pedestal and does conversion and pedestal tracking in the worker threads. --------- Co-authored-by: Lars Erik Fröjd <froejdh_e@pc-jungfrau-02.psi.ch>
88 lines
3.6 KiB
C++
88 lines
3.6 KiB
C++
// SPDX-License-Identifier: MPL-2.0
|
|
|
|
#include "aare/Pedestal.hpp"
|
|
#include "np_helper.hpp"
|
|
|
|
#include <cstdint>
|
|
#include <filesystem>
|
|
#include <pybind11/pybind11.h>
|
|
#include <pybind11/stl.h>
|
|
|
|
namespace py = pybind11;
|
|
|
|
template <typename SUM_TYPE>
|
|
void define_pedestal_bindings(py::module &m, const std::string &name) {
|
|
py::class_<Pedestal<SUM_TYPE>>(m, name.c_str())
|
|
.def(py::init<int, int, int>())
|
|
.def(py::init<int, int>())
|
|
.def("mean",
|
|
[](Pedestal<SUM_TYPE> &self) {
|
|
auto mea = new NDArray<SUM_TYPE, 2>{};
|
|
*mea = self.mean();
|
|
return return_image_data(mea);
|
|
})
|
|
.def("view",
|
|
[](py::object self_py) {
|
|
auto &self = self_py.cast<Pedestal<SUM_TYPE> &>();
|
|
auto v = self.view();
|
|
std::array<py::ssize_t, 2> shape{
|
|
static_cast<py::ssize_t>(v.shape(0)),
|
|
static_cast<py::ssize_t>(v.shape(1))};
|
|
std::array<py::ssize_t, 2> byte_strides{
|
|
static_cast<py::ssize_t>(v.strides()[0]) *
|
|
static_cast<py::ssize_t>(sizeof(SUM_TYPE)),
|
|
static_cast<py::ssize_t>(v.strides()[1]) *
|
|
static_cast<py::ssize_t>(sizeof(SUM_TYPE))};
|
|
auto arr = py::array_t<SUM_TYPE>(shape, byte_strides, v.data(),
|
|
self_py);
|
|
arr.attr("setflags")(py::arg("write") = false);
|
|
return arr;
|
|
})
|
|
.def("variance",
|
|
[](Pedestal<SUM_TYPE> &self) {
|
|
auto var = new NDArray<SUM_TYPE, 2>{};
|
|
*var = self.variance();
|
|
return return_image_data(var);
|
|
})
|
|
.def("std",
|
|
[](Pedestal<SUM_TYPE> &self) {
|
|
auto std = new NDArray<SUM_TYPE, 2>{};
|
|
*std = self.std();
|
|
return return_image_data(std);
|
|
})
|
|
.def("clear", py::overload_cast<>(&Pedestal<SUM_TYPE>::clear))
|
|
.def_property_readonly("rows", &Pedestal<SUM_TYPE>::rows)
|
|
.def_property_readonly("cols", &Pedestal<SUM_TYPE>::cols)
|
|
.def_property_readonly("n_samples", &Pedestal<SUM_TYPE>::n_samples)
|
|
.def_property_readonly("sum", &Pedestal<SUM_TYPE>::get_sum)
|
|
.def_property_readonly("sum2", &Pedestal<SUM_TYPE>::get_sum2)
|
|
.def("clone",
|
|
[&](Pedestal<SUM_TYPE> &pedestal) {
|
|
return Pedestal<SUM_TYPE>(pedestal);
|
|
})
|
|
// TODO! add push for other data types
|
|
.def("push",
|
|
[](Pedestal<SUM_TYPE> &pedestal, py::array_t<uint16_t> &f) {
|
|
auto v = make_view_2d(f);
|
|
pedestal.push(v);
|
|
})
|
|
.def(
|
|
"push_with_threshold",
|
|
[](Pedestal<SUM_TYPE> &pedestal,
|
|
py::array_t<uint16_t, py::array::c_style> &f,
|
|
py::array_t<SUM_TYPE, py::array::c_style> &threshold) {
|
|
auto frame_view = make_view_2d(f);
|
|
auto threshold_view = make_view_2d(threshold);
|
|
pedestal.push_with_threshold(frame_view, threshold_view);
|
|
},
|
|
py::arg("frame").noconvert(), py::arg("threshold").noconvert())
|
|
.def(
|
|
"push_no_update",
|
|
[](Pedestal<SUM_TYPE> &pedestal,
|
|
py::array_t<uint16_t, py::array::c_style> &f) {
|
|
auto v = make_view_2d(f);
|
|
pedestal.push_no_update(v);
|
|
},
|
|
py::arg().noconvert())
|
|
.def("update_mean", &Pedestal<SUM_TYPE>::update_mean);
|
|
} |