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https://github.com/slsdetectorgroup/aare.git
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formatted main branch
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@ -6,8 +6,8 @@
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#include "aare/RawMasterFile.hpp"
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#include "aare/RawSubFile.hpp"
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#include "aare/defs.hpp"
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#include "aare/decode.hpp"
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#include "aare/defs.hpp"
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// #include "aare/fClusterFileV2.hpp"
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#include "np_helper.hpp"
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@ -26,95 +26,103 @@ using namespace ::aare;
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void define_ctb_raw_file_io_bindings(py::module &m) {
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m.def("adc_sar_05_decode64to16", [](py::array_t<uint8_t> input) {
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m.def("adc_sar_05_decode64to16", [](py::array_t<uint8_t> input) {
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if (input.ndim() != 2) {
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throw std::runtime_error(
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"Only 2D arrays are supported at this moment");
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}
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if(input.ndim() != 2){
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throw std::runtime_error("Only 2D arrays are supported at this moment");
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}
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// Create a 2D output array with the same shape as the input
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std::vector<ssize_t> shape{input.shape(0),
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input.shape(1) /
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static_cast<ssize_t>(bits_per_byte)};
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py::array_t<uint16_t> output(shape);
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//Create a 2D output array with the same shape as the input
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std::vector<ssize_t> shape{input.shape(0), input.shape(1)/static_cast<ssize_t>(bits_per_byte)};
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py::array_t<uint16_t> output(shape);
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// Create a view of the input and output arrays
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NDView<uint64_t, 2> input_view(
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reinterpret_cast<uint64_t *>(input.mutable_data()),
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{output.shape(0), output.shape(1)});
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NDView<uint16_t, 2> output_view(output.mutable_data(),
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{output.shape(0), output.shape(1)});
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//Create a view of the input and output arrays
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NDView<uint64_t, 2> input_view(reinterpret_cast<uint64_t*>(input.mutable_data()), {output.shape(0), output.shape(1)});
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NDView<uint16_t, 2> output_view(output.mutable_data(), {output.shape(0), output.shape(1)});
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adc_sar_05_decode64to16(input_view, output_view);
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adc_sar_05_decode64to16(input_view, output_view);
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return output;
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});
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m.def("adc_sar_04_decode64to16", [](py::array_t<uint8_t> input) {
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if(input.ndim() != 2){
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throw std::runtime_error("Only 2D arrays are supported at this moment");
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}
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//Create a 2D output array with the same shape as the input
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std::vector<ssize_t> shape{input.shape(0), input.shape(1)/static_cast<ssize_t>(bits_per_byte)};
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py::array_t<uint16_t> output(shape);
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//Create a view of the input and output arrays
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NDView<uint64_t, 2> input_view(reinterpret_cast<uint64_t*>(input.mutable_data()), {output.shape(0), output.shape(1)});
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NDView<uint16_t, 2> output_view(output.mutable_data(), {output.shape(0), output.shape(1)});
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adc_sar_04_decode64to16(input_view, output_view);
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return output;
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});
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m.def(
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"apply_custom_weights",
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[](py::array_t<uint16_t, py::array::c_style | py::array::forcecast> &input,
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py::array_t<double, py::array::c_style | py::array::forcecast>
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&weights) {
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// Create new array with same shape as the input array (uninitialized values)
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py::buffer_info buf = input.request();
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py::array_t<double> output(buf.shape);
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// Use NDViews to call into the C++ library
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auto weights_view = make_view_1d(weights);
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NDView<uint16_t, 1> input_view(input.mutable_data(), {input.size()});
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NDView<double, 1> output_view(output.mutable_data(), {output.size()});
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apply_custom_weights(input_view, output_view, weights_view);
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return output;
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});
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py::class_<CtbRawFile>(m, "CtbRawFile")
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.def(py::init<const std::filesystem::path &>())
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.def("read_frame",
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[](CtbRawFile &self) {
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size_t image_size = self.image_size_in_bytes();
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py::array image;
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std::vector<ssize_t> shape;
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shape.reserve(2);
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shape.push_back(1);
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shape.push_back(image_size);
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m.def("adc_sar_04_decode64to16", [](py::array_t<uint8_t> input) {
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if (input.ndim() != 2) {
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throw std::runtime_error(
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"Only 2D arrays are supported at this moment");
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}
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py::array_t<DetectorHeader> header(1);
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// Create a 2D output array with the same shape as the input
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std::vector<ssize_t> shape{input.shape(0),
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input.shape(1) /
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static_cast<ssize_t>(bits_per_byte)};
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py::array_t<uint16_t> output(shape);
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// always read bytes
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image = py::array_t<uint8_t>(shape);
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// Create a view of the input and output arrays
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NDView<uint64_t, 2> input_view(
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reinterpret_cast<uint64_t *>(input.mutable_data()),
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{output.shape(0), output.shape(1)});
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NDView<uint16_t, 2> output_view(output.mutable_data(),
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{output.shape(0), output.shape(1)});
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self.read_into(reinterpret_cast<std::byte *>(image.mutable_data()),
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header.mutable_data());
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adc_sar_04_decode64to16(input_view, output_view);
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return py::make_tuple(header, image);
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})
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.def("seek", &CtbRawFile::seek)
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.def("tell", &CtbRawFile::tell)
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.def("master", &CtbRawFile::master)
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return output;
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});
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.def_property_readonly("image_size_in_bytes",
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&CtbRawFile::image_size_in_bytes)
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m.def("apply_custom_weights",
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[](py::array_t<uint16_t, py::array::c_style | py::array::forcecast>
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&input,
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py::array_t<double, py::array::c_style | py::array::forcecast>
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&weights) {
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// Create new array with same shape as the input array
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// (uninitialized values)
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py::buffer_info buf = input.request();
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py::array_t<double> output(buf.shape);
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.def_property_readonly("frames_in_file", &CtbRawFile::frames_in_file);
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// Use NDViews to call into the C++ library
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auto weights_view = make_view_1d(weights);
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NDView<uint16_t, 1> input_view(input.mutable_data(),
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{input.size()});
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NDView<double, 1> output_view(output.mutable_data(),
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{output.size()});
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apply_custom_weights(input_view, output_view, weights_view);
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return output;
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});
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py::class_<CtbRawFile>(m, "CtbRawFile")
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.def(py::init<const std::filesystem::path &>())
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.def("read_frame",
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[](CtbRawFile &self) {
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size_t image_size = self.image_size_in_bytes();
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py::array image;
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std::vector<ssize_t> shape;
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shape.reserve(2);
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shape.push_back(1);
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shape.push_back(image_size);
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py::array_t<DetectorHeader> header(1);
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// always read bytes
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image = py::array_t<uint8_t>(shape);
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self.read_into(
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reinterpret_cast<std::byte *>(image.mutable_data()),
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header.mutable_data());
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return py::make_tuple(header, image);
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})
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.def("seek", &CtbRawFile::seek)
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.def("tell", &CtbRawFile::tell)
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.def("master", &CtbRawFile::master)
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.def_property_readonly("image_size_in_bytes",
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&CtbRawFile::image_size_in_bytes)
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.def_property_readonly("frames_in_file", &CtbRawFile::frames_in_file);
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}
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