// // Copyright 2017-2018 Ettus Research, a National Instruments Company // Copyright 2019 Ettus Research, a National Instruments Brand // // SPDX-License-Identifier: GPL-3.0-or-later // #ifndef INCLUDED_UHD_STREAM_PYTHON_HPP #define INCLUDED_UHD_STREAM_PYTHON_HPP #include #include #include static size_t wrap_recv(uhd::rx_streamer* rx_stream, py::object& np_array, uhd::rx_metadata_t& metadata, const double timeout = 0.1) { // Get a numpy array object from given python object // No sanity checking possible! PyObject* array_obj = PyArray_FROM_OF(np_array.ptr(), NPY_ARRAY_CARRAY); PyArrayObject* array_type_obj = reinterpret_cast(array_obj); // Get dimensions of the numpy array const size_t dims = PyArray_NDIM(array_type_obj); const npy_intp* shape = PyArray_SHAPE(array_type_obj); // How many bytes to jump to get to the next element of this stride // (next row) const npy_intp* strides = PyArray_STRIDES(array_type_obj); const size_t channels = rx_stream->get_num_channels(); // Check if numpy array sizes are okay if (((channels > 1) && (dims != 2)) or ((size_t)shape[0] < channels)) { // Manually decrement the ref count Py_DECREF(array_obj); // If we don't have a 2D NumPy array, assume we have a 1D array size_t input_channels = (dims != 2) ? 1 : shape[0]; throw uhd::runtime_error( str(boost::format("Number of RX channels (%d) does not match the dimensions " "of the data array (%d)") % channels % input_channels)); } // Get a pointer to the storage std::vector channel_storage; char* data = PyArray_BYTES(array_type_obj); for (size_t i = 0; i < channels; ++i) { channel_storage.push_back((void*)(data + i * strides[0])); } // Get data buffer and size of the array size_t nsamps_per_buff; if (dims > 1) { nsamps_per_buff = (size_t)shape[1]; } else { nsamps_per_buff = PyArray_SIZE(array_type_obj); } // Release the GIL only for the recv() call const size_t result = [&]() { py::gil_scoped_release release; // Call the real recv() return rx_stream->recv(channel_storage, nsamps_per_buff, metadata, timeout); }(); // Manually decrement the ref count Py_DECREF(array_obj); return result; } static size_t wrap_send(uhd::tx_streamer* tx_stream, py::object& np_array, uhd::tx_metadata_t& metadata, const double timeout = 0.1) { // Get a numpy array object from given python object // No sanity checking possible! // Note: this increases the ref count, which we'll need to manually decrease at the // end PyObject* array_obj = PyArray_FROM_OF(np_array.ptr(), NPY_ARRAY_CARRAY); PyArrayObject* array_type_obj = reinterpret_cast(array_obj); // Get dimensions of the numpy array const size_t dims = PyArray_NDIM(array_type_obj); const npy_intp* shape = PyArray_SHAPE(array_type_obj); // How many bytes to jump to get to the next element of the stride // (next row) const npy_intp* strides = PyArray_STRIDES(array_type_obj); const size_t channels = tx_stream->get_num_channels(); // Check if numpy array sizes are ok if (((channels > 1) && (dims != 2)) or ((size_t)shape[0] < channels)) { // Manually decrement the ref count Py_DECREF(array_obj); // If we don't have a 2D NumPy array, assume we have a 1D array size_t input_channels = (dims != 2) ? 1 : shape[0]; throw uhd::runtime_error( str(boost::format("Number of TX channels (%d) does not match the dimensions " "of the data array (%d)") % channels % input_channels)); } // Get a pointer to the storage std::vector channel_storage; char* data = PyArray_BYTES(array_type_obj); for (size_t i = 0; i < channels; ++i) { channel_storage.push_back((void*)(data + i * strides[0])); } // Get data buffer and size of the array size_t nsamps_per_buff = (dims > 1) ? (size_t)shape[1] : PyArray_SIZE(array_type_obj); // Release the GIL only for the send() call const size_t result = [&]() { py::gil_scoped_release release; // Call the real send() return tx_stream->send(channel_storage, nsamps_per_buff, metadata, timeout); }(); // Manually decrement the ref count Py_DECREF(array_obj); return result; } static bool wrap_recv_async_msg(uhd::tx_streamer* tx_stream, uhd::async_metadata_t& async_metadata, double timeout = 0.1) { // Release the GIL py::gil_scoped_release release; return tx_stream->recv_async_msg(async_metadata, timeout); } void export_stream(py::module& m) { using stream_args_t = uhd::stream_args_t; using rx_streamer = uhd::rx_streamer; using tx_streamer = uhd::tx_streamer; py::class_(m, "stream_args") .def(py::init()) // Properties .def_readwrite("cpu_format", &stream_args_t::cpu_format) .def_readwrite("otw_format", &stream_args_t::otw_format) .def_readwrite("args", &stream_args_t::args) .def_readwrite("channels", &stream_args_t::channels); py::class_(m, "rx_streamer", "See: uhd::rx_streamer") // Methods .def("recv", &wrap_recv, py::arg("np_array"), py::arg("metadata"), py::arg("timeout") = 0.1) .def("get_num_channels", &uhd::rx_streamer::get_num_channels) .def("get_max_num_samps", &uhd::rx_streamer::get_max_num_samps) .def("issue_stream_cmd", &uhd::rx_streamer::issue_stream_cmd); py::class_(m, "tx_streamer", "See: uhd::tx_streamer") // Methods .def("send", &wrap_send, py::arg("np_array"), py::arg("metadata"), py::arg("timeout") = 0.1) .def("get_num_channels", &tx_streamer::get_num_channels) .def("get_max_num_samps", &tx_streamer::get_max_num_samps) .def("recv_async_msg", &wrap_recv_async_msg, py::arg("async_metadata"), py::arg("timeout") = 0.1); } #endif /* INCLUDED_UHD_STREAM_PYTHON_HPP */