Import array is moved to the Cython thread, and memory is put in a capsule, also on Linux. I don't know why, but it seemed to fix the memory leak
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@ -6,7 +6,7 @@ set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
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set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_CURRENT_SOURCE_DIR}/cmake")
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set(CYTHON_EXECUTABLE "cython3")
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include(UseCython)
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find_package(Numpy REQUIRED )
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include(FindNumpy)
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include_directories(
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${PYTHON_NUMPY_INCLUDE_DIR}
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@ -9,16 +9,18 @@
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#pragma once
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#ifndef LASP_PYARRAY_H
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#define LASP_PYARRAY_H
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#include <numpy/ndarrayobject.h>
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#include "lasp_types.h"
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#include <assert.h>
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#include <numpy/ndarrayobject.h>
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#if LASP_DOUBLE_PRECISION == 1
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#define LASP_NUMPY_FLOAT_TYPE NPY_FLOAT64
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#define LASP_NUMPY_COMPLEX_TYPE NPY_COMPLEX128
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#else
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#define LASP_NUMPY_FLOAT_TYPE NPY_FLOAT32
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#endif
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#include <malloc.h>
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#ifdef MS_WIN64
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/**
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* Function passed to Python to use for cleanup of
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* foreignly obtained data.
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@ -29,54 +31,48 @@ static inline void capsule_cleanup(PyObject *capsule) {
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free(memory);
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}
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#endif
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/**
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* Create a numpy array from a raw data pointer.
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*
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* @param data pointer to data, assumes d* for data
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* @param transfer_ownership If set to true, Numpy array will be responsible
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* for freeing the data.
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* @param transfer_ownership If set to true, the created Numpy array will be
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* responsible for freeing the data.
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*
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* @return Numpy array
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*/
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static inline PyObject *data_to_ndarray(d *data, int n_rows, int n_cols,
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static inline PyObject *data_to_ndarray(void *data, int n_rows, int n_cols,
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int typenum, bool transfer_ownership,
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bool F_contiguous) {
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/* fprintf(stderr, "Enter data_to_ndarray\n"); */
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assert(data);
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import_array();
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PyArray_Descr *descr = PyArray_DescrFromType(typenum);
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if(!descr) return NULL;
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if (!descr)
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return NULL;
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npy_intp dims[2] = {n_rows, n_cols};
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npy_intp strides[2];
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int flags = 0;
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if(F_contiguous){
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if (F_contiguous) {
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flags |= NPY_ARRAY_FARRAY;
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strides[0] = descr->elsize;
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strides[1] = descr->elsize*n_rows;
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strides[1] = descr->elsize * n_rows;
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} else {
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strides[0] = descr->elsize*n_rows;
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strides[0] = descr->elsize * n_rows;
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strides[1] = descr->elsize;
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}
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/* assert(n_rows > 0); */
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/* assert(n_cols > 0); */
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PyArrayObject *arr =
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(PyArrayObject *)PyArray_NewFromDescr(
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&PyArray_Type,
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descr, // Description
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2, // nd
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dims, // dimensions
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strides, // strides
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data, // Data pointer
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flags, // Flags
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NULL // obj
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(PyArrayObject *)PyArray_NewFromDescr(&PyArray_Type,
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descr, // Description
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2, // nd
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dims, // dimensions
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strides, // strides
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data, // Data pointer
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flags, // Flags
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NULL // obj
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);
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if (!arr) {
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@ -84,8 +80,7 @@ static inline PyObject *data_to_ndarray(d *data, int n_rows, int n_cols,
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return NULL;
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}
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if (transfer_ownership == true) {
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#ifdef MS_WIN64
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if (transfer_ownership) {
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// The default destructor of Python cannot free the data, as it is allocated
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// with malloc. Therefore, with this code, we tell Numpy/Python to use
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// the capsule_cleanup constructor. See:
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@ -94,19 +89,16 @@ static inline PyObject *data_to_ndarray(d *data, int n_rows, int n_cols,
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// Windows. We do it anyway, see if we find any problems on the way.
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PyObject *capsule = PyCapsule_New(data, LASP_CAPSULE_NAME, capsule_cleanup);
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int res = PyArray_SetBaseObject(arr, capsule);
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if(res != 0) {
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if (res != 0) {
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fprintf(stderr, "Failed to set base object of array!");
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return NULL;
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}
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#endif
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/* fprintf(stderr, "============Ownership transfer================\n"); */
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PyArray_ENABLEFLAGS(arr, NPY_OWNDATA);
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}
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/* fprintf(stderr, "Exit data_to_ndarray\n"); */
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return (PyObject *) arr;
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return (PyObject *)arr;
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}
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#undef LASP_CAPSULE_NAME
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#endif // LASP_PYARRAY_H
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//////////////////////////////////////////////////////////////////////
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@ -1,6 +1,6 @@
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// ascee_python.h
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//
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// Author: J.A. de Jong - ASCEE
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// Author: J.A. de Jong - ASCEE - Redu-Sone
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//
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// Description:
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// Some routines to generate numpy arrays from matrices and vectors.
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@ -8,16 +8,28 @@
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#pragma once
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#ifndef LASP_PYTHON_H
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#define LASP_PYTHON_H
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#ifdef __cplusplus
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#error "Cannot compile this file with C++"
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#endif
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#include "lasp_types.h"
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#include "lasp_mat.h"
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#include "lasp_pyarray.h"
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/**
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* Create a numpy array from an existing dmat.
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* @brief Create a numpy array from a buffer of floating point data
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*
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* @param mat dmat structure containing array data and metadata.
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* @param transfer_ownership If set to true, Numpy array will be responsible
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* for freeing the data.
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* @param data Pointer
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* @param n_rows Number of columns in the data
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* @param n_cols Number of rows in the data
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* @param transfer_ownership If set to true, the created Numpy array will
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* obtain ownership of the data and calls free() on destruction.
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* @param F_contiguous It set to true, the data is assumed to be column-major
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* ordered in memory (which means we can find element d[r,c] by d[n_cols*r+c],
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* if set to false. Data is assumed to be row-major ordered (which means we
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* find element d[r,c] by d[n_rows*c+r]
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*
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* @return Numpy array
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* @return
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*/
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static inline PyObject* dmat_to_ndarray(dmat* mat,bool transfer_ownership) {
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dbgassert(mat,NULLPTRDEREF);
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@ -48,6 +60,5 @@ static inline PyObject* dmat_to_ndarray(dmat* mat,bool transfer_ownership) {
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return arr;
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}
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#endif // LASP_PYTHON_H
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//////////////////////////////////////////////////////////////////////
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@ -3,7 +3,7 @@
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# PYTHON_NUMPY_FOUND
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# will be set by this script
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cmake_minimum_required(VERSION 2.6)
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cmake_minimum_required(VERSION 3.0)
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if(NOT PYTHON_EXECUTABLE)
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if(NumPy_FIND_QUIETLY)
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@ -35,7 +35,8 @@ cdef extern from "lasp_pyarray.h":
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int n_rows,int n_cols,
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int typenum,
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bool transfer_ownership,
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bool F_contiguous)
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bool F_contiguous) with gil
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ctypedef size_t us
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ctypedef vector[bool] boolvec
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@ -1,9 +1,9 @@
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#include "lasp_cpprtaudio.h"
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#include <RtAudio.h>
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#include <atomic>
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#include <thread>
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#include <cstring>
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#include <cassert>
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#include <cstring>
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#include <thread>
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#if MS_WIN64
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typedef uint8_t u_int8_t;
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@ -447,7 +447,7 @@ void threadfcn(DT9837A *td) {
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}
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inTotalCount = inxstat.currentScanCount;
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if (inxstat.currentIndex < buffer_mid_idx_in) {
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if ((us) inxstat.currentIndex < buffer_mid_idx_in) {
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topinenqueued = false;
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if (!botinenqueued) {
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/* cerr << "Copying in buffer bot" << endl; */
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@ -2,6 +2,7 @@ cimport cython
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from ..lasp_common import AvType
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from .lasp_deviceinfo cimport DeviceInfo
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from .lasp_daqconfig cimport DaqConfiguration
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from numpy cimport import_array
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from cpython.ref cimport PyObject,Py_INCREF, Py_DECREF
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import numpy as np
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@ -88,6 +89,7 @@ cdef void audioCallbackPythonThreadFunction(void* voidsd) nogil:
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sd.nFramesPerBlock))
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with gil:
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import_array()
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npy_format = cnp.NPY_FLOAT64
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callback = <object> sd.pyCallback
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# print(f'Number of input channels: {ninchannels}')
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@ -116,10 +118,7 @@ cdef void audioCallbackPythonThreadFunction(void* voidsd) nogil:
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# Numpy container
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True) # F-contiguous
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try:
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rval = callback(None,
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npy_output,
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nFramesPerBlock,
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)
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rval = callback(None, npy_output, nFramesPerBlock)
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except Exception as e:
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logging.error('exception in Cython callback for audio output: ', str(e))
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@ -131,21 +130,19 @@ cdef void audioCallbackPythonThreadFunction(void* voidsd) nogil:
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# Waiting indefinitely on the queue...
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inbuffer = <double*> sd.inQueue.dequeue()
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if inbuffer == NULL:
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logging.debug('Stopping thread...\n')
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logging.debug('Received empty buffer on input, stopping thread...\n')
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return
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npy_input = <object> data_to_ndarray(
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inbuffer,
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nFramesPerBlock,
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ninchannels,
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sd.npy_format,
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True, # Do transfer ownership
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True) # F-contiguous is True: data is Fortran-cont.
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try:
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npy_input = <object> data_to_ndarray(
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inbuffer,
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nFramesPerBlock,
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ninchannels,
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sd.npy_format,
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True, # Do transfer ownership
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True) # F-contiguous is True: data is Fortran-cont.
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rval = callback(npy_input,
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None,
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nFramesPerBlock,
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)
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rval = callback(npy_input, None, nFramesPerBlock)
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except Exception as e:
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logging.error('exception in cython callback for audio input: ', str(e))
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