Am 15.12.2011 um 17:17 schrieb Fabrice Silva: > How can one arbitrarily assumes that an ndarray owns its data ? > > More explicitly, I have some temporary home-made C structure that holds > a pointer to an array. I prepare (using Cython) an numpy.ndarray using > the PyArray_NewFromDescr function. I can delete my temporary C structure > without freeing the memory holding array, but I wish the numpy.ndarray > becomes the owner of the data. > > How can do I do such thing ?
There is an excellent blog entry from Travis Oliphant, that describes how to create a ndarray from existing data without copy: http://blog.enthought.com/?p=62 The created array does not actually own the data, but its base attribute points to an object, which frees the memory if the numpy array gets deallocated. I guess this is the behavior you want to achieve. Here is a cython implementation (for a uint8 array) Gregor """ see 'NumPy arrays with pre-allocated memory', http://blog.enthought.com/?p=62 """ import numpy as np from numpy cimport import_array, ndarray, npy_intp, set_array_base, PyArray_SimpleNewFromData, NPY_DOUBLE, NPY_INT, NPY_UINT8 cdef extern from "stdlib.h": void* malloc(int size) void free(void *ptr) cdef class MemoryReleaser: cdef void* memory def __cinit__(self): self.memory = NULL def __dealloc__(self): if self.memory: #release memory free(self.memory) print "memory released", hex(<long>self.memory) cdef MemoryReleaser MemoryReleaserFactory(void* ptr): cdef MemoryReleaser mr = MemoryReleaser.__new__(MemoryReleaser) mr.memory = ptr return mr cdef ndarray frompointer(void* ptr, int nbytes): import_array() #cdef int dims[1] #dims[0] = nbytes cdef npy_intp dims = <npy_intp>nbytes cdef ndarray arr = PyArray_SimpleNewFromData(1, &dims, NPY_UINT8, ptr) #TODO: check for error set_array_base(arr, MemoryReleaserFactory(ptr)) return arr def test_new_array_from_pointer(): nbytes = 16 cdef void* mem = malloc(nbytes) print "memory allocated", hex(<long>mem) return frompointer(mem, nbytes) _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
