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Am 08.03.2012 um 20:39 schrieb Pauli Virtanen:
> 08.03.2012 17:37, Christoph Gohle kirjoitti:
>> thanks for testing. I have now tried on different platforms. I get
>> all kinds of crashes on os x (now with numpy 1.6.1) and windows
>> with numpy 1.6.0. On Ubuntu with numpy 1.3.0 I get a hughe memory
>> leak...
>>
>> Any hints would be welcome.
>
> The type object inherits `tp_alloc` from Numpy. This routine always
> allocates memory of size NPY_SIZEOF_PYARRAYOBJECT for the
> PyArrayObject. Therefore, the write to new->unit in your
> __array_finalize__ goes to unallocated memory.
>
> This is probably a bug in Numpy --- arrayobject.c:array_alloc should
> respect the size specified by the subtype.
>
> A workaround is probably to specify a suitable tp_alloc routine yourself:
>
> PyType_GenericAlloc, /* tp_alloc */
> unitArray_new, /* tp_new */
> _PyObject_Del /* tp_free */
Now I have been playing around with this configuration and can't seem to get
rid of the memory leak I was talking about earlier. Reference counting seems to
be fine as far as I can tell. I am using the following tp_dealloc:
static void unitArray_dealloc(UnitArrayObject *obj) {
Py_XDECREF(obj->unit);
obj->unit = NULL;
if (obj->base.ob_type->tp_base != NULL) {
//call base dealloc
obj->base.ob_type->tp_base->tp_dealloc((PyObject*)obj);
}
}
I was also trying to provide my own tp_alloc like below and using the inherited
tp_free. This also crashes with a segmentation fault upon freeing saying that
the memory block was not allocated. Does this make sense?
static PyObject *
unitArray_alloc(PyTypeObject *type, Py_ssize_t NPY_UNUSED(nitems))
{
PyObject *obj;
/* nitems will always be 0 */
obj = (PyObject *)PyObject_Malloc(type->tp_basicsize);
PyObject_Init(obj, type);
return obj;
}
Cheers,
Christoph
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