Hi,
I've been looking at a 64-bit numpy problem we were having on Solaris:
>>> a=numpy.zeros(0x180000000,dtype='b1')
>>> a.data
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: size must be zero or positive
A working fix seemed to be this:
Index: arrayobject.c
===================================================================
--- arrayobject.c (revision 6530)
+++ arrayobject.c (working copy)
@@ -6774,7 +6774,7 @@
static PyObject *
array_data_get(PyArrayObject *self)
{
- intp nbytes;
+ Py_ssize_t nbytes;
if (!(PyArray_ISONESEGMENT(self))) {
PyErr_SetString(PyExc_AttributeError, "cannot get single-"\
"segment buffer for discontiguous array");
@@ -6782,10 +6782,10 @@
}
nbytes = PyArray_NBYTES(self);
if PyArray_ISWRITEABLE(self) {
- return PyBuffer_FromReadWriteObject((PyObject *)self, 0, (int)
nbytes);
+ return PyBuffer_FromReadWriteObject((PyObject *)self, 0,
(Py_ssize_t) nbytes);
}
else {
- return PyBuffer_FromObject((PyObject *)self, 0, (int) nbytes);
+ return PyBuffer_FromObject((PyObject *)self, 0, (Py_ssize_t)
nbytes);
}
}
This fix could be simpler but still illustrates the typical problem:
use of (or cast to) int rather than something "pointer sized".
I can see that a lot of effort has gone into making numpy 64-bit
enabled, but I also see a number of uses of int which look like
problems on LP64 platforms. Is anyone using numpy in 64-bit
environments on a day-to-day basis? Are you using very large arrays,
i.e. over 2G in size?
Cheers,
Todd
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