Georg Holzmann wrote:
Hallo!
I found now a way to get the data:
Therefore I do the following (2D example):
obj = PyArray_FromDimsAndData(2, dim0, PyArray_DOUBLE, (char*)data);
PyArrayObject *tmp = (PyArrayObject*)obj;
tmp-flags = NPY_FARRAY;
if in that example I also
Hallo!
I found now a way to get the data:
Therefore I do the following (2D example):
obj = PyArray_FromDimsAndData(2, dim0, PyArray_DOUBLE, (char*)data);
PyArrayObject *tmp = (PyArrayObject*)obj;
tmp-flags = NPY_FARRAY;
if in that example I also change the strides:
int s =
Hallo!
This depends on what you are trying to do, but generally, I find that if
you can afford it memory-wise, it is much faster to just get a C
contiguous array if you treat your C array element per element. If you
Yes, but the problem is that this data is very big (up to my memory
Anne Archibald wrote:
On 26/10/2007, Georg Holzmann [EMAIL PROTECTED] wrote:
if in that example I also change the strides:
int s = tmp-strides[1];
tmp-strides[0] = s;
tmp-strides[1] = s * dim0[0];
Then I get in python the fortran-style array in right order.
This is
On 26/10/2007, Travis E. Oliphant [EMAIL PROTECTED] wrote:
There is an optimization where-in the inner-loops are done over the
dimension with the smallest stride.
What other cache-coherent optimizations do you recommend?
That sounds like a very good first step. I'm far from an expert on
this
Hallo!
I have the following problem: I get a data array in column major storage
order and want to use it as numpy array without copying data.
Therefore I do the following (2D example):
obj = PyArray_FromDimsAndData(2, dim0, PyArray_DOUBLE, (char*)data);
PyArrayObject *tmp =
