On Mon, Apr 11, 2016 at 5:24 PM Chris Barker wrote:
> On Fri, Apr 8, 2016 at 4:37 PM, Ian Henriksen <
> insertinterestingnameh...@gmail.com> wrote:
>
>
>> If we introduced the T2 syntax, this would be valid:
>>
>> a @ b.T2
>>
>> It makes the intent much clearer.
>>
>
>
On Fri, Apr 8, 2016 at 4:37 PM, Ian Henriksen <
insertinterestingnameh...@gmail.com> wrote:
> If we introduced the T2 syntax, this would be valid:
>
> a @ b.T2
>
> It makes the intent much clearer.
>
would:
a @ colvector(b)
work too? or is T2 generalized to more than one column? (though I
On Mon, Apr 11, 2016 at 5:39 AM, Matěj Týč wrote:
> * ... I do see some value in providing a canonical right way to
> construct shared memory arrays in NumPy, but I'm not very happy with
> this solution, ... terrible code organization (with the global
> variables):
> * I
Dear Numpy developers,
I propose a pull request https://github.com/numpy/numpy/pull/7533 that
features numpy arrays that can be shared among processes (with some
effort).
Why:
In CPython, multiprocessing is the only way of how to exploit
multi-core CPUs if your parallel code can't avoid creating
Using order='F' solved the problem.
Thanks for reply.
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Yes, f2py is probably copying the arrays; you can check this by appending
-DF2PY_REPORT_ON_ARRAY_COPY=1 to your call to f2py.
I normally prefer to keep the numpy arrays C-order (most efficient for
numpy) and simply pass the array transpose to the f2py-ized fortran routine.
This means that the