On 2010-02-26 15:26, Pauli Virtanen wrote:
> No, the unpickled void scalar will own its data. The problem is that
> either the data is not saved correctly (unlikely), or it is unpickled
> incorrectly.
>
> The relevant code path to look at is multiarraymodule:array_scalar ->
> scalarapi.c:PyArray_Sc
On 2010-02-26 15:02, Robert Kern wrote:
>> Is this a known limitation?
>
> Nope. New bug! Thanks!
Good. I'm not crazy after all :)
> Pickling of complete arrays works. A quick workaround would be to send
> rank-0 scalars:
>
>Pool.map(map(np.asarray, x))
>
> Or just tuples:
>
>Pool.map(map
pe, 2010-02-26 kello 14:41 -0800, Martin Spacek kirjoitti:
[clip: pickling/unpickling numpy.void scalar objects]
> I suppose numpy.void is as it suggests, a pointer to a specific place in
> memory.
> I'm just surprised that this pointer isn't dereferenced before pickling Or is
> it? I'm not skil
On Fri, Feb 26, 2010 at 16:41, Martin Spacek wrote:
> I have a 1D structured ndarray with several different fields in the dtype. I'm
> using multiprocessing.Pool.map() to iterate over this structured ndarray,
> passing one entry (of type numpy.void) at a time to the function to be called
> by
> e
I have a 1D structured ndarray with several different fields in the dtype. I'm
using multiprocessing.Pool.map() to iterate over this structured ndarray,
passing one entry (of type numpy.void) at a time to the function to be called
by
each process in the pool. After much confusion about why this
