Dear all,
I have a question about the behaviour of
y = np.array(x, copy=False, dtype='float32')
when x is a memmap. If we check the memmap attribute of mmap
print "mmap attribute", y._mmap
numpy tells us that y is not a memmap.
But the following code snippet crashes the python interpreter
# opens the memmap
with open(filename,'r+b') as f:
mm = mmap.mmap(f.fileno(),0)
x = np.frombuffer(mm, dtype='float32')
# builds an array from the memmap, with the option copy=False
y = np.array(x, copy=False, dtype='float32')
print "before", y
# closes the file
mm.close()
print "after", y
In my code I use memmaps to share read-only objects when doing parallel
processing
and the behaviour of np.array, even if not consistent, it's desirable.
I share scipy sparse matrices over many processes and if np.array would
make a copy
when dealing with memmaps this would force me to rewrite part of the sparse
matrices
code.
Would it be possible in the future releases of numpy to have np.array
check,
if copy is false, if y is a memmap and in that case return a full memmap
object
instead of slicing it?
Best wishes
Isaia
P.S. A longer account of the issue may be found on my university blog
http://www.im.ufrj.br/nisoli/blog/?p=131
--
Isaia Nisoli
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