Thanks Sebastian. Casting it to an array would certainly help.
Another oddity of zero-ranked scalars is that they look iterable, but in
fact are not. Because all they do is generate an error.
>>> a = np.array(22)
Test if iterable:
>>> hasattr(a, __iter__)
True
Or:
>>> import collections
>>> i
I'm deeply puzzled by the recently changed behavior of zero-rank memmaps. I
think this change happened from version 1.6.0 to 1.6.1, which I'm currently
using.
>>> import numpy as np
Create a zero-rank memmap.
>>> x = np.memmap(filename='/tmp/m', dtype=float, mode='w+', shape=())
Give it a value
ething wrong? Are my expectations wrong? Or is this an issue
somewhere deeper in numpy? I looked at the memmap.py and it seems to me that
most of the work is delegated to numpy.ndarray.__new__. Something wrong there
maybe?
Can somebody help please?
Thanks!
Robert,
I've replaced my C:\Python24\Lib\site-packages\numpy\core\memmap.py
with the one from http://svn.scipy.org/svn/numpy/trunk/numpy/core/
and that fixes the problem.
Thanks for your quick help!
When is the new version of numpy due?
Wim Bakker
Any garbage below this line is not
nge(10):
a[i] = i
del a
a = numpy.memmap(r'C:\Temp\test.dat', mode='r', shape=(10,), dtype='b')
a = a.astype('d')
del a
==
The last delete produces the warnings.
Am I doing something wrong or should this be fixed in numpy?
Regards,
Wim Bakker
