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:
x[...] = 22 x memmap(22.0)
So far so good. But now:
b = (x + x) / 1.5 b memmap(29.333333333333332)
WT.? Why is the result of this calculation a memmap? It even thinks that it's still linked to the file, but it's not:
b.filename '/tmp/m'
If I try this with arrays then I don't get this weird behavior:
a = np.array(2, dtype=float)
(a + a) / 2.5 1.6000000000000001
which gives me a Python float, not a zero-rank array. Why does the memmap behave like that? Why do I get a memmap even though it's not connected to any file? Regards, Wim
Hey, this is indirectly related (I think it might fix many of these memmap oddities though?)... Why does the memmap object not implement: def __array_wrap__(self, obj): if self is obj: return obj return np.array(obj, copy=False, subok=False) By doing so if we have a ufunc with only memmap objects, the result will not be a memmap object, but if the ufunc has an output parameter, then "self is obj" which means that it is not casted. The problem is that subclass automatically inherit an __array_wrap__ method that sets the result to the subclasses type (which is not generally wanted for memmaps). May make a PR for this... Regards, Sebastian On Fri, 2012-09-21 at 14:59 +0200, Wim Bakker wrote:
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:
x[...] = 22 x memmap(22.0)
So far so good. But now:
b = (x + x) / 1.5 b memmap(29.333333333333332)
WT.? Why is the result of this calculation a memmap?
It even thinks that it's still linked to the file, but it's not:
b.filename '/tmp/m'
If I try this with arrays then I don't get this weird behavior:
a = np.array(2, dtype=float)
(a + a) / 2.5 1.6000000000000001
which gives me a Python float, not a zero-rank array.
Why does the memmap behave like that? Why do I get a memmap even though it's not connected to any file?
Regards,
Wim _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
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