Hello folks,
I recently was trying to write code to modify an array in-place (so
as not to invalidate any references to that array) via the standard
python idiom for lists, e.g.:
a[:] = numpy.flipud(a)
Now, flipud returns a view on 'a', so assigning that to 'a[:]'
provides pretty strange results as the buffer that is being read (the
view) is simultaneously modified. Here is an example:
In [2]: a = numpy.arange(10).reshape((5,2))
In [3]: a
Out[3]:
array([[0, 1],
[2, 3],
[4, 5],
[6, 7],
[8, 9]])
In [4]: numpy.flipud(a)
Out[4]:
array([[8, 9],
[6, 7],
[4, 5],
[2, 3],
[0, 1]])
In [5]: a[:] = numpy.flipud(a)
In [6]: a
Out[6]:
array([[8, 9],
[6, 7],
[4, 5],
[6, 7],
[8, 9]])
A question, then: Does this represent a bug? Or perhaps there is a
better idiom for modifying an array in-place than 'a[:] = ...'? Or is
incumbent on the user to ensure that any time an array is directly
modified, that the modifying array is not a view of the original array?
Thanks for any thoughts,
Zach Pincus
Program in Biomedical Informatics and Department of Biochemistry
Stanford University School of Medicine
_______________________________________________
Numpy-discussion mailing list
Numpy-discussion@scipy.org
http://projects.scipy.org/mailman/listinfo/numpy-discussion
