On Fri, Oct 06, 2006 at 10:30:43AM +0900, Bill Baxter wrote:
> If this is somehow controversial for some reason then let's discuss
> it. But so far the only response has been "copying data is a bad
> idea", which is really a separate issue.
An interesting issue, though. I've often wondered about an array
view where data is visible in more than one place. For example, you
have an array
x = N.arange((100))
on which you'd like to apply a filter, which, say, finds the moving
variance of the 5 closest elements. Here, a new representation of x
would be useful:
In [23]: indices = N.arange(5) + N.arange(96).reshape((96,1))
In [24]: indices
Out[24]:
array([[ 0, 1, 2, 3, 4],
[ 1, 2, 3, 4, 5],
...
[95, 96, 97, 98, 99]])
In [25]: y = x.index_view(indices) # returns array with same values as
# y = x[indices]
# except that no values are copied
In [29]: y
Out[29]:
array([[ 0, 1, 2, 3, 4],
[ 1, 2, 3, 4, 5],
[ 2, 3, 4, 5, 6],
...
after which you would be able to do
In [30]: y.var(axis=1)
Out[30]:
array([ 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.,
2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.,
While there would be a lot of jumping around in indices, at the loops
can be done in C, which should speed up the process. Of course, with
small arrays I can simply copy the data, but it becomes troublesome
with images and other such large signals.
I am not sure if providing the indices is the best way to initialise
such an array (otherwise maybe a rule for index calculation?) or how
feasible it would be to implement, so I'd be glad to hear anyone's
thoughts on the topic.
Regards
Stéfan
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