Re: [Numpy-discussion] advanced indexing question

[David Kershaw]

Sebastian Berg  sipsolutions.net> writes:
> 
> Python has a mechanism both for getting an item and for setting an item.
> The latter will end up doing this (python already does this for us):
> x[:,d,:,d] = x[:,d,:,d] + 1
> so there is an item assignment going on (__setitem__ not __getitem__)
> 
> - Sebastian
> 
> 
> 
> 
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Thanks for the prompt help Sebastian,

So can I use any legitimate ndarray indexing selection object, obj, in
 x.__setitem__(obj,y)
and as long as y's shape can be broadcast to x[obj]'s shape it will always 
set the appropriate elements of x to the corresponding elements of y?



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[Numpy-discussion] advanced indexing question

[David Kershaw]

The numpy reference manual, array objects/indexing/advance indexing, 
says: 
Advanced indexing always returns a copy of the data (contrast with 
basic slicing that returns a view).

If I run the following code:
 import numpy as np
 d=range[2]
 x=np.arange(36).reshape(3,2,3,2)
 y=x[:,d,:,d]
 y+=1
 print x
 x[:,d,:,d]+=1
 print x
then the first print x shows that x is unchanged as it should be since y 
was a copy, not a view, but the second print x shows that all the elements 
of x with 1st index = 3rd index are now 1 bigger. Why did the left side of
 x[:,d,:,d]+=1
act like a view and not a copy?

Thanks,
David

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