Numpy-scalars vs Numpy 0-d arrays: copy or not copy?

[Travis Oliphant]

Sebastien Bardeau wrote:

>>One possible solution (there can be more) is using ndarray:
>>
>>In [47]: a=numpy.array([1,2,3], dtype="i4")
>>In [48]: n=1    # the position that you want to share
>>In [49]: b=numpy.ndarray(buffer=a[n:n+1], shape=(), dtype="i4")
>>  
>>    
>>
>Ok thanks. Actually that was also the solution I found. But this is much 
>more complicated when arrays are N dimensional with N>1, and above all 
>if user asks for a slice in one or more dimension. Here is how I 
>redefine the __getitem__ method for my arrays. Remember that the goal is 
>to return a 0-d array rather than a numpy.scalar when I extract a single 
>element out of a N-dimensional (N>=1) array:
>  
>

How about this.  To get the i,j,k,l element

a[i:i+1,j:j+1,k:k+1,l:l+1].squeeze()

-Travis


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