retain dimensions for numpy slice

[duncan smith]

On 24/10/16 19:05, Peter Otten wrote:
> duncan smith wrote:
> 
>> Hello,
>>       I have several arrays that I need to combine elementwise in
>> various fashions. They are basically probability tables and there is a
>> mapping of axes to variables. I have code for transposing and reshaping
>> that aligns the variables / axes so the usual broadcasting rules achieve
>> the desired objective. But for a specific application I want to avoid
>> the transposing and reshaping. So I've specified arrays that contain the
>> full dimensionality (dimensions equal to the total number of variables).
>> e.g.
>>
>> Arrays with shape,
>>
>> [1,3,3] and [2,3,1]
>>
>> to represent probability tables with variables
>>
>> [B,C] and [A,B].
>>
>> One operation that I need that is not elementwise is summing over axes,
>> but I can use numpy.sum with keepdims=True to retain the appropriate
>> shape.
>>
>> The problem I have is with slicing. This drops dimensions. Does anyone
>> know of a solution to this so that I can e.g. take an array with shape
>> [2,3,1] and generate a slice with shape [2,1,1]? I'm hoping to avoid
>> having to manually reshape it. Thanks.
> 
> Can you clarify your requirement or give an example of what you want?
> 
> Given an array 
> 
>>>> a.shape
> (2, 3, 1)
> 
> you can get a slice with shape (2,1,1) with (for example)
> 
>>>> a[:,:1,:].shape
> (2, 1, 1)
> 
> or even
> 
>>>> newshape = (2, 1, 1)
>>>> a[tuple(slice(d) for d in newshape)].shape
> (2, 1, 1)
> 
> but that's probably not what you are asking for...
> 

Thanks. I think that's exactly what I wanted.

Duncan