# [Numpy-discussion] Array views

srean srean.list@gmail....
Sat Mar 26 12:32:24 CDT 2011

```Hi,

I am also interested in this. In my application there is a large 2d array,
lets call it 'b' to keep the notation consistent in the thread.  b's
columns need to be recomputed often. Ideally this re-computation happens in
a function. Lets call that function updater(b, col_index): The simplest
example is where
updater(b, col_index) is a matrix vector multiply, where the matrix or the
vector changes.

Is there anyway apart from using ufuncs that I can make updater() write the
result directly in b and not create a new temporary column that is then
copied into b ?  Say for the matrix vector multiply example.
I can write the matrix vector product in terms of ufuncs but will lose out
in terms of speed.

In the best case scenario I would like to maintain 'b' in a csr sparse
matrix form, as 'b' participates in a matrix vector multiply. I think csr
would be asking for too much, but even ccs should help.  I dont want to
clutter this thread with the sparsity issues though, any solution to the
original question or pointers to solutions would be appreciated.

Thanks
--srean

On Sat, Mar 26, 2011 at 12:10 PM, Hugo Gagnon <
sourceforge.numpy@user.fastmail.fm> wrote:

> Hello,
>
> Say I have a few 1d arrays and one 2d array which columns I want to be
> the 1d arrays.
> I also want all the a's arrays to share the *same data* with the b
> array.
> If I call my 1d arrays a1, a2, etc. and my 2d array b, then
>
> b[:,0] = a1[:]
> b[:,1] = a2[:]
> ...
>
> won't work because apparently copying occurs.
> I tried it the other way around i.e.
>
> a1 = b[:,0]
> a2 = b[:,1]
> ...
>
> and it works but that doesn't help me for my problem.
> Is there a way to reformulate the first code snippet above but with
> shallow copying?
>
> Thanks,
> --
>  Hugo Gagnon
> --
>  Hugo Gagnon
>
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>
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