On 5/3/14, 11:56 PM, Siegfried Gonzi wrote:
> Hi all
>
> I noticed IDL uses at least 400% (4 processors or cores) out of the box
> for simple things like reading and processing files, calculating the
> mean etc.
>
> I have never seen this happening with numpy except for the linalgebra
> stuff (e.g
If b is indeed big I don't see a problem with the python loop, elegance
aside; but Cython will not beat it on that front.
On Mon, May 5, 2014 at 9:34 AM, srean wrote:
> Great ! thanks. I should have seen that.
>
> Is there any way array multiplication (as opposed to matrix
> multiplication) can
Great ! thanks. I should have seen that.
Is there any way array multiplication (as opposed to matrix multiplication)
can be sped up without forming A and (A * b) explicitly.
A = np.repeat(x, [4, 2, 1, 3], axis = 0)# A.shape == 10,10
c = sum(b * A, axis = 1)# b.shape ==
