[Numpy-discussion] Vectorizing computation
Julian Taylor
jtaylor.debian at googlemail.com
Fri Feb 13 07:25:31 EST 2015
On 02/13/2015 01:03 PM, Francesc Alted wrote:
> 2015-02-13 12:51 GMT+01:00 Julian Taylor <jtaylor.debian at googlemail.com
> <mailto:jtaylor.debian at googlemail.com>>:
>
> On 02/13/2015 11:51 AM, Francesc Alted wrote:
> > Hi,
> >
> > I would like to vectorize the next computation:
> >
> > nx, ny, nz = 720, 180, 3
> > outheight = np.arange(nz) * 3
> > oro = np.arange(nx * ny).reshape((nx, ny))
> >
> > def compute1(outheight, oro):
> > result = np.zeros((nx, ny, nz))
> > for ix in range(nx):
> > for iz in range(nz):
> > result[ix, :, iz] = outheight[iz] + oro[ix, :]
> > return result
> >
> > I think this should be possible by using an advanced use of
> broadcasting
> > in numpy. Anyone willing to post a solution?
>
>
> result = outheight + oro.reshape(nx, ny, 1)
>
>
> And 4x faster for my case. Oh my, I am afraid that my mind will never
> scratch all the amazing possibilities that broadcasting is offering :)
>
> Thank you very much for such an elegant solution!
>
if speed is a concern this is faster as it has a better data layout for
numpy during the computation, but the result may be worse layed out for
further processing
result = outheight.reshape(nz, 1, 1) + oro
return np.rollaxis(result, 0, 3)
More information about the NumPy-Discussion
mailing list