2015-02-13 12:51 GMT+01:00 Julian Taylor <jtaylor.debian@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!
 
Francesc