2015-02-13 13:25 GMT+01:00 Julian Taylor <[email protected]>:
> On 02/13/2015 01:03 PM, Francesc Alted wrote: > > 2015-02-13 12:51 GMT+01:00 Julian Taylor <[email protected] > > <mailto:[email protected]>>: > > > > 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) > > Holly cow, this makes for another 4x speed improvement! I don't think I need that much in my scenario, so I will stick with the first one (more readable and the expected data layout), but thanks a lot! Francesc
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