Eh. The order of the outputs will be different than your code, if that makes a difference.
On Tue, Apr 5, 2016 at 3:31 PM, Eric Moore <e...@redtetrahedron.org> wrote: > def reduce_data(buffer, resolution): > thinned_buffer = np.zeros((resolution**3, 3)) > > min_xyz = buffer.min(axis=0) > max_xyz = buffer.max(axis=0) > delta_xyz = max_xyz - min_xyz > > inds_xyz = np.floor(resolution * (buffer - min_xyz) / > delta_xyz).astype(int) > > # handle values right at the max > inds_xyz[inds_xyz == resolution] -= 1 > > # covert to linear indices so that we can use np.add.at > inds_lin = inds_xyz[:,0] > inds_lin += inds_xyz[:,1] * resolution > inds_lin += inds_xyz[:,2] * resolution**2 > > np.add.at(thinned_buffer, inds_lin, buffer) > counts = np.bincount(inds_lin, minlength=resolution**3) > > thinned_buffer[counts != 0, :] /= counts[counts != 0, None] > return thinned_buffer > > > The bulk of the time is spent in np.add.at, so just over 5 s here with > your 1e7 to 1e6 example. > > On Tue, Apr 5, 2016 at 2:09 PM, mpc <matt.p.co...@gmail.com> wrote: > >> This wasn't intended to be a histogram, but you're right in that it would >> be >> much better if I can just go through each point once and bin the results, >> that makes more sense, thanks! >> >> >> >> -- >> View this message in context: >> http://numpy-discussion.10968.n7.nabble.com/Multidimension-array-access-in-C-via-Python-API-tp42710p42733.html >> Sent from the Numpy-discussion mailing list archive at Nabble.com. >> _______________________________________________ >> NumPy-Discussion mailing list >> NumPy-Discussion@scipy.org >> https://mail.scipy.org/mailman/listinfo/numpy-discussion >> > >
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