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.
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