[Numpy-discussion] Multidimension array access in C via Python API
chris.barker at noaa.gov
Tue Apr 5 14:16:14 EDT 2016
On Tue, Apr 5, 2016 at 9:48 AM, mpc <matt.p.conte at gmail.com> wrote:
> The idea is that I want to thin a large 2D buffer of x,y,z points to a
> resolution by dividing the data into equal sized "cubes" (i.e. resolution
> number of cubes along each axis) and averaging the points inside each cube
> (if any).
are the original x,y,z points aranged along a nice even grid? or
if the former, I have Cython code that does that :-) I could dig it up,
haven't used it in a while. or scikit.image might have something.
If the latter, then Ben is right -- you NEED a spatial index --
scipy.spatial.kdtree will probably do what you want, though it would be
easier to use a sphere to average over than a cube.
Also, maybe Kernel Density Estimation could help here????
Otherwise, you could use Cython to write a non-vectorized version of your
below code -- it would be order NM where N is the number of "cubes" and M
is the number of original points. I think, but would be a lot faster than
the pure python.
Here is where you would do the cython:
while x_idx < max_x:
> y_idx = min_y
> while y_idx < max_y:
> z_idx = min_z
> while z_idx < max_z:
> inside_block_points = np.where((x_buffer >= x_idx) &
> (x_buffer <=
> x_idx + x_block) &
> (y_buffer >=
> y_idx) &
> (y_buffer <=
> y_idx + y_block) &
> (z_buffer >=
> z_idx) &
> (z_buffer <=
> z_idx + z_block))
instead of where, you could loop through all your points and find the ones
inside your extents.
though now that I think about it -- you are mapping arbitrary points to a
regular grid, so you only need to go through the points once, assigning
each one to a bin, and then compute the average in each bin.
Is this almost a histogram?
Christopher Barker, Ph.D.
Emergency Response Division
NOAA/NOS/OR&R (206) 526-6959 voice
7600 Sand Point Way NE (206) 526-6329 fax
Seattle, WA 98115 (206) 526-6317 main reception
Chris.Barker at noaa.gov
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