On Di, 2016-04-05 at 20:19 +0200, Sebastian Berg wrote: > On Di, 2016-04-05 at 09:48 -0700, mpc wrote: > > The idea is that I want to thin a large 2D buffer of x,y,z points > > to > > a given > > resolution by dividing the data into equal sized "cubes" (i.e. > > resolution is > > number of cubes along each axis) and averaging the points inside > > each > > cube > > (if any). > > > > Another point is timing your actual code, in this case you could have > noticed that all time is spend in the while loops and little time in > those min/max calls before. > > Algorithms, or what you do is the other thing. In the end, it seems > your code is just a high dimensional histogram. Though I am not sure > if > numpy's histogram is fast, I am sure it vastly outperforms this and > if
Hmm, well maybe not quite, but it seems similar like a weighted histogram. > you are interested in how it does this, you could even check its > code, > it is just in python (though numpy internally always has quite a lot > of > fun boilerplate to make sure of corner cases). > > And if you search for what you want to do first, you may find faster > solutions easily, batteries included and all, there are a lot of > tools > out there. The other point is, don't optimize much if you don't know > exactly what you need to optimize. > > - Sebastian > > > > > * # Fill up buffer data for demonstration purposes with initial > > buffer of > > size 10,000,000 to reduce to 1,000,000 > > size = 10000000 > > buffer = np.ndarray(shape=(size,3), dtype=np.float) > > # fill it up > > buffer[:, 0] = np.random.ranf(size) > > buffer[:, 1] = np.random.ranf(size) > > buffer[:, 2] = np.random.ranf(size) > > > > # Create result buffer to size of cubed resolution (i.e. 100 ^ > > 3 > > = > > 1,000,000) > > resolution = 100 > > thinned_buffer = np.ndarray(shape=(resolution ** 3,3), > > dtype=np.float) > > > > # Trying to convert the following into C to speed it up > > x_buffer = buffer[:, 0] > > y_buffer = buffer[:, 1] > > z_buffer = buffer[:, 2] > > min_x = x_buffer.min() > > max_x = x_buffer.max() > > min_y = y_buffer.min() > > max_y = y_buffer.max() > > min_z = z_buffer.min() > > max_z = z_buffer.max() > > z_block = (max_z - min_z) / resolution > > x_block = (max_x - min_x) / resolution > > y_block = (max_y - min_y) / resolution > > > > current_idx = 0 > > x_idx = min_x > > 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)) > > if inside_block_points[0].size > 0: > > mean_point = > > buffer[inside_block_points[0]].mean(axis=0) > > thinned_buffer[current_idx] = mean_point > > current_idx += 1 > > z_idx += z_block > > y_idx += y_block > > x_idx += x_block > > return thin_buffer > > * > > > > > > > > -- > > View this message in context: > > http://numpy-discussion.10968.n7.nabble > > .com/Multidimension-array-access-in-C-via-Python-API > > -tp42710p42726.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 > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion
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