ENH: Method to sample points randomly from within geometries #2860
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The CI failure is unrelated and is also on main. |
Co-authored-by: Joris Van den Bossche <jorisvandenbossche@gmail.com>
…andas into simple_sampling
| xmin, ymin, xmax, ymax = geom.bounds | ||
| candidates = [] | ||
| while len(candidates) < size: | ||
| batch = points_from_xy( |
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I think this is fine as a first implementation, but this sampling is potentially quite wasteful, if you have a lot of points, and your first sample gets say 95% of size, you would only need to target for another 5% but this will then try to draw the full length of size again. (But this is perhaps better than drawing too few points by guessing how many sampled points will be accepted on the next iteration).
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It is tricky to get the heuristic right if we wanted to use different size. It super depends on the convexity of each polygon. Maybe a less wasteful option would be to go with a number larger that size initially to have a higher chance of hitting the size at one go. But I'd leave that for later if needed.
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Yes, I kept the chunk size constant as a first pass to be conservative.
I think the statistically efficient method is to sample each round proportional to the hit rate times the remaining sample size, but that can cause the size of a round to get very large very quickly.
| "source": [ | ||
| "## Variable number of points\n", | ||
| "\n", | ||
| "You can also sample different number of points from different geometries if you pass an array specifying the size of the sample per geometry." |
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Do we allow to pass a column name as well? (instead of just the values, so I assume gdf.sample_points(gdf["col"]) works for sure)
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As we have in plotting? Not now. Do we want to?
jorisvandenbossche
changed the title
Done. |
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Failures are unrelated: the py38 is one is related to pyogrio (if that keeps failing, might be something with the 0.6.0 release), and the dev build is failing because of scikit-learn/scikit-learn#26290 |
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Thanks @ljwolf and @martinfleis! |
Ad discussed in person and during the dev call, to help the review process of #2363, it was decided to split the PR into multiple smaller ones dealing with one task per PR.
This PR implements the sampling based on samples either from a uniform distribution or using pointpats. Mostly based off #2363, with some minor changes and exposure of seed and random generator for better control.