Computes distances to polygon edges and vertices and can check whether points are inside/outside.
This library is optimized to perform well with hundreds or thousands of polygons and thousands or millions of points.
Example timings (190 polygons, 1 M reference points, run on i7-10710U):
- distances to nearest edges: 0.7 s
- distances to nearest vertices: 0.6 s
- check whether points are inside or outside: 0.1 s
$ pip install polygons
- Python: 3.8 - 3.12
- Operating systems: Linux, macOS, and Windows
- Check whether points are inside or outside polygons
- Nearest distances to edges
- Nearest distances to vertices
If you use this tool in a program or publication, please acknowledge its author(s):
@misc{polygons,
author = {Bast, Radovan},
title = {Polygons: Fast points-in-polygon test and distances to polygons},
month = {1},
year = {2024},
publisher = {Zenodo},
version = {v0.3.3},
doi = {10.5281/zenodo.3825616},
url = {https://doi.org/10.5281/zenodo.3825616}
}import polygons
# polygon_points is a list of lists
# the library has been developed to perform
# with very many polygons - this is just to have a simple example
# in this example the polygons have the same number of points but there
# is no restriction like this, this is only an example
polygon_points = [
[(0.0, 0.0), (1.0, 0.0), (1.0, 1.0), (0.0, 1.0)],
[(0.0, 2.0), (1.0, 2.0), (1.0, 3.0), (0.0, 3.0)],
]
# the more points you compute in one go, the better
# here using two points to make a simple example but if you have many points
# then compute a thousand or a million in one go
# so that the library can parallelize over the points
points = [(0.5, 0.5), (0.5, -0.5)]
# parameters for the tree construction:
# - each tree node has 4 children nodes
# - each leaf collects 4 edges
# you can try different parameters and check the timing
# they (should) have no effect on the results apart from timing
num_edges_children = 4
num_nodes_children = 4
tree = polygons.build_search_tree(
polygon_points, num_edges_children, num_nodes_children
)
inside = polygons.points_are_inside(tree, points)
print(inside) # [True, False]
# indices are the indices of the nearest polygon vertices (counted
# consecutively)
indices, distances = polygons.distances_nearest_vertices(tree, points)
print(indices) # [0, 0]
print(distances) # [0.7071067811865476, 0.7071067811865476]
distances = polygons.distances_nearest_edges(tree, points)
print(distances) # [0.5, 0.5]
indices, distances = polygons.distances_nearest_vertices(
tree, [(0.6, 0.6), (0.5, -0.5)]
)
print(indices) # [2, 0]
print(distances) # [0.5656854249492381, 0.7071067811865476]- http://geomalgorithms.com/a03-_inclusion.html
- https://en.wikipedia.org/wiki/Point_in_polygon
- https://en.wikipedia.org/wiki/Binary_space_partitioning
Running the benchmark:
$ cargo test --release -- --ignored --nocapture
Python interface inspired by https://github.com/dev-cafe/rustafarian.
Building and testing the Python interface:
$ maturin develop
Social media preview generated using https://github.com/qrohlf/trianglify.
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