A pure Python Quadtree implementation.
Quadtrees are a useful data structure for sparse datasets where the location/position of the data is important. They're especially good for spatial indexing & image processing.
An actual visualization of a quads.QuadTree:
Full documentation at https://quads.readthedocs.io/en/latest/
>>> import quads
>>> tree = quads.QuadTree(
... (0, 0), # The center point
... 10, # The width
... 10, # The height
... )
# You can choose to simply represent points that exist.
>>> tree.insert((1, 2))
True
# ...or include extra data at those points.
>>> tree.insert(quads.Point(4, -3, data="Samus"))
True
# You can search for a given point. It returns the point if found...
>>> tree.find((1, 2))
Point(1, 2)
# Or `None` if there's no match.
>>> tree.find((4, -4))
None
# You can also find all the points within a given region.
>>> bb = quads.BoundingBox(min_x=-1, min_y=-2, max_x=2, max_y=2)
>>> tree.within_bb(bb)
[Point(1, 2)]
# You can also search to find the nearest neighbors of a point, even
# if that point doesn't have data within the quadtree.
>>> tree.nearest_neighbors((0, 1), count=2)
[
Point(1, 2),
Point(4, -4),
]
# And if you have `matplotlib` installed (not required!), you can visualize
# the tree.
>>> quads.visualize(tree)$ pip install quads
- Python 3.7+ (untested on older versions but may work)
$ git clone https://github.com/toastdriven/quads.git
$ cd quads
$ poetry install
$ poetry shell
# Just the tests.
$ pytest .
# With coverage.
$ pytest -s --cov=quads .
# And with pretty reports.
$ pytest -s --cov=quads . && coverage html
New BSD