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:
>>> 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)