This is a Rust implementation of a quadtree data structure, which can be used to partition two-dimensional space and efficiently perform spatial queries such as searching for all points within a certain area.
This project includes Python bindings using the PyO3 library, allowing the quadtree to be fast and used from Python.
To install in the activated Python environment run pip install quadtree-fast or run:
pip install maturin
maturin develop --release
Python usage:
from quadtree import Point, QuadTree, Rect
# Create a new region centered around (0,0) with width and height of 100
boundary = Rect(0.0, 0.0, 100.0, 100.0)
# Create quadtree out of that region with 100 maximum points and 0 starting depth
quadtree = QuadTree(boundary, 100, 0)
# Insert some points into the quadtree
point1 = Point(10.0, 10.0)
point2 = Point(20.0, 20.0)
point3 = Point(30.0, 30.0)
point4 = Point(40.0, 40.0)
quadtree.insert(point1)
quadtree.insert(point2)
quadtree.insert(point3)
quadtree.insert(point4)
# Search for all points within rectangle centered at (15, 15) with height and width of 20
search_rect = Rect(15.0, 15.0, 20.0, 20.0)
result = quadtree.query_rect(search_rect)
print(result) # Output: [Point(x=10.0, y=10.0), Point(x=20.0, y=20.0)]
# Search for all points with circle of radius 20 centered at (0, 0)
result = quadtree.query_radius(0, 0, 20.0)
print(result) # Output: [Point(x=10.0, y=10.0)]
Data can be attached to each point, e.g.:
p1 = Point(10.0, 5.0, data=1)
p2 = Point(10.0, 10.0, data="any Python object")