AABBTree - Axis-Aligned Bounding Box Trees

Repository Documentation PyPI

AABBTree is a pure Python implementation of a static d-dimensional axis aligned bounding box (AABB) tree. It is inspired by Introductory Guide to AABB Tree Collision Detection from Azure From The Trenches.

Left: An AABB tree, leaves numbered by insertion order. Right: The AABBs and their bounding boxes.

Installation

AABBTree is available through PyPI and can be installed by running:

pip install aabbtree

To test that the package installed properly, run:

python -c "import aabbtree"

Alternatively, the package can be installed from source by downloading the latest release from the AABBTree repository on GitHub. Extract the source and, from the top-level directory, run:

pip install -e .

The --user flag may be needed, depending on permissions.

Example

The following example shows how to build an AABB tree and test for overlap:

>>> from aabbtree import AABB
>>> from aabbtree import AABBTree
>>> tree = AABBTree()
>>> aabb1 = AABB([(0, 0), (0, 0)])
>>> aabb2 = AABB([(-1, 1), (-1, 1)])
>>> aabb3 = AABB([(4, 5), (2, 3)])
>>> tree.add(aabb1, 'box 1')
>>> tree.does_overlap(aabb2)
True
>>> tree.overlap_values(aabb2)
['box 1']
>>> tree.does_overlap(aabb3)
False
>>> tree.add(aabb3)
>>> print(tree)
AABB: [(0, 5), (0, 3)]
Value: None
Left:
  AABB: [(0, 0), (0, 0)]
  Value: box 1
  Left: None
  Right: None
Right:
  AABB: [(4, 5), (2, 3)]
  Value: None
  Left: None
  Right: None

Documentation

Documentation for the project is available at https://aabbtree.readthedocs.io.

Contributing

Contributions to the project are welcome. Please visit the AABBTree repository to clone the source files, create a pull request, and submit issues.

Publication

If you use AABBTree in you work, please consider including this citation in your bibliography:

K. A. Hart and J. J. Rimoli, Generation of statistically representative microstructures with direct grain geomety control, Computer Methods in Applied Mechanics and Engineering, 370 (2020), 113242. (BibTeX) (DOI)

The incremental insertion method is discussed in section 2.2.2 of the paper.

License and Copyright Notice

Copyright © 2019-2023, Georgia Tech Research Corporation

AABBTree is open source and freely available under the terms of the MIT license.