A fast ball tree implementation for three dimensional (weighted) data with an Euclidean distance norm. The base implementation is in C and there is a wrapper for Python. The tree is optimised towards spatial correlation function calculations since it provides fast counting routinge, e.g. by implementing a dualtree query algorithm.
- Code: https://github.com/jlvdb/balltree.git
- Docs: https://balltree.readthedocs.io/
- PyPI: https://pypi.org/project/balltree/
A C library can be built with the provided make file, the python wrapper is
automatically compiled and installed with pip install balltree.
The installation does not require any external C libraries, the python wrapper
requires the Python.h header (which should be included in a default python
installation) and numpy (including numpy/arrayobject.h).
- Jan Luca van den Busch (author, Ruhr-Universit??t Bochum, Astronomisches Institut)