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# 8. KDTree module

Author: | Thomas Hamelryck
Year: | 2002
Licence: | Biopython

The KD tree data structure can be used for all kinds of searches that involve N-dimensional vectors. For example, neighbor searches (find all points within a radius of a given point) or finding all point pairs in a set that are within a certain radius of each other.

MDAnalysis uses Biopython‘s KDTree module for distance selections between atoms but its use goes far beyond this narrow application.

See also

The algorithms are based on [deBerg2000] with improvements suggested by [Bentley1990]. See the documentation of the individual classes for details.

References

[deBerg2000] | Mark de Berg, Marc van Kreveld, Mark Overmars, Otfried Schwarzkopf. Computational Geometry: Algorithms and Applications. Springer, 2nd edition. 2000.

[Bentley1990] | J.L. Bentley, “Kd trees for semidynamic point sets,” in Sixth Annual ACM Symposium on Computational Geometry, vol. 91. San Francisco, 1990

Contents

- 8.1. KDTree — `MDAnalysis.KDTree.KDTree`
- 8.2. Fast atom neighbor lookup using a KD tree (implemented in C++) — `MDAnalysis.KDTree.NeighborSearch`

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