Package Bio :: Package KDTree :: Module KDTree'

Module KDTree'


KD tree data structure for searching N-dimensional vectors.

The KD tree data structure can be used for all kinds of searches that
involve N-dimensional vectors, e.g.  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. See "Computational Geometry:
Algorithms and Applications" (Mark de Berg, Marc van Kreveld, Mark Overmars,
Otfried Schwarzkopf). Author: Thomas Hamelryck.

Classes

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KDTree
KD tree implementation (C++, SWIG python wrapper)

Functions

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_dist(p, q)

source code

_neighbor_test(nr_points, dim, bucket_size, radius)
Test all fixed radius neighbor search.

source code

_test(nr_points, dim, bucket_size, radius)
Test neighbor search.

source code

random(size=None)
Return random floats in the half-open interval [0.0, 1.0).

source code

Variables

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sqrt = <ufunc 'sqrt'>

Function Details

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_neighbor_test(nr_points, dim, bucket_size, radius)

source code

Test all fixed radius neighbor search.

Test all fixed radius neighbor search using the
KD tree C module.

o nr_points - number of points used in test
o dim - dimension of coords
o bucket_size - nr of points per tree node
o radius - radius of search (typically 0.05 or so)

_test(nr_points, dim, bucket_size, radius)

source code

Test neighbor search.

Test neighbor search using the KD tree C module.

o nr_points - number of points used in test
o dim - dimension of coords
o bucket_size - nr of points per tree node
o radius - radius of search (typically 0.05 or so)