KDTree

class KDTree(*args)

Partition tree data structure.

Allows one to store and search points fast.

Parameters:

sample2-d sequence of float

Points.

See also

NearestNeighbourAlgorithm

Notes

When nanoflann support is enabled, the ResourceMap key KDTree-leaf_max_size allows one to set the tree leaf size which involves a build vs query tradeoff: large values will tend to result in fast build and slow queries, and small values typically result in slow build and fast queries. Also when nanoflann version is at least v1.5.0, the ResourceMap key KDTree-n_thread_build allows one to set the number of threads used during the tree building phase. It is also decided by OPENTURNS_NUM_THREADS.

Examples

>>> import openturns as ot
>>> sample = ot.Normal(2).getSample(10)
>>> tree = ot.KDTree(sample)
>>> neighbour = sample[tree.query([0.1, 0.2])]

Methods

getClassName()	Accessor to the object's name.
getName()	Accessor to the object's name.
getSample()	Get the points which have been used to build this nearest neighbour algorithm.
hasName()	Test if the object is named.
query(*args)	Get the index of the nearest neighbour of the given point.
queryK(x, k[, sorted])	Get the indices of nearest neighbours of the given point.
setName(name)	Accessor to the object's name.
setSample(sample)	Build a NearestNeighbourAlgorithm from these points.

__init__(*args)

getClassName()

Accessor to the object’s name.

Returns:

class_namestr

The object class name (object.__class__.__name__).

getName()

Accessor to the object’s name.

Returns:

namestr

The name of the object.

getSample()

Get the points which have been used to build this nearest neighbour algorithm.

Returns:

sampleSample

Input points.

hasName()

Test if the object is named.

Returns:

hasNamebool

True if the name is not empty.

query(*args)

Get the index of the nearest neighbour of the given point.

Available usages:

query(point)

query(sample)

Parameters:

pointsequence of float

Given point.

sample2-d sequence of float

Given points.

Returns:

indexint

Index of the nearest neighbour of the given point.

indicesIndices

Index of the nearest neighbour of the given points.

queryK(x, k, sorted=False)

Get the indices of nearest neighbours of the given point.

Parameters:

xsequence of float

Given point.

kint

Number of indices to return.

sortedbool, optional

Boolean to tell whether returned indices are sorted according to the distance to the given point.

Returns:

indicessequence of int

Indices of the k nearest neighbours of the given point.

setName(name)

Accessor to the object’s name.

Parameters:

namestr

The name of the object.

setSample(sample)

Build a NearestNeighbourAlgorithm from these points.

Parameters:

sampleSample

Input points.