sparse_distance_matrix(self, other, max_distance, p=2.0, output_type='dok_matrix')

Computes a distance matrix between two KDTrees, leaving as zero any distance greater than max_distance.

Parameters

other : KDTree

max_distance : positive float

p : float, 1<=p<=infinity

Which Minkowski p-norm to use. A finite large p may cause a ValueError if overflow can occur.

output_type : string, optional

Which container to use for output data. Options: 'dok_matrix', 'coo_matrix', 'dict', or 'ndarray'. Default: 'dok_matrix'.

versionadded

Returns

result : dok_matrix, coo_matrix, dict or ndarray

Sparse matrix representing the results in "dictionary of keys" format. If a dict is returned the keys are (i,j) tuples of indices. If output_type is 'ndarray' a record array with fields 'i', 'j', and 'v' is returned,

Compute a sparse distance matrix.

Examples

You can compute a sparse distance matrix between two kd-trees:

>>> import numpy as np
... from scipy.spatial import KDTree
... rng = np.random.default_rng()
... points1 = rng.random((5, 2))
... points2 = rng.random((5, 2))
... kd_tree1 = KDTree(points1)
... kd_tree2 = KDTree(points2)
... sdm = kd_tree1.sparse_distance_matrix(kd_tree2, 0.3)
... sdm.toarray()
array([[0.        , 0.        , 0.12295571, 0.        , 0.        ],
   [0.        , 0.        , 0.        , 0.        , 0.        ],
   [0.28942611, 0.        , 0.        , 0.2333084 , 0.        ],
   [0.        , 0.        , 0.        , 0.        , 0.        ],
   [0.24617575, 0.29571802, 0.26836782, 0.        , 0.        ]])

You can check distances above the :None:None:`max_distance` are zeros:

>>> from scipy.spatial import distance_matrix
... distance_matrix(points1, points2)
array([[0.56906522, 0.39923701, 0.12295571, 0.8658745 , 0.79428925],
   [0.37327919, 0.7225693 , 0.87665969, 0.32580855, 0.75679479],
   [0.28942611, 0.30088013, 0.6395831 , 0.2333084 , 0.33630734],
   [0.31994999, 0.72658602, 0.71124834, 0.55396483, 0.90785663],
   [0.24617575, 0.29571802, 0.26836782, 0.57714465, 0.6473269 ]])

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

scipy.spatial._kdtree.KDTree.sparse_distance_matrix

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them

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GitHub : /scipy/spatial/_kdtree.py#811
type: <class 'function'>
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