scipy.spatial.KDTree.

# sparse_distance_matrix#

KDTree.sparse_distance_matrix(other, max_distance, p=2.0, output_type='dok_matrix')[source]#

Compute a sparse distance matrix.

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

Parameters:
otherKDTree
max_distancepositive float
pfloat, 1<=p<=infinity

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

output_typestring, optional

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

Returns:
resultdok_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,

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 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 ]])
```