scipy.spatial.cKDTree.sparse_distance_matrix¶
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cKDTree.sparse_distance_matrix(self, other, max_distance, p=2.)¶ Compute a sparse distance matrix
Computes a distance matrix between two cKDTrees, leaving as zero any distance greater than max_distance.
- Parameters
- othercKDTree
- 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 cKDTree >>> rng = np.random.default_rng() >>> points1 = rng.random((5, 2)) >>> points2 = rng.random((5, 2)) >>> kd_tree1 = cKDTree(points1) >>> kd_tree2 = cKDTree(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 ]])