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scipy.spatial.KDTree.

# sparse_distance_matrix

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

  Compute a sparse distance 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’.

      Added in version 1.6.0.

  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,

  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 ]])