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uxarray.grid.neighbors.BallTree

uxarray.grid.neighbors.BallTree#

class uxarray.grid.neighbors.BallTree(grid, coordinates='nodes', coordinate_system='spherical', distance_metric='haversine', reconstruct=False)#

Custom BallTree data structure written around the sklearn.neighbors.BallTree implementation for use with either the (node_x, node_y, node_z) and (node_lon, node_lat), edge (edge_x, edge_y, edge_z) and (edge_lon, edge_lat), or center (face_x, face_y, face_z) and (face_lon, face_lat) nodes of the inputted unstructured grid.

Parameters:

  • grid (ux.Grid) – Source grid used to construct the BallTree

  • coordinates (str, default="nodes") – Identifies which tree to construct or select, with “nodes” selecting the Corner Nodes, “face centers” selecting the Face Centers of each face, and “edge centers” selecting the edge centers of each face.

  • distance_metric (str, default="haversine") – Distance metric used to construct the BallTree, options include: ‘euclidean’, ‘l2’, ‘minkowski’, ‘p’,’manhattan’, ‘cityblock’, ‘l1’, ‘chebyshev’, ‘infinity’, ‘seuclidean’, ‘mahalanobis’, ‘hamming’, ‘canberra’, ‘braycurtis’, ‘jaccard’, ‘dice’, ‘rogerstanimoto’, ‘russellrao’, ‘sokalmichener’, ‘sokalsneath’, ‘haversine’

Notes

See sklearn.neighbors.BallTree for further information about the wrapped data structures.

__init__(grid, coordinates='nodes', coordinate_system='spherical', distance_metric='haversine', reconstruct=False)#

Methods

__init__(grid[, coordinates, ...])

query(coords[, k, in_radians, ...])

Queries the tree for the k nearest neighbors.

query_radius(coords[, r, in_radians, ...])

Queries the tree for all neighbors within a radius r.

Attributes

coordinates

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