KdTreeCrossmatch

class KdTreeCrossmatch(n_neighbors: int = 1, radius_arcsec: float = 1.0, min_radius_arcsec: float = 0.0)

Nearest neighbor crossmatch using a 3D k-D tree

Methods

crossmatch(crossmatch_args, how, suffixes[, ...])	Perform a crossmatch.
crossmatch_nested(crossmatch_args, ...)	Perform a crossmatch and store results in nested column.
perform_crossmatch(crossmatch_args)	Perform a cross-match between the data from two HEALPix pixels
validate(left, right)	Validate the arguments for the crossmatch

__init__(n_neighbors: int = 1, radius_arcsec: float = 1.0, min_radius_arcsec: float = 0.0)

Initialize the KDTree crossmatch algorithm.

Parameters:

n_neighborsint

The number of neighbors to find within each point.

radius_arcsecfloat, default 1.0

The threshold distance in arcseconds beyond which neighbors are not added.

min_radius_arcsecfloat, default 0.0

The threshold distance in arcseconds beyond which neighbors are added.

Methods

__init__([n_neighbors, radius_arcsec, ...])	Initialize the KDTree crossmatch algorithm.
crossmatch(crossmatch_args, how, suffixes[, ...])	Perform a crossmatch.
crossmatch_nested(crossmatch_args, ...)	Perform a crossmatch and store results in nested column.
perform_crossmatch(crossmatch_args)	Perform a cross-match between the data from two HEALPix pixels
validate(left, right)	Validate the arguments for the crossmatch

Attributes

extra_columns

The metadata for the columns generated by the crossmatch algorithm