# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
This module contains functions for matching coordinate catalogs.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import numpy as np
import six
__all__ = ['match_coordinates_3d', 'match_coordinates_sky']
[docs]def match_coordinates_3d(matchcoord, catalogcoord, nthneighbor=1, storekdtree=True):
"""
Finds the nearest 3-dimensional matches of a coordinate or coordinates in
a set of catalog coordinates.
This finds the 3-dimensional closest neighbor, which is only different
from the on-sky distance if `distance` is set in either `matchcoord`
or `catalogcoord`.
Parameters
----------
matchcoord : `~astropy.coordinates.SphericalCoordinatesBase`
The coordinate(s) to match to the catalog.
catalogcoord : `~astropy.coordinates.SphericalCoordinatesBase`
The base catalog in which to search for matches. Typically this will
be a coordinate object that is an array (i.e.,
``catalogcoord.isscalar == False``)
nthneighbor : int, optional
Which closest neighbor to search for. Typically ``1`` is desired here,
as that is correct for matching one set of coordinates to another.
The next likely use case is ``2``, for matching a coordinate catalog
against *itself* (``1`` is inappropriate because each point will find
itself as the closest match).
storekdtree : bool or str, optional
If True or a string, will store the KD-Tree used for the computation
in the `catalogcoord`. This dramatically speeds up subsequent calls
with the same catalog. If a str, it specifies the attribute name for
`catalogcoord` that should store the KD-tree.
Returns
-------
idx : integer array
Indecies into `catalogcoord` to get the matched points for each
`matchcoord`. Shape matches `matchcoord`.
sep2d : `~astropy.units.quantity.Angle`
The on-sky separation between the closest match for each `matchcoord` and
the `matchcoord`. Shape matches `matchcoord`.
dist3d : `~astropy.units.quantity.Quantity`
The 3D distance between the closest match for each `matchcoord` and
the `matchcoord`. Shape matches `matchcoord`.
Notes
-----
This function requires `scipy` to be installed or it will fail.
"""
from warnings import warn
#without scipy this will immediately fail
from scipy import spatial
try:
KDTree = spatial.cKDTree
except:
warn('C-base KD tree not found, falling back on (much slower) '
'python implementation')
KDTree = spatial.KDTree
if storekdtree is True:
storekdtree = '_kdtree'
# figure out where any cached KDTree might be
if isinstance(storekdtree, six.string_types):
kdt = getattr(catalogcoord, storekdtree, None)
if kdt is not None and not isinstance(kdt, KDTree):
raise ValueError('Invalid `storekdtree` string:' + storekdtree)
elif isinstance(storekdtree, KDTree):
kdt = storekdtree
storekdtree = None
elif not storekdtree:
kdt = None
else:
raise ValueError('Invalid `storekdtree` argument:' +
str(storekdtree))
if kdt is None:
#need to build the cartesian KD-tree for the catalog
cart = catalogcoord.cartesian
flatxyz = cart.reshape((3, np.prod(cart.shape) // 3))
kdt = KDTree(flatxyz.value.T)
#make sure coordinate systems match
matchcoord = matchcoord.transform_to(catalogcoord.__class__)
#make sure units match
catunit = catalogcoord.cartesian.unit
cart = matchcoord.cartesian.to(catunit)
flatxyz = cart.reshape((3, np.prod(cart.shape) // 3))
dist, idx = kdt.query(flatxyz.T, nthneighbor)
if nthneighbor > 1: # query gives 1D arrays if k=1, 2D arrays otherwise
dist = dist[:, -1]
idx = idx[:, -1]
if storekdtree:
#cache the kdtree
setattr(catalogcoord, storekdtree, kdt)
sep2d = catalogcoord[idx].separation(matchcoord)
return idx.reshape(cart.shape[1:]), sep2d, dist.reshape(cart.shape[1:]) * catunit
[docs]def match_coordinates_sky(matchcoord, catalogcoord, nthneighbor=1, storekdtree=True):
"""
Finds the nearest on-sky matches of a coordinate or coordinates in
a set of catalog coordinates.
This finds the on-sky closest neighbor, which is only different from the
3-dimensional match if `distance` is set in either `matchcoord`
or `catalogcoord`.
Parameters
----------
matchcoord : `~astropy.coordinates.SphericalCoordinatesBase`
The coordinate(s) to match to the catalog.
catalogcoord : `~astropy.coordinates.SphericalCoordinatesBase`
The base catalog in which to search for matches. Typically this will
be a coordinate object that is an array (i.e.,
``catalogcoord.isscalar == False``)
nthneighbor : int, optional
Which closest neighbor to search for. Typically ``1`` is desired here,
as that is correct for matching one set of coordinates to another.
The next likely use case is ``2``, for matching a coordinate catalog
against *itself* (``1`` is inappropriate because each point will find
itself as the closest match).
storekdtree : bool or str, optional
If True or a string, will store the KD-Tree used for the computation
in the `catalogcoord`. This dramatically speeds up subsequent calls
with the same catalog. If a str, it specifies the attribute name for
`catalogcoord` that should store the KD-tree.
Returns
-------
idx : integer array
Indecies into `catalogcoord` to get the matched points for each
`matchcoord`. Shape matches `matchcoord`.
sep2d : `~astropy.units.quantity.Angle`
The on-sky separation between the closest match for each `matchcoord` and
the `matchcoord`. Shape matches `matchcoord`.
dist3d : `~astropy.units.quantity.Quantity`
The 3D distance between the closest match for each `matchcoord` and
the `matchcoord`. Shape matches `matchcoord`.
Notes
-----
This function requires `scipy` to be installed or it will fail.
"""
dcoo = matchcoord._distance
cpcoo = matchcoord._cartpoint
dcat = catalogcoord._distance
cpcat = catalogcoord._cartpoint
try:
matchcoord._distance = matchcoord._cartpoint = None
catalogcoord._distance = catalogcoord._cartpoint = None
return match_coordinates_3d(matchcoord, catalogcoord, nthneighbor, storekdtree)
finally:
matchcoord._distance = dcoo
matchcoord._cartpoint = cpcoo
catalogcoord._distance = dcat
catalogcoord._cartpoint = cpcat