def __call__(self, ra, dec, spherematch=True, radius=0, contains=False):
T = self.tab
# HACK - magic 13x9 +1 arcmin.
if radius == 0:
radius = sqrt(14.**2 + 10.**2) / 2.
d2 = arcmin2distsq(radius)
if self.sdssxyz is None:
self.sdssxyz = radectoxyz(T.ra, T.dec)
if not spherematch:
rcfs = []
for r, d in broadcast(ra, dec):
xyz = radectoxyz(r, d)
dist2s = sum((xyz - self.sdssxyz)**2, axis=1)
I = flatnonzero(dist2s < d2)
rcfs.append(
zip(T[I].run, T[I].camcol, T[I].field, T[I].ra, T[I].dec))
else:
from astrometry.libkd import spherematch
from astrometry.libkd import spherematch_c
if self.kd is None:
self.kd = spherematch_c.kdtree_build(self.sdssxyz)
rds = array([x for x in broadcast(ra, dec)])
xyz = radectoxyz(rds[:, 0], rds[:, 1]).astype(double)
kd2 = spherematch_c.kdtree_build(xyz)
notself = False
inds, D = spherematch_c.match(self.kd, kd2, np.sqrt(d2), notself,
True)
if len(inds) == 0:
return []
spherematch_c.kdtree_free(kd2)
I = np.argsort(D[:, 0])
inds = inds[I]
rcfs = [[] for i in range(len(rds))]
cols = T.columns()
gotem = False
if contains:
if ('ramin' in cols and 'ramax' in cols and 'decmin' in cols
and 'decmax' in cols):
gotem = True
for j, i in inds:
(r, d) = rds[i]
if (r >= T.ramin[j] and r <= T.ramax[j]
and d >= T.decmin[j] and d <= T.decmax[j]):
rcfs[i].append((T.run[j], T.camcol[j], T.field[j],
T.ra[j], T.dec[j]))
#print '%i fields contain the first query RA,Dec' % len(rcfs[0])
else:
print 'you requested fields *containing* the query RA,Dec,'
print 'but the fields list file \"%s\" doesn\'t contain RAMIN,RAMAX,DECMIN, and DECMAX columns' % tablefn
if not gotem:
for j, i in inds:
rcfs[i].append(
(T.run[j], T.camcol[j], T.field[j], T.ra[j], T.dec[j]))
if isscalar(ra) and isscalar(dec):
return rcfs[0]
return rcfs
def __call__(self, ra, dec, spherematch=True, radius=0, contains=False):
T = self.tab
# HACK - magic 13x9 arcmin.
if radius == 0:
radius = sqrt(13.**2 + 9.**2)/2.
d2 = arcmin2distsq(radius)
if self.sdssxyz is None:
self.sdssxyz = radectoxyz(T.ra, T.dec)
if not spherematch:
rcfs = []
for r,d in broadcast(ra,dec):
xyz = radectoxyz(r,d)
dist2s = sum((xyz - self.sdssxyz)**2, axis=1)
I = flatnonzero(dist2s < d2)
rcfs.append(zip(T[I].run, T[I].camcol,
T[I].field, T[I].ra, T[I].dec))
else:
from astrometry.libkd import spherematch
from astrometry.libkd import spherematch_c
if self.kd is None:
self.kd = spherematch_c.kdtree_build(self.sdssxyz)
rds = array([x for x in broadcast(ra,dec)])
xyz = radectoxyz(rds[:,0], rds[:,1]).astype(double)
kd2 = spherematch_c.kdtree_build(xyz)
notself = False
inds,D = spherematch_c.match(self.kd, kd2, np.sqrt(d2), notself)
if len(inds) == 0:
return []
spherematch_c.kdtree_free(kd2)
I = np.argsort(D[:,0])
inds = inds[I]
rcfs = [[] for i in range(len(rds))]
cols = T.columns()
gotem = False
if contains:
if ('ramin' in cols and 'ramax' in cols and
'decmin' in cols and 'decmax' in cols):
gotem = True
for j,i in inds:
(r,d) = rds[i]
if (r >= T.ramin[j] and r <= T.ramax[j]
and d >= T.decmin[j] and d <= T.decmax[j]):
rcfs[i].append((T.run[j], T.camcol[j],
T.field[j], T.ra[j], T.dec[j]))
#print '%i fields contain the first query RA,Dec' % len(rcfs[0])
else:
print 'you requested fields *containing* the query RA,Dec,'
print 'but the fields list file \"%s\" doesn\'t contain RAMIN,RAMAX,DECMIN, and DECMAX columns' % tablefn
if not gotem:
for j,i in inds:
rcfs[i].append((T.run[j], T.camcol[j], T.field[j],
T.ra[j], T.dec[j]))
if isscalar(ra) and isscalar(dec):
return rcfs[0]
return rcfs
def trees_match(kd1, kd2, radius, nearest=False, notself=False,
permuted=True, count=False):
'''
Runs rangesearch or nearest-neighbour matching on given kdtrees.
'radius' is Euclidean distance.
If 'nearest'=True, returns the nearest neighbour of each point in "kd1";
ie, "I" will NOT contain duplicates, but "J" may.
If 'count'=True, also counts the number of objects within range
as well as returning the nearest neighbor of each point in "kd1";
the return value becomes I,J,d,counts , counts a numpy array of ints.
Returns (I, J, d), where
I are indices into kd1
J are indices into kd2
d are distances-squared
[counts is number of sources in range]
>>> import numpy as np
>>> X = np.array([[1, 2, 3, 6]]).T.astype(float)
>>> Y = np.array([[1, 4, 4]]).T.astype(float)
>>> kd1 = tree_build(X)
>>> kd2 = tree_build(Y)
>>> I,J,d = trees_match(kd1, kd2, 1.1, nearest=True)
>>> print I
[0 1 2]
>>> print J
[0 0 2]
>>> print d
[ 0. 60. 60.]
>>> I,J,d,count = trees_match(kd1, kd2, 1.1, nearest=True, count=True)
>>> print I
[0 1 2]
>>> print J
[0 0 2]
>>> print d
[ 0. 60. 60.]
>>> print count
[1 1 2]
'''
rtn = None
if nearest:
rtn = spherematch_c.nearest2(kd2, kd1, radius, notself, count)
# J,I,d,[count]
rtn = (rtn[1], rtn[0], np.sqrt(rtn[2])) + rtn[3:]
#distsq2deg(rtn[2]),
else:
(inds,dists) = spherematch_c.match(kd1, kd2, radius, notself, permuted)
#d = dist2deg(dists[:,0])
d = dists[:,0]
I,J = inds[:,0], inds[:,1]
rtn = (I,J,d)
return rtn
def trees_match(kd1, kd2, radius, nearest=False, notself=False,
permuted=True, count=False):
'''
Runs rangesearch or nearest-neighbour matching on given kdtrees.
'radius' is Euclidean distance.
If 'nearest'=True, returns the nearest neighbour of each point in "kd1";
ie, "I" will NOT contain duplicates, but "J" may.
If 'count'=True, also counts the number of objects within range
as well as returning the nearest neighbor of each point in "kd1";
the return value becomes I,J,d,counts , counts a numpy array of ints.
Returns (I, J, d), where
I are indices into kd1
J are indices into kd2
d are distances-squared
[counts is number of sources in range]
>>> import numpy as np
>>> X = np.array([[1, 2, 3, 6]]).T.astype(float)
>>> Y = np.array([[1, 4, 4]]).T.astype(float)
>>> kd1 = tree_build(X)
>>> kd2 = tree_build(Y)
>>> I,J,d = trees_match(kd1, kd2, 1.1, nearest=True)
>>> print I
[0 1 2]
>>> print J
[0 0 2]
>>> print d
[ 0. 60. 60.]
>>> I,J,d,count = trees_match(kd1, kd2, 1.1, nearest=True, count=True)
>>> print I
[0 1 2]
>>> print J
[0 0 2]
>>> print d
[ 0. 60. 60.]
>>> print count
[1 1 2]
'''
rtn = None
if nearest:
rtn = spherematch_c.nearest2(kd2, kd1, radius, notself, count)
# J,I,d,[count]
rtn = (rtn[1], rtn[0], np.sqrt(rtn[2])) + rtn[3:]
#distsq2deg(rtn[2]),
else:
(inds,dists) = spherematch_c.match(kd1, kd2, radius, notself, permuted)
#d = dist2deg(dists[:,0])
d = dists[:,0]
I,J = inds[:,0], inds[:,1]
rtn = (I,J,d)
return rtn
def trees_match(kd1, kd2, radius, nearest=False, notself=False,
permuted=True):
'''
Runs rangesearch or nearest-neighbour matching on given kdtrees.
'radius' is Euclidean distance.
If nearest=True, returns the nearest neighbour of each point in "kd1";
ie, "I" will NOT contain duplicates, but "J" may.
Returns (I, J, d), where
I are indices into kd1
J are indices into kd2
d are distances-squared
>>> import numpy as np
>>> X = np.array([[1, 2, 3, 6]]).T.astype(float)
>>> Y = np.array([[1, 4, 4]]).T.astype(float)
>>> kd1 = tree_build(X)
>>> kd2 = tree_build(Y)
>>> I,J,d = trees_match(kd1, kd2, 1.1, nearest=True)
>>> print I
[0 1 2]
>>> print J
[0 0 2]
>>> print d
[ 0. 60. 60.]
'''
if nearest:
J,I,d = spherematch_c.nearest2(kd2, kd1, radius, notself)
d = distsq2deg(d)
else:
(inds,dists) = spherematch_c.match(kd1, kd2, radius, notself, permuted)
d = dist2deg(dists[:,0])
I,J = inds[:,0], inds[:,1]
return I,J,d
def match(x1, x2, radius, notself=False):
'''
(indices,dists) = match(x1, x2, radius):
Given an N1 x D1 array x1,
and an N2 x D2 array x2,
and radius:
Returns the indices (Nx2 int array) and distances (Nx1 float
array) between points in "x1" and "x2" that are within "radius"
Euclidean distance of each other.
"x1" is N1xD and "x2" is N2xD. "x1" and "x2" can be the same
array. Dimensions D above 5-10 will probably not run faster than
naive.
Despite the name of this package, the arrays x1 and x2 need not be
celestial positions; in particular, there is no RA wrapping at 0,
and no special handling at the poles. If you want to match
celestial coordinates like RA,Dec, see the match_radec function.
The "indices" return value has a row for each match; the matched
points are:
x1[indices[:,0],:]
and
x2[indices[:,1],:]
This function doesn\'t know about spherical coordinates -- it just
searches for matches in n-dimensional space.
>>> from astrometry.util.starutil_numpy import *
>>> from astrometry.libkd import spherematch
# RA,Dec in degrees
>>> ra1 = array([ 0, 1, 2, 3, 4, 359,360])
>>> dec1 = array([-90,-89,-1, 0, 1, 89, 90])
# xyz: N x 3 array: unit vectors
>>> xyz1 = radectoxyz(ra1, dec1)
>>> ra2 = array([ 45, 1, 4, 4, 4, 0, 1])
>>> dec2 = array([-89, -88, -1, 0, 2, 89, 89])
>>> xyz2 = radectoxyz(ra2, dec2)
# The \'radius\' is now distance between points on the unit sphere --
# for small angles, this is ~ angular distance in radians. You can use
# the function:
>>> radius_in_deg = 2.
>>> r = sqrt(deg2distsq(radius_in_deg))
>>> (inds,dists) = spherematch.match(xyz1, xyz2, r)
# Now "inds" is an Mx2 array of the matching indices,
# and "dists" the distances between them:
# eg, sqrt(sum((xyz1[inds[:,0],:] - xyz2[inds[:,1],:])**2, axis=1)) = dists
>>> print inds
[[0 0]
[1 0]
[1 1]
[2 2]
[3 2]
[3 3]
[4 3]
[4 4]
[5 5]
[6 5]
[5 6]
[6 6]]
>>> print sqrt(sum((xyz1[inds[:,0],:] - xyz2[inds[:,1],:])**2, axis=1))
[ 0.01745307 0.01307557 0.01745307 0.0348995 0.02468143 0.01745307
0.01745307 0.01745307 0.0003046 0.01745307 0.00060917 0.01745307]
>>> print dists[:,0]
[ 0.01745307 0.01307557 0.01745307 0.0348995 0.02468143 0.01745307
0.01745307 0.01745307 0.0003046 0.01745307 0.00060917 0.01745307]
>>> print vstack((ra1[inds[:,0]], dec1[inds[:,0]], ra2[inds[:,1]], dec2[inds[:,1]])).T
[[ 0 -90 45 -89]
[ 1 -89 45 -89]
[ 1 -89 1 -88]
[ 2 -1 4 -1]
[ 3 0 4 -1]
[ 3 0 4 0]
[ 4 1 4 0]
[ 4 1 4 2]
[359 89 0 89]
[360 90 0 89]
[359 89 1 89]
[360 90 1 89]]
'''
(kd1,kd2) = _buildtrees(x1, x2)
#print 'spherematch.match: notself=', notself
(inds,dists) = spherematch_c.match(kd1, kd2, radius, notself)
_freetrees(kd1, kd2)
return (inds,dists)
def match(x1, x2, radius, notself=False, permuted=True, indexlist=False):
'''
(indices,dists) = match(x1, x2, radius):
Given an N1 x D1 array x1,
and an N2 x D2 array x2,
and radius:
Returns the indices (Nx2 int array) and distances (Nx1 float
array) between points in "x1" and "x2" that are within "radius"
Euclidean distance of each other.
"x1" is N1xD and "x2" is N2xD. "x1" and "x2" can be the same
array. Dimensions D above 5-10 will probably not run faster than
naive.
Despite the name of this package, the arrays x1 and x2 need not be
celestial positions; in particular, there is no RA wrapping at 0,
and no special handling at the poles. If you want to match
celestial coordinates like RA,Dec, see the match_radec function.
The "indices" return value has a row for each match; the matched
points are:
x1[indices[:,0],:]
and
x2[indices[:,1],:]
This function doesn\'t know about spherical coordinates -- it just
searches for matches in n-dimensional space.
>>> from astrometry.util.starutil_numpy import *
>>> from astrometry.libkd import spherematch
# RA,Dec in degrees
>>> ra1 = array([ 0, 1, 2, 3, 4, 359,360])
>>> dec1 = array([-90,-89,-1, 0, 1, 89, 90])
# xyz: N x 3 array: unit vectors
>>> xyz1 = radectoxyz(ra1, dec1)
>>> ra2 = array([ 45, 1, 4, 4, 4, 0, 1])
>>> dec2 = array([-89, -88, -1, 0, 2, 89, 89])
>>> xyz2 = radectoxyz(ra2, dec2)
# The \'radius\' is now distance between points on the unit sphere --
# for small angles, this is ~ angular distance in radians. You can use
# the function:
>>> radius_in_deg = 2.
>>> r = sqrt(deg2distsq(radius_in_deg))
>>> (inds,dists) = spherematch.match(xyz1, xyz2, r)
# Now "inds" is an Mx2 array of the matching indices,
# and "dists" the distances between them:
# eg, sqrt(sum((xyz1[inds[:,0],:] - xyz2[inds[:,1],:])**2, axis=1)) = dists
>>> print inds
[[0 0]
[1 0]
[1 1]
[2 2]
[3 2]
[3 3]
[4 3]
[4 4]
[5 5]
[6 5]
[5 6]
[6 6]]
>>> print sqrt(sum((xyz1[inds[:,0],:] - xyz2[inds[:,1],:])**2, axis=1))
[ 0.01745307 0.01307557 0.01745307 0.0348995 0.02468143 0.01745307
0.01745307 0.01745307 0.0003046 0.01745307 0.00060917 0.01745307]
>>> print dists[:,0]
[ 0.01745307 0.01307557 0.01745307 0.0348995 0.02468143 0.01745307
0.01745307 0.01745307 0.0003046 0.01745307 0.00060917 0.01745307]
>>> print vstack((ra1[inds[:,0]], dec1[inds[:,0]], ra2[inds[:,1]], dec2[inds[:,1]])).T
[[ 0 -90 45 -89]
[ 1 -89 45 -89]
[ 1 -89 1 -88]
[ 2 -1 4 -1]
[ 3 0 4 -1]
[ 3 0 4 0]
[ 4 1 4 0]
[ 4 1 4 2]
[359 89 0 89]
[360 90 0 89]
[359 89 1 89]
[360 90 1 89]]
'''
(kd1,kd2) = _buildtrees(x1, x2)
if indexlist:
inds = spherematch_c.match2(kd1, kd2, radius, notself, permuted)
else:
(inds,dists) = spherematch_c.match(kd1, kd2, radius, notself, permuted)
_freetrees(kd1, kd2)
if indexlist:
return inds
return (inds,dists)
def match(x1, x2, radius, notself=False, permuted=True, indexlist=False):
'''
::
(indices,dists) = match(x1, x2, radius):
Or::
inds = match(x1, x2, radius, indexlist=True):
Returns the indices (Nx2 int array) and distances (Nx1 float
array) between points in *x1* and *x2* that are within *radius*
Euclidean distance of each other.
*x1* is N1xD and *x2* is N2xD. *x1* and *x2* can be the same
array. Dimensions D above 5-10 will probably not run faster than
naive.
Despite the name of this package, the arrays x1 and x2 need not be
celestial positions; in particular, there is no RA wrapping at 0,
and no special handling at the poles. If you want to match
celestial coordinates like RA,Dec, see the match_radec function.
If *indexlist* is True, the return value is a python list with one
element per data point in the first tree; that element is a python
list containing the indices of points matched in the second tree.
The *indices* return value has a row for each match; the matched
points are:
x1[indices[:,0],:]
and
x2[indices[:,1],:]
This function doesn\'t know about spherical coordinates -- it just
searches for matches in n-dimensional space.
>>> from astrometry.util.starutil_numpy import *
>>> from astrometry.libkd import spherematch
>>> # RA,Dec in degrees
>>> ra1 = array([ 0, 1, 2, 3, 4, 359,360])
>>> dec1 = array([-90,-89,-1, 0, 1, 89, 90])
>>> # xyz: N x 3 array: unit vectors
>>> xyz1 = radectoxyz(ra1, dec1)
>>> ra2 = array([ 45, 1, 4, 4, 4, 0, 1])
>>> dec2 = array([-89, -88, -1, 0, 2, 89, 89])
>>> xyz2 = radectoxyz(ra2, dec2)
>>> # The \'radius\' is now distance between points on the unit sphere --
>>> # for small angles, this is ~ angular distance in radians. You can use
>>> # the function:
>>> radius_in_deg = 2.
>>> r = sqrt(deg2distsq(radius_in_deg))
>>> (inds,dists) = spherematch.match(xyz1, xyz2, r)
>>> # Now *inds* is an Mx2 array of the matching indices,
>>> # and *dists* the distances between them:
>>> # eg, sqrt(sum((xyz1[inds[:,0],:] - xyz2[inds[:,1],:])**2, axis=1)) = dists
>>> print inds
[[0 0]
[1 0]
[1 1]
[2 2]
[3 2]
[3 3]
[4 3]
[4 4]
[5 5]
[6 5]
[5 6]
[6 6]]
>>> print sqrt(sum((xyz1[inds[:,0],:] - xyz2[inds[:,1],:])**2, axis=1))
[ 0.01745307 0.01307557 0.01745307 0.0348995 0.02468143 0.01745307
0.01745307 0.01745307 0.0003046 0.01745307 0.00060917 0.01745307]
>>> print dists[:,0]
[ 0.01745307 0.01307557 0.01745307 0.0348995 0.02468143 0.01745307
0.01745307 0.01745307 0.0003046 0.01745307 0.00060917 0.01745307]
>>> print vstack((ra1[inds[:,0]], dec1[inds[:,0]], ra2[inds[:,1]], dec2[inds[:,1]])).T
[[ 0 -90 45 -89]
[ 1 -89 45 -89]
[ 1 -89 1 -88]
[ 2 -1 4 -1]
[ 3 0 4 -1]
[ 3 0 4 0]
[ 4 1 4 0]
[ 4 1 4 2]
[359 89 0 89]
[360 90 0 89]
[359 89 1 89]
[360 90 1 89]]
Parameters
----------
x1 : numpy array, float, shape N1 x D
First array of points to match
x2 : numpy array, float, shape N2 x D
Second array of points to match
radius : float
Scalar Euclidean distance to match
Returns
-------
indices : numpy array, integers, shape M x 2, for M matches
The array of matching indices; *indices[:,0]* are indices in *x1*,
*indices[:,1]* are indices in *x2*.
dists : numpy array, floats, length M, for M matches
The distances between matched points.
If *indexlist* is *True*:
indices : list of ints of integers
The list of matching indices. One list element per *x1* element,
containing a list of matching indices in *x2*.
'''
(kd1,kd2) = _buildtrees(x1, x2)
if indexlist:
inds = spherematch_c.match2(kd1, kd2, radius, notself, permuted)
else:
(inds,dists) = spherematch_c.match(kd1, kd2, radius, notself, permuted)
if indexlist:
return inds
return (inds,dists)