def match_radec(ra1, dec1, ra2, dec2, radius_in_deg, notself=False,
nearest=False, indexlist=False):
'''
(m1,m2,d12) = match_radec(ra1,dec1, ra2,dec2, radius_in_deg)
Cross-matches numpy arrays of RA,Dec points.
Behaves like spherematch.pro of IDL
ra1,dec1 (and 2): RA,Dec in degrees of points to match.
Must be scalars or numpy arrays.
radius_in_deg: search radius in degrees.
notself: if True, avoids returning 'identity' matches;
ASSUMES that ra1,dec1 == ra2,dec2.
nearest: if True, returns only the nearest match in (ra2,dec2)
for each point in (ra1,dec1).
indexlist: returns a list of length len(ra1), containing None or a
list of ints of matched points in ra2,dec2. Returns this list.
Returns:
m1: indices into the "ra1,dec1" arrays of matching points.
Numpy array of ints.
m2: same, but for "ra2,dec2".
d12: distance, in degrees, between the matching points.
'''
# Convert to coordinates on the unit sphere
xyz1 = radectoxyz(ra1, dec1)
#if all(ra1 == ra2) and all(dec1 == dec2):
if ra1 is ra2 and dec1 is dec2:
xyz2 = xyz1
else:
xyz2 = radectoxyz(ra2, dec2)
r = deg2dist(radius_in_deg)
if nearest:
(inds,dists2) = _nearest_func(xyz2, xyz1, r, notself=notself)
I = np.flatnonzero(inds >= 0)
J = inds[I]
d = distsq2deg(dists2[I])
else:
X = match(xyz1, xyz2, r, notself=notself, indexlist=indexlist)
if indexlist:
return X
(inds,dists) = X
dist_in_deg = dist2deg(dists)
I,J = inds[:,0], inds[:,1]
d = dist_in_deg[:,0]
return (I, J, d)
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], distsq2deg(rtn[2]),) + rtn[3:]
else:
(inds,dists) = spherematch_c.match(kd1, kd2, radius, notself, permuted)
d = dist2deg(dists[:,0])
I,J = inds[:,0], inds[:,1]
rtn = (I,J,d)
return rtn
def match_radec(ra1, dec1, ra2, dec2, radius_in_deg, notself=False,
nearest=False):
'''
(m1,m2,d12) = match_radec(ra1,dec1, ra2,dec2, radius_in_deg)
Cross-matches numpy arrays of RA,Dec points.
Behaves like spherematch.pro of IDL
ra1,dec1 (and 2): RA,Dec in degrees of points to match.
Must be scalars or numpy arrays.
radius_in_deg: search radius in degrees.
notself: if True, avoids returning 'identity' matches;
ASSUMES that ra1,dec1 == ra2,dec2.
nearest: if True, returns only the nearest match in (ra2,dec2)
for each point in (ra1,dec1).
Returns:
m1: indices into the "ra1,dec1" arrays of matching points.
Numpy array of ints.
m2: same, but for "ra2,dec2".
d12: distance, in degrees, between the matching points.
'''
# Convert to coordinates on the unit sphere
xyz1 = radectoxyz(ra1, dec1)
#if all(ra1 == ra2) and all(dec1 == dec2):
if ra1 is ra2 and dec1 is dec2:
xyz2 = xyz1
else:
xyz2 = radectoxyz(ra2, dec2)
r = deg2dist(radius_in_deg)
if nearest:
(inds,dists2) = _nearest_func(xyz2, xyz1, r, notself=notself)
I = np.flatnonzero(inds >= 0)
J = inds[I]
d = distsq2deg(dists2[I])
else:
(inds,dists) = match(xyz1, xyz2, r, notself)
dist_in_deg = dist2deg(dists)
I,J = inds[:,0], inds[:,1]
d = dist_in_deg[:,0]
return (I, J, d)
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_radec(ra1, dec1, ra2, dec2, radius_in_deg, notself=False,
nearest=False, indexlist=False, count=False):
'''
(m1,m2,d12) = match_radec(ra1,dec1, ra2,dec2, radius_in_deg)
Cross-matches numpy arrays of RA,Dec points.
Behaves like spherematch.pro of IDL
ra1,dec1 (and 2): RA,Dec in degrees of points to match.
Must be scalars or numpy arrays.
radius_in_deg: search radius in degrees.
notself: if True, avoids returning 'identity' matches;
ASSUMES that ra1,dec1 == ra2,dec2.
nearest: if True, returns only the nearest match in (ra2,dec2)
for each point in (ra1,dec1).
indexlist: returns a list of length len(ra1), containing None or a
list of ints of matched points in ra2,dec2. Returns this list.
Returns:
m1: indices into the "ra1,dec1" arrays of matching points.
Numpy array of ints.
m2: same, but for "ra2,dec2".
d12: distance, in degrees, between the matching points.
'''
# Convert to coordinates on the unit sphere
xyz1 = radectoxyz(ra1, dec1)
#if all(ra1 == ra2) and all(dec1 == dec2):
if ra1 is ra2 and dec1 is dec2:
xyz2 = xyz1
else:
xyz2 = radectoxyz(ra2, dec2)
r = deg2dist(radius_in_deg)
extra = ()
if nearest:
X = _nearest_func(xyz2, xyz1, r, notself=notself, count=count)
if not count:
(inds,dists2) = X
I = np.flatnonzero(inds >= 0)
J = inds[I]
d = distsq2deg(dists2[I])
else:
#print 'X', X
#(inds,dists2,counts) = X
J,I,d,counts = X
extra = (counts,)
print 'I', I.shape, I.dtype
print 'J', J.shape, J.dtype
print 'counts', counts.shape, counts.dtype
else:
X = match(xyz1, xyz2, r, notself=notself, indexlist=indexlist)
if indexlist:
return X
(inds,dists) = X
dist_in_deg = dist2deg(dists)
I,J = inds[:,0], inds[:,1]
d = dist_in_deg[:,0]
return (I, J, d) + extra
def match_radec(ra1, dec1, ra2, dec2, radius_in_deg, notself=False,
nearest=False, indexlist=False, count=False):
'''
Cross-matches numpy arrays of RA,Dec points.
Behaves like spherematch.pro of IDL.
Parameters
----------
ra1, dec1, ra2, dec2 : numpy arrays, or scalars.
RA,Dec in degrees of points to match.
radius_in_deg : float
Search radius in degrees.
notself : boolean
If True, avoids returning 'identity' matches;
ASSUMES that ra1,dec1 == ra2,dec2.
nearest : boolean
If True, returns only the nearest match in *(ra2,dec2)*
for each point in *(ra1,dec1)*.
indexlist : boolean
If True, returns a list of length *len(ra1)*, containing *None*
or a list of ints of matched points in *ra2,dec2*.
Returns
-------
m1 : numpy array of integers
Indices into the *ra1,dec1* arrays of matching points.
m2 : numpy array of integers
Same, but for *ra2,dec2*.
d12 : numpy array, float
Distance, in degrees, between the matching points.
'''
# Convert to coordinates on the unit sphere
xyz1 = radectoxyz(ra1, dec1)
#if all(ra1 == ra2) and all(dec1 == dec2):
if ra1 is ra2 and dec1 is dec2:
xyz2 = xyz1
else:
xyz2 = radectoxyz(ra2, dec2)
r = deg2dist(radius_in_deg)
extra = ()
if nearest:
X = _nearest_func(xyz2, xyz1, r, notself=notself, count=count)
if not count:
(inds,dists2) = X
I = np.flatnonzero(inds >= 0)
J = inds[I]
d = distsq2deg(dists2[I])
else:
#print 'X', X
#(inds,dists2,counts) = X
J,I,d,counts = X
extra = (counts,)
print('I', I.shape, I.dtype)
print('J', J.shape, J.dtype)
print('counts', counts.shape, counts.dtype)
else:
X = match(xyz1, xyz2, r, notself=notself, indexlist=indexlist)
if indexlist:
return X
(inds,dists) = X
dist_in_deg = dist2deg(dists)
I,J = inds[:,0], inds[:,1]
d = dist_in_deg[:,0]
return (I, J, d) + extra
