示例#1
0
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)
示例#2
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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
示例#3
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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)
示例#4
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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
示例#5
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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
示例#6
0
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