Coverage for python/lsst/faro/utils/coord_util.py: 27%
24 statements
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« prev ^ index » next coverage.py v7.3.2, created at 2023-11-23 12:46 +0000
1# This file is part of faro.
2#
3# Developed for the LSST Data Management System.
4# This product includes software developed by the LSST Project
5# (https://www.lsst.org).
6# See the COPYRIGHT file at the top-level directory of this distribution
7# for details of code ownership.
8#
9# This program is free software: you can redistribute it and/or modify
10# it under the terms of the GNU General Public License as published by
11# the Free Software Foundation, either version 3 of the License, or
12# (at your option) any later version.
13#
14# This program is distributed in the hope that it will be useful,
15# but WITHOUT ANY WARRANTY; without even the implied warranty of
16# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
17# GNU General Public License for more details.
18#
19# You should have received a copy of the GNU General Public License
20# along with this program. If not, see <https://www.gnu.org/licenses/>.
22import numpy as np
24import lsst.geom as geom
26__all__ = (
27 "averageRaFromCat",
28 "averageDecFromCat",
29 "averageRaDecFromCat",
30 "averageRaDec",
31 "sphDist",
32)
35def averageRaFromCat(cat):
36 """Compute the average right ascension from a catalog of measurements.
37 This function is used as an aggregate function to extract just RA
38 from a lsst.afw.table.MultiMatch final match catalog.
39 The actual computation involves both RA and Dec.
40 The intent is to use this for a set of measurements of the same source
41 but that's neither enforced nor required.
42 Parameters
43 ----------
44 cat : collection
45 Object for which ``cat["coord_ra"]`` and ``cat["coord_dec"]`` both
46 return radians.
47 Returns
48 -------
49 ra_mean : `float`
50 Mean RA in radians.
51 """
52 meanRa, _ = averageRaDecFromCat(cat)
53 return meanRa
56def averageDecFromCat(cat):
57 """Compute the average declination from a catalog of measurements.
58 This function is used as an aggregate function to extract just declination
59 from a lsst.afw.table.MultiMatch final match catalog.
60 The actual computation involves both RA and Dec.
61 The intent is to use this for a set of measurements of the same source
62 but that's neither enforced nor required.
63 Parameters
64 ----------
65 cat : collection
66 Object for which ``cat["coord_ra"]`` and ``cat["coord_dec"]`` both
67 return radians.
68 Returns
69 -------
70 dec_mean : `float`
71 Mean Dec in radians.
72 """
73 _, meanDec = averageRaDecFromCat(cat)
74 return meanDec
77def averageRaDecFromCat(cat):
78 """Calculate the average right ascension and declination from a catalog.
79 Convenience wrapper around averageRaDec
80 Parameters
81 ----------
82 cat : collection
83 Object for which ``cat["coord_ra"]`` and ``cat["coord_dec"]`` both
84 return radians.
85 Returns
86 -------
87 ra_mean : `float`
88 Mean RA in radians.
89 dec_mean : `float`
90 Mean Dec in radians.
91 """
92 return averageRaDec(cat["coord_ra"], cat["coord_dec"])
95def averageRaDec(ra, dec):
96 """Calculate average RA, Dec from input lists using spherical geometry.
97 Parameters
98 ----------
99 ra : `list` [`float`]
100 RA in [radians]
101 dec : `list` [`float`]
102 Dec in [radians]
103 Returns
104 -------
105 float, float
106 meanRa, meanDec -- Tuple of average RA, Dec [radians]
107 """
108 assert len(ra) == len(dec)
110 angleRa = [geom.Angle(r, geom.radians) for r in ra]
111 angleDec = [geom.Angle(d, geom.radians) for d in dec]
112 coords = [
113 geom.SpherePoint(ar, ad, geom.radians) for (ar, ad) in zip(angleRa, angleDec)
114 ]
116 meanRa, meanDec = geom.averageSpherePoint(coords)
118 return meanRa.asRadians(), meanDec.asRadians()
121def sphDist(ra_mean, dec_mean, ra, dec):
122 """Calculate distance on the surface of a unit sphere.
123 Parameters
124 ----------
125 ra_mean : `float`
126 Mean RA in radians.
127 dec_mean : `float`
128 Mean Dec in radians.
129 ra : `numpy.array` [`float`]
130 Array of RA in radians.
131 dec : `numpy.array` [`float`]
132 Array of Dec in radians.
133 Notes
134 -----
135 Uses the Haversine formula to preserve accuracy at small angles.
136 Law of cosines approach doesn't work well for the typically very small
137 differences that we're looking at here.
138 """
139 # Haversine
140 dra = ra - ra_mean
141 ddec = dec - dec_mean
142 a = np.square(np.sin(ddec / 2)) + np.cos(dec_mean) * np.cos(dec) * np.square(
143 np.sin(dra / 2)
144 )
145 dist = 2 * np.arcsin(np.sqrt(a))
147 # This is what the law of cosines would look like
148 # dist = np.arccos(np.sin(dec1)*np.sin(dec2) + np.cos(dec1)*np.cos(dec2)*np.cos(ra1 - ra2))
150 # This will also work, but must run separately for each element
151 # whereas the numpy version will run on either scalars or arrays:
152 # sp1 = geom.SpherePoint(ra1, dec1, geom.radians)
153 # sp2 = geom.SpherePoint(ra2, dec2, geom.radians)
154 # return sp1.separation(sp2).asRadians()
156 return dist