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LSST Data Management Base Package
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sourceMatchStatistics.py
Go to the documentation of this file.
2# LSST Data Management System
3# Copyright 2008, 2009, 2010 LSST Corporation.
4#
5# This product includes software developed by the
6# LSST Project (http://www.lsst.org/).
7#
8# This program is free software: you can redistribute it and/or modify
9# it under the terms of the GNU General Public License as published by
10# the Free Software Foundation, either version 3 of the License, or
11# (at your option) any later version.
12#
13# This program is distributed in the hope that it will be useful,
14# but WITHOUT ANY WARRANTY; without even the implied warranty of
15# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
16# GNU General Public License for more details.
17#
18# You should have received a copy of the LSST License Statement and
19# the GNU General Public License along with this program. If not,
20# see <http://www.lsstcorp.org/LegalNotices/>.
21#
22
23__all__ = ["sourceMatchStatistics"]
24
25import numpy as np
26
27
28def sourceMatchStatistics(matchList, log=None):
29 """Compute statistics on the accuracy of a wcs solution, using a
30 precomputed list of matches between an image and a catalog.
31
32 Parameters
33 ----------
34 matchList : `lsst.afw.detection.SourceMatch`
35 List of matches between sources and references to compute statistics
36 on.
37
38 Returns
39 -------
40 values : `dict
41 Value dictionary with fields:
42
43 - diffInPixels_mean : Average distance between image and
44 catalog position in pixels (`float`).
45 - diffInPixels_std : Root mean square of distribution of distances
46 (`float`).
47 - diffInPixels_Q25 : 25% quantile boundary of the match dist
48 distribution (`float`).
49 - diffInPixels_Q50 : 50% quantile boundary of the match dist
50 distribution (`float`).
51 - diffInPixels_Q75 : 75% quantile boundary of the match
52 dist distribution (`float`).
53 """
54
55 size = len(matchList)
56 if size == 0:
57 raise ValueError("matchList contains no elements")
58
59 dist = np.zeros(size)
60 i = 0
61 for match in matchList:
62 catObj = match.first
63 srcObj = match.second
64
65 cx = catObj.getXAstrom()
66 cy = catObj.getYAstrom()
67
68 sx = srcObj.getXAstrom()
69 sy = srcObj.getYAstrom()
70
71 dist[i] = np.hypot(cx-sx, cy-sy)
72 i = i+1
73
74 dist.sort()
75
76 quartiles = []
77 for f in (0.25, 0.50, 0.75):
78 i = int(f*size + 0.5)
79 if i >= size:
80 i = size - 1
81 quartiles.append(dist[i])
82
83 values = {}
84 values['diffInPixels_Q25'] = quartiles[0]
85 values['diffInPixels_Q50'] = quartiles[1]
86 values['diffInPixels_Q75'] = quartiles[2]
87 values['diffInPixels_mean'] = dist.mean()
88 values['diffInPixels_std'] = dist.std()
89
90 return values