LSST Applications 30.0.7,g0e76e35be5+e8e946ae08,g19811a7679+138f7293ba,g199a45376c+5e234f8357,g1fd858c14a+2f48dbc4c4,g262e1987ae+fb36cac54d,g29ae962dfc+d9108a0941,g2c21b0017a+4f59a27f16,g31e44d4a5c+b0138be388,g33ac35c1f1+28b9f72785,g35bb328faa+b0138be388,g40c9b15c53+823ad735c1,g47891489e3+bcc48a0b46,g53246c7159+b0138be388,g64539dfbff+e8e946ae08,g67b6fd64d1+bcc48a0b46,g74acd417e5+422380537a,g76965917b2+a5ca99c4d9,g786e29fd12+796b79145d,g7aefaa3e3d+dc0c200193,g86b635cae8+734fe384f0,g87389fa792+d8b5378923,g89139ef638+bcc48a0b46,g8bbb235e95+3f4f7f9447,g8ea07a8fe4+78a4c88802,g9290983e33+ffdc83c6f7,g92c671f44c+e8e946ae08,gaa753fd333+03f406da14,gbf99507273+b0138be388,gc49b57b85e+8df26ee1f0,gca7fc764a6+bcc48a0b46,gd7ef33dd92+bcc48a0b46,gdab6d2f7ff+422380537a,ge1c02a5578+b0138be388,ge410e46f29+bcc48a0b46,ge80df9fc40+e6db5413d1,geaed405ab2+1de65a85c6,gf5dcc679e7+35a0ce2edd,gf5f1c85443+e8e946ae08
LSST Data Management Base Package
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_coaddInputsContinued.py
Go to the documentation of this file.
1# This file is part of afw.
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
19from lsst.utils import continueClass
20
21from ._imageLib import CoaddInputs
22
23import numpy as np
24
25__all__ = [] # import this module only for its side effects
26
27
28@continueClass
29class CoaddInputs: # noqa: F811
30
31 def subset_containing_ccds(self, point, wcs, includeValidPolygon=False):
32 """Return a view (shallow copy) of ExposureCatalog containing only the
33 subset of detectors that contain the given point.
34
35 Parameters
36 ----------
37 point : `~lsst.geom.Point2D`
38 Point in the coadd coordinate system.
39 wcs : `lsst.geom.SkyWcs`
40 WCS for the coadd coordinate system. This is ignored if the
41 CoaddInputs are made by stitching cell_coadds.
42 includeValidPolygon : `bool`, optional
43 If True, check that the point is within the validPolygon of those records which have one.
44
45 Returns
46 -------
47 subset : `~lsst.afw.table.ExposureCatalog`
48 ExposureCatalog containing only the relevant detector records.
49 """
50
51 ccds = self.ccds
52 # If the records have a WCS attached, we interpret that to mean that
53 # they come from a genuine afw exposure. If not, we interpret that to
54 # mean they come from cell_coadds. For the latter, the validPolygons
55 # are already in coadd coordinates and WCS lookup is not needed.
56 if len(ccds) == 0 or ccds[0].wcs is not None:
57 return ccds.subsetContaining(point, wcs, includeValidPolygon)
58 else:
59 cuts = np.array([record.validPolygon.contains(point) for record in ccds])
60 return ccds[cuts]
61
62 def subset_containing_visits(self, point, wcs, includeValidPolygon=False):
63 """Return a view (shallow copy) of ExposureCatalog containing only the
64 subset of visits that contain the given point.
65
66 Parameters
67 ----------
68 point : `~lsst.geom.Point2D`
69 Point in the coadd coordinate system.
70 wcs : `lsst.geom.SkyWcs`
71 WCS for the coadd coordinate system. This is ignored if the
72 CoaddInputs are made by stitching cell_coadds.
73 includeValidPolygon : `bool`, optional
74 If True, check that the point is within the validPolygon of those records which have one.
75
76 Returns
77 -------
78 subset : `~lsst.afw.table.ExposureCatalog`
79 ExposureCatalog containing only the relevant visit records.
80 """
81
82 visits = self.visits
83 if len(visits) == 0 or visits[0].wcs is not None:
84 return visits.subsetContaining(point, wcs, includeValidPolygon)
85 else:
86 ccd_cuts = np.array([record.validPolygon.contains(point) for record in self.ccds])
87 visit_cuts = np.isin(visits["visit"], self.ccds["visit"][ccd_cuts])
88 return visits[visit_cuts]
subset_containing_visits(self, point, wcs, includeValidPolygon=False)
subset_containing_ccds(self, point, wcs, includeValidPolygon=False)