Coverage for python / lsst / analysis / tools / tasks / photometricCatalogMatch.py: 23%
99 statements
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1# This file is part of analysis_tools.
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.
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22__all__ = ("PhotometricCatalogMatchConfig", "PhotometricCatalogMatchTask")
25import lsst.geom
26import lsst.pipe.base as pipeBase
27import numpy as np
28from astropy.table import Table
29from lsst.pipe.tasks.loadReferenceCatalog import LoadReferenceCatalogTask
31from ..actions.vector import StarSelector, VisitPlotFlagSelector
32from ..tasks.catalogMatch import CatalogMatchConfig, CatalogMatchConnections, CatalogMatchTask
35class PhotometricCatalogMatchConnections(
36 CatalogMatchConnections,
37 dimensions=("tract", "skymap"),
38 defaultTemplates={"targetCatalog": "objectTable_tract", "refCatalog": "ps1_pv3_3pi_20170110"},
39):
40 matchedCatalog = pipeBase.connectionTypes.Output(
41 doc="Catalog with matched target and reference objects with separations",
42 name="{targetCatalog}_{refCatalog}_match_photom",
43 storageClass="ArrowAstropy",
44 dimensions=("tract", "skymap"),
45 )
48class PhotometricCatalogMatchConfig(
49 CatalogMatchConfig,
50 pipelineConnections=PhotometricCatalogMatchConnections,
51):
52 def setDefaults(self):
53 super().setDefaults()
54 self.matchesRefCat = True
55 self.referenceCatalogLoader.doReferenceSelection = False
56 self.referenceCatalogLoader.doApplyColorTerms = True
59class PhotometricCatalogMatchTask(CatalogMatchTask):
60 """A wrapper task to provide the information that
61 is specific to the photometric reference catalog.
62 """
64 ConfigClass = PhotometricCatalogMatchConfig
65 _DefaultName = "analysisToolsPhotometricCatalogMatch"
67 def runQuantum(self, butlerQC, inputRefs, outputRefs):
68 """Run the matching to the photometric reference catalog.
70 Parameters
71 ----------
72 butlerQC : `lsst.pipe.base.QuantumContext`
73 inputRefs : `lsst.pipe.base.InputQuantizedConnection`
74 outputRefs : `lsst.pipe.base.OutputQuantizedConnection`
75 """
77 inputs = butlerQC.get(inputRefs)
78 bands = []
79 for filterName in self.config.filterNames:
80 bands.append(self.config.referenceCatalogLoader.refObjLoader.filterMap[filterName])
82 # For some reason the imsim filterMaps don't work the same way
83 # as the HSC ones do, this is a bit hacky but fixes this.
84 # This code makes the assumption that filterMap is a dict
85 # mapping observed filter names: band, but filterMap is
86 # currently defined as a mapping of:
87 # observed filter names -> reference catalog filter names.
88 # Therefore, when the reference catalog filter name is
89 # not the band name we need this work around.
90 # TODO: workaround for DM-46728
91 if bands[0].startswith("lsst") or "sim" in bands[0] or "smeared" in bands[0]:
92 bands = self.config.filterNames
93 elif bands[0].startswith("monster"):
94 # for the_monster_* the reference catalog filter name is
95 # "monster_{system}_{band}" the last character is the band
96 bands = [band[-1] for band in bands]
98 columns = self.prepColumns(bands)
99 table = inputs["catalog"].get(parameters={"columns": columns})
101 tract = butlerQC.quantum.dataId["tract"]
103 loaderTask = LoadReferenceCatalogTask(
104 config=self.config.referenceCatalogLoader,
105 dataIds=[ref.dataId for ref in inputRefs.refCat],
106 name=inputs["refCat"][0].ref.datasetType.name,
107 refCats=inputs["refCat"],
108 )
110 skymap = inputs.pop("skymap")
111 loadedRefCat = self._loadRefCat(loaderTask, skymap[tract])
112 outputs = self.run(targetCatalog=table, refCatalog=loadedRefCat, bands=bands)
114 butlerQC.put(outputs, outputRefs)
117class PhotometricCatalogMatchVisitConnections(
118 pipeBase.PipelineTaskConnections,
119 dimensions=("visit",),
120 defaultTemplates={"targetCatalog": "sourceTable_visit", "refCatalog": "ps1_pv3_3pi_20170110"},
121):
122 catalog = pipeBase.connectionTypes.Input(
123 doc="The visit-wide catalog to make plots from.",
124 storageClass="ArrowAstropy",
125 name="{targetCatalog}",
126 dimensions=("visit",),
127 deferLoad=True,
128 )
130 refCat = pipeBase.connectionTypes.PrerequisiteInput(
131 doc="The photometric reference catalog to match to.",
132 name="{refCatalog}",
133 storageClass="SimpleCatalog",
134 dimensions=("skypix",),
135 deferLoad=True,
136 multiple=True,
137 )
139 visitSummaryTable = pipeBase.connectionTypes.Input(
140 doc="A summary table of the ccds in the visit",
141 storageClass="ExposureCatalog",
142 name="finalVisitSummary",
143 dimensions=("visit",),
144 )
146 matchedCatalog = pipeBase.connectionTypes.Output(
147 doc="Catalog with matched target and reference objects with separations",
148 name="{targetCatalog}_{refCatalog}_match_photom",
149 storageClass="ArrowAstropy",
150 dimensions=("visit",),
151 )
154class PhotometricCatalogMatchVisitConfig(
155 PhotometricCatalogMatchConfig, pipelineConnections=PhotometricCatalogMatchVisitConnections
156):
157 def setDefaults(self):
158 super().setDefaults()
159 self.idColumn = "sourceId"
160 self.matchesRefCat = True
161 self.filterNames = []
162 self.extraPerBandColumns = []
163 self.patchColumn = ""
164 self.selectorBands = []
165 self.extraColumns = ["x", "y", "ap09Flux", "ap09FluxErr"]
166 self.selectorActions.flagSelector = VisitPlotFlagSelector
167 self.sourceSelectorActions.sourceSelector = StarSelector()
168 self.sourceSelectorActions.sourceSelector.vectorKey = "extendedness"
169 self.extraColumnSelectors.selector1.fluxType = "psfFlux"
170 self.extraColumnSelectors.selector2.vectorKey = "extendedness"
171 self.extraColumnSelectors.selector3.vectorKey = "extendedness"
172 self.extraColumnSelectors.selector4 = VisitPlotFlagSelector
175class PhotometricCatalogMatchVisitTask(PhotometricCatalogMatchTask):
176 """A wrapper task to provide the information that
177 is specific to the photometric reference catalog.
178 """
180 ConfigClass = PhotometricCatalogMatchVisitConfig
181 _DefaultName = "analysisToolsPhotometricCatalogMatchVisit"
183 def runQuantum(self, butlerQC, inputRefs, outputRefs):
184 """Run the matching to the photometric reference catalog.
186 Parameters
187 ----------
188 butlerQC : `lsst.pipe.base.QuantumContext`
189 inputRefs : `lsst.pipe.base.InputQuantizedConnection`
190 outputRefs : `lsst.pipe.base.OutputQuantizedConnection`
191 """
193 inputs = butlerQC.get(inputRefs)
194 physicalFilter = inputs["catalog"].dataId["physical_filter"]
196 # For some reason the imsim filterMaps don't work the same way as
197 # the HSC ones do, this is a bit hacky but fixes this
198 # This code makes the assumption that filterMap is a dict
199 # mapping observed filter names: band, but filterMap is
200 # currently defined as a mapping of:
201 # observed filter names -> reference catalog filter names.
202 # Therefore, when the reference catalog filter name is
203 # not the band name we need this work around.
204 # TODO: workaround for DM-46728
205 if "sim" in physicalFilter:
206 physicalFilter = physicalFilter[0]
207 bands = [physicalFilter]
208 elif self.config.referenceCatalogLoader.refObjLoader.filterMap[physicalFilter].startswith("monster"):
209 # filtermap gives the column name for the refcat
210 # which is monster_{system}_{band}
211 bands = [self.config.referenceCatalogLoader.refObjLoader.filterMap[physicalFilter][-1]]
212 else:
213 bands = [self.config.referenceCatalogLoader.refObjLoader.filterMap[physicalFilter]]
215 # No bands needed for visit tables
216 # but we do need them later for the matching
217 columns = [self.config.idColumn, "coord_ra", "coord_dec", "detector"]
218 columns.extend(self.config.extraColumns)
219 for selectorAction in [
220 self.config.selectorActions,
221 self.config.sourceSelectorActions,
222 self.config.extraColumnSelectors,
223 ]:
224 for selector in selectorAction:
225 selectorSchema = selector.getFormattedInputSchema()
226 columns += [s[0] for s in selectorSchema]
228 table = inputs["catalog"].get(parameters={"columns": columns})
230 loaderTask = LoadReferenceCatalogTask(
231 config=self.config.referenceCatalogLoader,
232 dataIds=[ref.dataId for ref in inputRefs.refCat],
233 name=inputs["refCat"][0].ref.datasetType.name,
234 refCats=inputs["refCat"],
235 )
237 visitSummaryTable = inputs.pop("visitSummaryTable")
238 loadedRefCat = self._loadRefCat(loaderTask, visitSummaryTable, physicalFilter)
239 outputs = self.run(targetCatalog=table, refCatalog=loadedRefCat, bands=bands)
241 # The matcher adds the band to the front of the columns
242 # but the visit plots aren't expecting it
243 cols = list(outputs.matchedCatalog.columns)
244 for col in cols:
245 if col[:2] == bands[0] + "_":
246 outputs.matchedCatalog.rename_column(col, col[2:])
248 butlerQC.put(outputs, outputRefs)
250 def _loadRefCat(self, loaderTask, visitSummaryTable, physicalFilter):
251 """Make a reference catalog with coordinates in degrees
253 Parameters
254 ----------
255 visitSummaryTable : `lsst.afw.table.ExposureCatalog`
256 The table of visit information
257 """
258 # Get convex hull around the detectors, then get its center and radius
259 corners = []
260 for visSum in visitSummaryTable:
261 for ra, dec in zip(visSum["raCorners"], visSum["decCorners"]):
262 # If the coordinates are nan then don't keep going
263 # because it crashes later
264 if not np.isfinite(ra) or not np.isfinite(dec):
265 raise pipeBase.NoWorkFound("Visit summary corners not finite")
266 corners.append(lsst.geom.SpherePoint(ra, dec, units=lsst.geom.degrees).getVector())
267 visitBoundingCircle = lsst.sphgeom.ConvexPolygon.convexHull(corners).getBoundingCircle()
268 center = lsst.geom.SpherePoint(visitBoundingCircle.getCenter())
269 radius = visitBoundingCircle.getOpeningAngle()
271 # Get the observation date of the visit
272 epoch = visSum.getVisitInfo().getDate().toAstropy()
274 # Load the reference catalog in the skyCircle of the detectors, then
275 # convert the coordinates to degrees and convert the catalog to a
276 # dataframe
278 loadedRefCat = loaderTask.getSkyCircleCatalog(center, radius, [physicalFilter], epoch=epoch)
280 return Table(loadedRefCat)