Coverage for python/lsst/analysis/tools/tasks/sourceObjectTableAnalysis.py: 38%
47 statements
« prev ^ index » next coverage.py v7.5.0, created at 2024-05-01 12:36 +0000
« prev ^ index » next coverage.py v7.5.0, created at 2024-05-01 12:36 +0000
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.
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/>.
21from __future__ import annotations
23__all__ = ("SourceObjectTableAnalysisConfig", "SourceObjectTableAnalysisTask")
25import lsst.pex.config as pexConfig
26import numpy as np
27import pandas as pd
28from astropy.table import vstack
29from lsst.pipe.base import connectionTypes as ct
30from smatch import Matcher
32from ..interfaces import AnalysisBaseConfig, AnalysisBaseConnections, AnalysisPipelineTask
35class SourceObjectTableAnalysisConnections(
36 AnalysisBaseConnections,
37 dimensions=("visit",),
38 defaultTemplates={
39 "inputName": "sourceTable_visit",
40 "inputCoaddName": "deep",
41 "associatedSourcesInputName": "isolated_star_sources",
42 "outputName": "sourceObjectTable",
43 },
44):
45 data = ct.Input(
46 doc="Visit based source table to load from the butler",
47 name="sourceTable_visit",
48 storageClass="ArrowAstropy",
49 dimensions=("visit", "band"),
50 deferLoad=True,
51 )
53 associatedSources = ct.Input(
54 doc="Table of associated sources",
55 name="{associatedSourcesInputName}",
56 storageClass="ArrowAstropy",
57 multiple=True,
58 deferLoad=True,
59 dimensions=("instrument", "skymap", "tract"),
60 )
62 refCat = ct.Input(
63 doc="Catalog of positions to use as reference.",
64 name="objectTable",
65 storageClass="DataFrame",
66 dimensions=["skymap", "tract", "patch"],
67 multiple=True,
68 deferLoad=True,
69 )
72class SourceObjectTableAnalysisConfig(
73 AnalysisBaseConfig, pipelineConnections=SourceObjectTableAnalysisConnections
74):
75 ra_column = pexConfig.Field(
76 doc="Name of column in refCat to use for right ascension.",
77 dtype=str,
78 default="r_ra",
79 )
80 dec_column = pexConfig.Field(
81 doc="Name of column in refCat to use for declination.",
82 dtype=str,
83 default="r_dec",
84 )
87class SourceObjectTableAnalysisTask(AnalysisPipelineTask):
88 ConfigClass = SourceObjectTableAnalysisConfig
89 _DefaultName = "sourceTableVisitAnalysis"
91 def runQuantum(self, butlerQC, inputRefs, outputRefs):
92 inputs = butlerQC.get(inputRefs)
94 # Get isolated sources:
95 visit = inputs["data"].dataId["visit"]
96 band = inputs["data"].dataId["band"]
97 names = self.collectInputNames()
98 names -= {self.config.ra_column, self.config.dec_column}
99 data = inputs["data"].get(parameters={"columns": names})
101 dataId = butlerQC.quantum.dataId
102 plotInfo = self.parsePlotInfo(inputs, dataId)
104 isolatedSources = []
105 for associatedSourcesRef in inputs["associatedSources"]:
106 associatedSources = associatedSourcesRef.get(parameters={"columns": ["visit", "source_row"]})
107 visit_sources = associatedSources[associatedSources["visit"] == visit]
108 isolatedSources.append(data[visit_sources["source_row"]])
109 isolatedSources = vstack(isolatedSources)
111 # Get objects:
112 allRefCats = []
113 for refCatRef in inputs["refCat"]:
114 refCat = refCatRef.get(
115 parameters={"columns": ["detect_isPrimary", self.config.ra_column, self.config.dec_column]}
116 )
117 goodInds = (
118 refCat["detect_isPrimary"]
119 & np.isfinite(refCat[self.config.ra_column])
120 & np.isfinite(refCat[self.config.dec_column])
121 )
122 allRefCats.append(refCat[goodInds])
124 refCat = pd.concat(allRefCats)
126 with Matcher(isolatedSources["coord_ra"], isolatedSources["coord_dec"]) as m:
127 idx, isolatedMatchIndices, refMatchIndices, dists = m.query_radius(
128 refCat[self.config.ra_column].values,
129 refCat[self.config.dec_column].values,
130 1 / 3600.0,
131 return_indices=True,
132 )
134 matchRef = refCat.iloc[refMatchIndices]
135 matchIS = isolatedSources[isolatedMatchIndices].to_pandas()
137 allCat = pd.concat([matchRef.reset_index(), matchIS.reset_index()], axis=1)
138 outputs = self.run(data=allCat, bands=band, plotInfo=plotInfo)
139 butlerQC.put(outputs, outputRefs)