Coverage for python/lsst/analysis/tools/tasks/associatedSourcesTractAnalysis.py: 37%
55 statements
« prev ^ index » next coverage.py v6.5.0, created at 2023-01-14 04:04 -0800
« prev ^ index » next coverage.py v6.5.0, created at 2023-01-14 04:04 -0800
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
23import numpy as np
24import pandas as pd
25from lsst.geom import Box2D
26from lsst.pipe.base import connectionTypes as ct
28from .base import AnalysisBaseConfig, AnalysisBaseConnections, AnalysisPipelineTask
31class AssociatedSourcesTractAnalysisConnections(
32 AnalysisBaseConnections,
33 dimensions=("skymap", "tract"),
34 defaultTemplates={
35 "outputName": "isolated_star_sources",
36 "associatedSourcesInputName": "isolated_star_sources",
37 },
38):
39 sourceCatalogs = ct.Input(
40 doc="Visit based source table to load from the butler",
41 name="sourceTable_visit",
42 storageClass="DataFrame",
43 deferLoad=True,
44 dimensions=("visit", "band"),
45 multiple=True,
46 )
48 associatedSources = ct.Input(
49 doc="Table of associated sources",
50 name="{associatedSourcesInputName}",
51 storageClass="DataFrame",
52 deferLoad=True,
53 dimensions=("instrument", "skymap", "tract"),
54 )
56 skyMap = ct.Input(
57 doc="Input definition of geometry/bbox and projection/wcs for warped exposures",
58 name="skyMap",
59 storageClass="SkyMap",
60 dimensions=("skymap",),
61 )
64class AssociatedSourcesTractAnalysisConfig(
65 AnalysisBaseConfig, pipelineConnections=AssociatedSourcesTractAnalysisConnections
66):
67 def setDefaults(self):
68 super().setDefaults()
71class AssociatedSourcesTractAnalysisTask(AnalysisPipelineTask):
72 ConfigClass = AssociatedSourcesTractAnalysisConfig
73 _DefaultName = "associatedSourcesTractAnalysisTask"
75 @staticmethod
76 def getBoxWcs(skymap, tract):
77 """Get box that defines tract boundaries."""
78 tractInfo = skymap.generateTract(tract)
79 wcs = tractInfo.getWcs()
80 tractBox = tractInfo.getBBox()
81 return tractBox, wcs
83 @classmethod
84 def callback(cls, inputs, dataId):
85 """Callback function to be used with reconstructor."""
86 return cls.prepareAssociatedSources(
87 inputs["skyMap"],
88 dataId["tract"],
89 inputs["sourceCatalogs"],
90 inputs["associatedSources"],
91 )
93 @classmethod
94 def prepareAssociatedSources(cls, skymap, tract, sourceCatalogs, associatedSources):
95 """Concatenate source catalogs and join on associated object index."""
97 # Keep only sources with associations
98 dataJoined = pd.concat(sourceCatalogs).merge(associatedSources, on="sourceId", how="inner")
99 dataJoined.set_index("sourceId", inplace=True)
101 # Determine which sources are contained in tract
102 ra = np.radians(dataJoined["coord_ra"].values)
103 dec = np.radians(dataJoined["coord_dec"].values)
104 box, wcs = cls.getBoxWcs(skymap, tract)
105 box = Box2D(box)
106 x, y = wcs.skyToPixelArray(ra, dec)
107 boxSelection = box.contains(x, y)
109 # Keep only the sources in groups that are fully contained within the
110 # tract
111 dataJoined["boxSelection"] = boxSelection
112 dataFiltered = dataJoined.groupby("obj_index").filter(lambda x: all(x["boxSelection"]))
113 dataFiltered.drop(columns="boxSelection", inplace=True)
115 return dataFiltered
117 def runQuantum(self, butlerQC, inputRefs, outputRefs):
118 inputs = butlerQC.get(inputRefs)
120 # Load specified columns from source catalogs
121 names = self.collectInputNames()
122 names |= {"sourceId", "coord_ra", "coord_dec"}
123 names.remove("obj_index")
124 sourceCatalogs = []
125 for handle in inputs["sourceCatalogs"]:
126 sourceCatalogs.append(self.loadData(handle, names))
127 inputs["sourceCatalogs"] = sourceCatalogs
129 dataId = butlerQC.quantum.dataId
130 plotInfo = self.parsePlotInfo(inputs, dataId, connectionName="associatedSources")
132 # TODO: make key used for object index configurable
133 inputs["associatedSources"] = self.loadData(inputs["associatedSources"], ["obj_index", "sourceId"])
135 data = self.callback(inputs, dataId)
137 kwargs = {"data": data, "plotInfo": plotInfo, "skymap": inputs["skyMap"]}
138 outputs = self.run(**kwargs)
139 butlerQC.put(outputs, outputRefs)