Coverage for python/lsst/analysis/tools/tasks/associatedSourcesTractAnalysis.py: 43%
41 statements
« prev ^ index » next coverage.py v6.4.2, created at 2022-08-04 03:18 -0700
« prev ^ index » next coverage.py v6.4.2, created at 2022-08-04 03:18 -0700
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
23from lsst.pipe.base import connectionTypes as ct
25from .base import AnalysisBaseConfig, AnalysisBaseConnections, AnalysisPipelineTask
27# These need to be updated for this analysis context
28# from ..analysisPlots.analysisPlots import ShapeSizeFractionalDiffScatter
29# from ..analysisPlots.analysisPlots import Ap12PsfSkyPlot
30# from ..analysisMetrics.analysisMetrics import ShapeSizeFractionalMetric
33class AssociatedSourcesTractAnalysisConnections(
34 AnalysisBaseConnections,
35 dimensions=("skymap", "tract"),
36 defaultTemplates={
37 "inputName": "isolated_star_sources",
38 # "associatedSourcesInputName": "isolated_star_sources"},
39 },
40):
41 data = ct.Input(
42 doc="Visit based source table to load from the butler",
43 name="sourceTable_visit",
44 storageClass="DataFrame",
45 # deferLoad=True,
46 dimensions=("visit", "band"),
47 multiple=True,
48 )
50 associatedSources = ct.Input(
51 doc="Table of associated sources",
52 # name="{associatedSourcesInputName}",
53 name="{inputName}",
54 storageClass="DataFrame",
55 # deferLoad=True,
56 dimensions=("instrument", "skymap", "tract"),
57 )
59 skyMap = ct.Input(
60 doc="Input definition of geometry/bbox and projection/wcs for warped exposures",
61 name="skyMap",
62 storageClass="SkyMap",
63 dimensions=("skymap",),
64 )
67class AssociatedSourcesTractAnalysisConfig(
68 AnalysisBaseConfig, pipelineConnections=AssociatedSourcesTractAnalysisConnections
69):
70 def setDefaults(self):
71 super().setDefaults()
72 # set plots to run
73 # update for this analysis context
74 # self.plots.shapeSizeFractionalDiffScatter = \
75 # ShapeSizeFractionalDiffScatter()
76 # self.plots.Ap12PsfSkyPlot = Ap12PsfSkyPlot()
78 # set metrics to run
79 # update for this analysis context
80 # self.metrics.shapeSizeFractionalMetric = ShapeSizeFractionalMetric()
83class AssociatedSourcesTractAnalysisTask(AnalysisPipelineTask):
84 ConfigClass = AssociatedSourcesTractAnalysisConfig
85 _DefaultName = "associatedSourcesTractAnalysisTask"
87 def getBoxWcs(self, skymap, tract):
88 tractInfo = skymap.generateTract(tract)
89 wcs = tractInfo.getWcs()
90 tractBox = tractInfo.getBBox()
91 self.log.info("Running tract: %s", tract)
92 return tractBox, wcs
94 def prepareAssociatedSources(self, skymap, tract, sourceCatalogs, associatedSources):
95 """
96 This should be a standalone function rather than being associated with
97 this class.
98 """
100 import lsst.geom as geom
101 import numpy as np
102 import pandas as pd
104 # Keep only sources with associations
105 dataJoined = pd.concat(sourceCatalogs).merge(associatedSources, on="sourceId", how="inner")
106 dataJoined.set_index("sourceId", inplace=True)
108 # Determine which sources are contained in tract
109 ra = np.radians(dataJoined["coord_ra"].values)
110 dec = np.radians(dataJoined["coord_dec"].values)
111 box, wcs = self.getBoxWcs(skymap, tract)
112 box = geom.Box2D(box)
113 x, y = wcs.skyToPixelArray(ra, dec)
114 boxSelection = box.contains(x, y)
116 # Keep only the sources in groups that are fully contained within the
117 # tract
118 dataJoined["boxSelection"] = boxSelection
119 dataFiltered = dataJoined.groupby("obj_index").filter(lambda x: all(x["boxSelection"]))
120 dataFiltered.drop(columns="boxSelection", inplace=True)
122 return dataFiltered
124 def runQuantum(self, butlerQC, inputRefs, outputRefs):
125 inputs = butlerQC.get(inputRefs)
127 dataFiltered = self.prepareAssociatedSources(
128 inputs["skyMap"],
129 inputRefs.associatedSources.dataId.byName()["tract"],
130 inputs["data"],
131 inputs["associatedSources"],
132 )
134 kwargs = {"data": dataFiltered}
136 outputs = self.run(**kwargs)
137 butlerQC.put(outputs, outputRefs)