Coverage for python/lsst/ap/pipe/metrics.py : 47%

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1# This file is part of ap_pipe.
2#
3# Developed for the LSST Data Management System.
4# This product includes software developed by the LSST Project
5# (http://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 <http://www.gnu.org/licenses/>.
21#
23"""Metrics for ap_pipe tasks.
24"""
26__all__ = [
27 "ApFakesCompletenessMetricTask", "ApFakesCompletenessMetricConfig",
28 "ApFakesCountMetricTask", "ApFakesCountMetricConfig"
29]
31import astropy.units as u
32import numpy as np
33import traceback
35import lsst.pex.config as pexConfig
36from lsst.pipe.base import Struct
37import lsst.pipe.base.connectionTypes as connTypes
38from lsst.pipe.tasks.insertFakes import InsertFakesConfig
39from lsst.verify import Measurement
40from lsst.verify.tasks import MetricTask, MetricComputationError
43class ApFakesCompletenessMetricConnections(
44 MetricTask.ConfigClass.ConnectionsClass,
45 dimensions={"instrument", "visit", "detector", "band"},
46 defaultTemplates={"coaddName": "deep",
47 "fakesType": "fakes_",
48 "package": "ap_pipe",
49 "metric": "apFakesCompleteness"}):
50 """ApFakesCompleteness connections.
51 """
52 matchedFakes = connTypes.Input(
53 doc="Fakes matched to their detections in the difference image.",
54 name="{fakesType}{coaddName}Diff_matchDiaSrc",
55 storageClass="DataFrame",
56 dimensions=("instrument", "visit", "detector"),
57 )
60# Inherits from InsertFakesConfig to preserve column names in the fakes
61# catalog.
62class ApFakesCompletenessMetricConfig(
63 MetricTask.ConfigClass,
64 InsertFakesConfig,
65 pipelineConnections=ApFakesCompletenessMetricConnections):
66 """ApFakesCompleteness config.
67 """
68 magMin = pexConfig.RangeField(
69 doc="Minimum of cut on magnitude range used to compute completeness "
70 "in.",
71 dtype=float,
72 default=20,
73 min=1,
74 max=40,
75 )
76 magMax = pexConfig.RangeField(
77 doc="Maximum of cut on magnitude range used to compute completeness "
78 "in.",
79 dtype=int,
80 default=30,
81 min=1,
82 max=40,
83 )
86class ApFakesCompletenessMetricTask(MetricTask):
87 """Metric task for summarizing the completeness of fakes inserted into the
88 AP pipeline.
89 """
90 _DefaultName = "apFakesCompleteness"
91 ConfigClass = ApFakesCompletenessMetricConfig
93 def runQuantum(self, butlerQC, inputRefs, outputRefs):
94 try:
95 inputs = butlerQC.get(inputRefs)
96 inputs["band"] = butlerQC.quantum.dataId["band"]
97 outputs = self.run(**inputs)
98 if outputs.measurement is not None:
99 butlerQC.put(outputs, outputRefs)
100 else:
101 self.log.debugf("Skipping measurement of {!r} on {} "
102 "as not applicable.", self, inputRefs)
103 except MetricComputationError:
104 # Apparently lsst.log doesn't have built-in exception support?
105 self.log.errorf(
106 "Measurement of {!r} failed on {}->{}\n{}",
107 self, inputRefs, outputRefs, traceback.format_exc())
109 def run(self, matchedFakes, band):
110 """Compute the completeness of recovered fakes within a magnitude
111 range.
113 Parameters
114 ----------
115 matchedFakes : `lsst.afw.table.SourceCatalog` or `None`
116 Catalog of fakes that were inserted into the ccdExposure matched
117 to their detected counterparts.
118 band : `str`
119 Single character name of the observed band for this quanta.
121 Returns
122 -------
123 result : `lsst.pipe.base.Struct`
124 A `~lsst.pipe.base.Struct` containing the following component:
125 ``measurement``
126 the ratio (`lsst.verify.Measurement` or `None`)
127 """
128 if matchedFakes is not None:
129 magnitudes = np.fabs(matchedFakes[f"{self.config.mag_col}" % band])
130 magCutFakes = matchedFakes[np.logical_and(magnitudes > self.config.magMin,
131 magnitudes < self.config.magMax)]
132 if len(magCutFakes) <= 0.0:
133 raise MetricComputationError(
134 "No matched fakes catalog sources found; Completeness is "
135 "ill defined.")
136 else:
137 meas = Measurement(
138 self.config.metricName,
139 ((magCutFakes["diaSourceId"] > 0).sum() / len(magCutFakes))
140 * u.dimensionless_unscaled)
141 else:
142 self.log.info("Nothing to do: no matched catalog found.")
143 meas = None
144 return Struct(measurement=meas)
147class ApFakesCountMetricConnections(
148 ApFakesCompletenessMetricConnections,
149 dimensions={"instrument", "visit", "detector", "band"},
150 defaultTemplates={"coaddName": "deep",
151 "fakesType": "fakes_",
152 "package": "ap_pipe",
153 "metric": "apFakesCompleteness"}):
154 pass
157class ApFakesCountMetricConfig(
158 ApFakesCompletenessMetricConfig,
159 pipelineConnections=ApFakesCountMetricConnections):
160 """ApFakesCompleteness config.
161 """
162 pass
165class ApFakesCountMetricTask(ApFakesCompletenessMetricTask):
166 """Metric task for summarizing the completeness of fakes inserted into the
167 AP pipeline.
168 """
169 _DefaultName = "apFakesCount"
170 ConfigClass = ApFakesCountMetricConfig
172 def run(self, matchedFakes, band):
173 """Compute the number of fakes inserted within a magnitude
174 range.
176 Parameters
177 ----------
178 matchedFakes : `lsst.afw.table.SourceCatalog` or `None`
179 Catalog of fakes that were inserted into the ccdExposure matched
180 to their detected counterparts.
181 band : `str`
182 Single character name of the observed band for this quanta.
184 Returns
185 -------
186 result : `lsst.pipe.base.Struct`
187 A `~lsst.pipe.base.Struct` containing the following component:
188 ``measurement``
189 the ratio (`lsst.verify.Measurement` or `None`)
190 """
191 if matchedFakes is not None:
192 magnitudes = np.fabs(matchedFakes[f"{self.config.mag_col}" % band])
193 magCutFakes = matchedFakes[np.logical_and(magnitudes > self.config.magMin,
194 magnitudes < self.config.magMax)]
195 meas = Measurement(self.config.metricName,
196 len(magCutFakes) * u.count)
197 else:
198 self.log.info("Nothing to do: no matched catalog supplied.")
199 meas = None
200 return Struct(measurement=meas)