Coverage for python / lsst / drp / tasks / single_frame_detect_and_measure.py: 0%
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1# This file is part of drp_tasks.
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
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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
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22__all__ = ["SingleFrameDetectAndMeasureTask", "SingleFrameDetectAndMeasureConfig"]
24import lsst.afw.table as afwTable
25import lsst.geom
26import lsst.meas.algorithms
27import lsst.meas.deblender
28import lsst.meas.extensions.photometryKron
29import lsst.meas.extensions.shapeHSM
30import lsst.pex.config as pexConfig
31import lsst.pipe.base as pipeBase
32from lsst.pipe.base import connectionTypes
35class SingleFrameDetectAndMeasureConnections(
36 pipeBase.PipelineTaskConnections, dimensions=("instrument", "visit", "detector")
37):
38 # inputs
39 exposure = connectionTypes.Input(
40 doc="Exposure to be calibrated, and detected and measured on.",
41 name="preliminary_visit_image",
42 storageClass="Exposure",
43 dimensions=["instrument", "visit", "detector"],
44 )
45 input_background = connectionTypes.Input(
46 doc="Background models estimated during calibration task; calibrated to be in nJy units.",
47 name="preliminary_visit_image_background",
48 storageClass="Background",
49 dimensions=("instrument", "visit", "detector"),
50 )
52 # outputs
53 sources = connectionTypes.Output(
54 doc="Catalog of measured sources detected on the calibrated exposure.",
55 name="single_visit_star_reprocessed_unstandardized",
56 storageClass="ArrowAstropy",
57 dimensions=["instrument", "visit", "detector"],
58 )
59 sources_footprints = connectionTypes.Output(
60 doc="Catalog of measured sources detected on the calibrated exposure; includes source footprints.",
61 name="single_visit_star_reprocessed_footprints",
62 storageClass="SourceCatalog",
63 dimensions=["instrument", "visit", "detector"],
64 )
65 background = connectionTypes.Output(
66 doc=(
67 "Total background model including new detections in this task. "
68 "Note that the background model has units of ADU, while the corresponding "
69 "image has units of nJy - the image must be 'uncalibrated' before the background "
70 "can be restored."
71 ),
72 name="preliminary_visit_image_reprocessed_background",
73 dimensions=("instrument", "visit", "detector"),
74 storageClass="Background",
75 )
78class SingleFrameDetectAndMeasureConfig(
79 pipeBase.PipelineTaskConfig, pipelineConnections=SingleFrameDetectAndMeasureConnections
80):
81 # To generate catalog ids consistently across subtasks.
82 id_generator = lsst.meas.base.DetectorVisitIdGeneratorConfig.make_field()
84 detection = pexConfig.ConfigurableField(
85 target=lsst.meas.algorithms.SourceDetectionTask,
86 doc="Task to detect sources to return in the output catalog.",
87 )
88 sky_sources = pexConfig.ConfigurableField(
89 target=lsst.meas.algorithms.SkyObjectsTask,
90 doc="Task to generate sky sources ('empty' regions where there are no detections).",
91 )
92 deblend = pexConfig.ConfigurableField(
93 target=lsst.meas.deblender.SourceDeblendTask,
94 doc="Task to split blended sources into their components.",
95 )
96 measurement = pexConfig.ConfigurableField(
97 target=lsst.meas.base.SingleFrameMeasurementTask,
98 doc="Task to measure sources to return in the output catalog.",
99 )
100 normalized_calibration_flux = pexConfig.ConfigurableField(
101 target=lsst.meas.algorithms.NormalizedCalibrationFluxTask,
102 doc="Task to normalize the calibration flux (e.g. compensated tophats).",
103 )
104 apply_aperture_correction = pexConfig.ConfigurableField(
105 target=lsst.meas.base.ApplyApCorrTask,
106 doc="Task to apply aperture corrections to the measured sources.",
107 )
108 set_primary_flags = pexConfig.ConfigurableField(
109 target=lsst.meas.algorithms.setPrimaryFlags.SetPrimaryFlagsTask,
110 doc="Task to add isPrimary to the catalog.",
111 )
112 catalog_calculation = pexConfig.ConfigurableField(
113 target=lsst.meas.base.CatalogCalculationTask,
114 doc="Task to compute catalog values using only the catalog entries.",
115 )
116 do_add_sky_sources = pexConfig.Field(
117 dtype=bool,
118 default=True,
119 doc="Generate sky sources?",
120 )
122 def setDefaults(self):
123 super().setDefaults()
125 # Re-estimate the background
126 self.detection.reEstimateBackground = True
127 self.detection.doTempLocalBackground = False
129 self.measurement.plugins = [
130 "base_SkyCoord",
131 "base_PixelFlags",
132 "base_SdssCentroid",
133 "ext_shapeHSM_HsmSourceMoments",
134 "ext_shapeHSM_HsmPsfMoments",
135 "base_GaussianFlux",
136 "base_PsfFlux",
137 "base_CircularApertureFlux",
138 "base_ClassificationSizeExtendedness",
139 "base_CompensatedTophatFlux",
140 ]
141 # NOTE: these apertures were selected for HSC, and may not be
142 # what we want for LSSTCam.
143 self.measurement.plugins["base_CircularApertureFlux"].radii = [
144 3.0,
145 4.5,
146 6.0,
147 9.0,
148 12.0,
149 17.0,
150 25.0,
151 35.0,
152 50.0,
153 70.0,
154 ]
155 lsst.meas.extensions.shapeHSM.configure_hsm(self.measurement)
157 # TODO DM-46306: should make this the ApertureFlux default!
158 # Use a large aperture to be independent of seeing in calibration
159 self.measurement.plugins["base_CircularApertureFlux"].maxSincRadius = 12.0
161 # Only apply calibration fluxes, do not measure them.
162 self.normalized_calibration_flux.do_measure_ap_corr = False
165class SingleFrameDetectAndMeasureTask(pipeBase.PipelineTask):
166 """Use the visit-level calibrations to perform detection and measurement
167 on the single frame exposures and produce a "final" exposure and catalog.
168 """
170 ConfigClass = SingleFrameDetectAndMeasureConfig
171 _DefaultName = "singleFrameDetectAndMeasure"
173 def __init__(self, schema=None, **kwargs):
174 super().__init__(**kwargs)
176 if schema is None:
177 schema = afwTable.SourceTable.makeMinimalSchema()
179 self.makeSubtask("detection", schema=schema)
180 self.makeSubtask("sky_sources", schema=schema)
181 self.makeSubtask("deblend", schema=schema)
182 self.makeSubtask("measurement", schema=schema)
183 self.makeSubtask("normalized_calibration_flux", schema=schema)
184 self.makeSubtask("apply_aperture_correction", schema=schema)
185 self.makeSubtask("catalog_calculation", schema=schema)
186 self.makeSubtask("set_primary_flags", schema=schema, isSingleFrame=True)
188 schema.addField(
189 "visit",
190 type="L",
191 doc="Visit this source appeared on.",
192 )
193 schema.addField(
194 "detector",
195 type="U",
196 doc="Detector this source appeared on.",
197 )
199 self.schema = schema
201 def runQuantum(self, butlerQC, inputRefs, outputRefs):
202 inputs = butlerQC.get(inputRefs)
203 id_generator = self.config.id_generator.apply(butlerQC.quantum.dataId)
205 exposure = inputs.pop("exposure")
206 input_background = inputs.pop("input_background")
208 # This should not happen with a properly configured execution context.
209 assert not inputs, "runQuantum got more inputs than expected"
211 # Specify the fields that `annotate` needs below, to ensure they
212 # exist, even as None.
213 result = pipeBase.Struct(
214 sources=None,
215 sources_footprints=None,
216 )
217 try:
218 self.run(
219 exposure=exposure,
220 input_background=input_background,
221 id_generator=id_generator,
222 result=result,
223 )
224 except pipeBase.AlgorithmError as e:
225 error = pipeBase.AnnotatedPartialOutputsError.annotate(
226 e, self, result.sources_footprints, log=self.log
227 )
228 butlerQC.put(result, outputRefs)
229 raise error from e
231 butlerQC.put(result, outputRefs)
233 def run(
234 self,
235 exposure,
236 input_background,
237 id_generator=None,
238 result=None,
239 ):
240 """Detect and measure sources on the exposure(s) (snap combined as
241 necessary), and make a "final" Processed Visit Image using all of the
242 supplied metadata, plus a catalog measured on it.
243 Stripped-down version of `ReprocessVisitImageTask`.
245 Parameters
246 ----------
247 exposure : `lsst.afw.image.Exposure`
248 Initial calibrated exposure.
249 The DETECTED mask plane will be modified in place.
250 id_generator : `lsst.meas.base.IdGenerator`, optional
251 Object that generates source IDs and provides random seeds.
252 result : `lsst.pipe.base.Struct`, optional
253 Result struct that is modified to allow saving of partial outputs
254 for some failure conditions. If the task completes successfully,
255 this is also returned.
257 Returns
258 -------
259 result : `lsst.pipe.base.Struct`
260 Results as a struct with attributes:
262 ``sources``
263 Sources that were measured on the exposure, with calibrated
264 fluxes and magnitudes. (`astropy.table.Table`)
265 ``sources_footprints``
266 Footprints of sources that were measured on the exposure.
267 (`lsst.afw.table.SourceCatalog`)
268 ``background``
269 Total background that was fit to, and subtracted from the
270 exposure when detecting ``sources``, in the same nJy units as
271 ``exposure``. (`lsst.afw.math.BackgroundList`)
272 """
273 if result is None:
274 result = pipeBase.Struct()
275 if id_generator is None:
276 id_generator = lsst.meas.base.IdGenerator()
278 table = afwTable.SourceTable.make(self.schema, id_generator.make_table_id_factory())
280 detections = self.detection.run(
281 table=table,
282 exposure=exposure,
283 background=input_background,
284 )
285 sources = detections.sources
286 result.background = detections.background
288 if self.config.do_add_sky_sources:
289 self.sky_sources.run(exposure.mask, id_generator.catalog_id, sources)
291 self.deblend.run(exposure=exposure, sources=sources)
292 # The deblender may not produce a contiguous catalog; ensure
293 # contiguity for subsequent tasks.
294 if not sources.isContiguous():
295 sources = sources.copy(deep=True)
297 self.measurement.run(sources, exposure)
298 self.normalized_calibration_flux.run(exposure=exposure, catalog=sources)
299 self.apply_aperture_correction.run(sources, exposure.apCorrMap)
300 self.catalog_calculation.run(sources)
301 self.set_primary_flags.run(sources)
303 sources["visit"] = exposure.visitInfo.id
304 sources["detector"] = exposure.info.getDetector().getId()
305 result.sources_footprints = sources
306 result.sources = sources.asAstropy()
308 return result