Coverage for python/lsst/ap/association/transformDiaSourceCatalog.py : 41%

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1# This file is part of ap_association
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/>.
22__all__ = ("TransformDiaSourceCatalogConnections",
23 "TransformDiaSourceCatalogConfig",
24 "TransformDiaSourceCatalogTask")
26import numpy as np
27import os
29from lsst.daf.base import DateTime
30import lsst.pex.config as pexConfig
31import lsst.pipe.base as pipeBase
32import lsst.pipe.base.connectionTypes as connTypes
33from lsst.pipe.tasks.postprocess import TransformCatalogBaseTask
34from lsst.pipe.tasks.parquetTable import ParquetTable
35from lsst.utils import getPackageDir
38class TransformDiaSourceCatalogConnections(pipeBase.PipelineTaskConnections,
39 dimensions=("instrument", "visit", "detector"),
40 defaultTemplates={"coaddName": "deep", "fakesType": ""}):
41 """Butler connections for TransformDiaSourceCatalogTask.
42 """
43 diaSourceCat = connTypes.Input(
44 doc="Catalog of DiaSources produced during image differencing.",
45 name="{fakesType}{coaddName}Diff_diaSrc",
46 storageClass="SourceCatalog",
47 dimensions=("instrument", "visit", "detector"),
48 )
49 diffIm = connTypes.Input(
50 doc="Difference image on which the DiaSources were detected.",
51 name="{fakesType}{coaddName}Diff_differenceExp",
52 storageClass="ExposureF",
53 dimensions=("instrument", "visit", "detector"),
54 )
55 diaSourceTable = connTypes.Output(
56 doc=".",
57 name="{fakesType}{coaddName}Diff_diaSrcTable",
58 storageClass="DataFrame",
59 dimensions=("instrument", "visit", "detector"),
60 )
63class TransformDiaSourceCatalogConfig(pipeBase.PipelineTaskConfig,
64 pipelineConnections=TransformDiaSourceCatalogConnections):
65 """
66 """
67 functorFile = pexConfig.Field(
68 dtype=str,
69 doc='Path to YAML file specifying Science DataModel functors to use '
70 'when copying columns and computing calibrated values.',
71 default=os.path.join(getPackageDir("ap_association"),
72 "data",
73 "DiaSource.yaml")
74 )
77class TransformDiaSourceCatalogTask(TransformCatalogBaseTask):
78 """Apply Science DataModel-ification on the DiaSource afw table.
80 This task calibrates and renames columns in the DiaSource catalog
81 to ready the catalog for insertion into the Apdb.
83 This is a Gen3 Butler only task. It will not run in Gen2.
84 """
86 ConfigClass = TransformDiaSourceCatalogConfig
87 _DefaultName = "transformDiaSourceCatalog"
89 def __init__(self, **kwargs):
90 super().__init__(**kwargs)
91 self.funcs = self.getFunctors()
93 def runQuantum(self, butlerQC, inputRefs, outputRefs):
94 inputs = butlerQC.get(inputRefs)
95 expId, expBits = butlerQC.quantum.dataId.pack("visit_detector",
96 returnMaxBits=True)
97 inputs["ccdVisitId"] = expId
98 inputs["band"] = butlerQC.quantum.dataId["band"]
100 outputs = self.run(**inputs)
102 butlerQC.put(outputs, outputRefs)
104 def run(self,
105 diaSourceCat,
106 diffIm,
107 band,
108 ccdVisitId,
109 funcs=None):
110 """Convert input catalog to ParquetTable/Pandas and run functors.
112 Additionally, add new columns for stripping information from the
113 exposure and into the DiaSource catalog.
115 Parameters
116 ----------
118 Returns
119 -------
120 results : `lsst.pipe.base.Struct`
121 Results struct with components.
123 - ``diaSourceTable`` : Catalog of DiaSources with calibrated values
124 and renamed columns.
125 (`lsst.pipe.tasks.ParquetTable` or `pandas.DataFrame`)
126 """
127 self.log.info(
128 "Transforming/standardizing the DiaSource table ccdVisitId: %i",
129 ccdVisitId)
131 diaSourceDf = diaSourceCat.asAstropy().to_pandas()
132 diaSourceDf["bboxSize"] = self.computeBBoxSizes(diaSourceCat)
133 diaSourceDf["ccdVisitId"] = ccdVisitId
134 diaSourceDf["filterName"] = band
135 diaSourceDf["midPointTai"] = diffIm.getInfo().getVisitInfo().getDate().get(system=DateTime.MJD)
136 diaSourceDf["diaObjectId"] = 0
137 diaSourceDf["pixelId"] = 0
139 df = self.transform(band,
140 ParquetTable(dataFrame=diaSourceDf),
141 self.funcs,
142 dataId=None).df
143 return pipeBase.Struct(
144 diaSourceTable=df
145 )
147 def computeBBoxSizes(self, inputCatalog):
148 """Compute the size of a square bbox that fully contains the detection
149 footprint.
151 Parameters
152 ----------
153 inputCatalog : `lsst.afw.table.SourceCatalog`
154 Catalog containing detected footprints.
156 Returns
157 -------
158 outputBBoxSizes : `numpy.ndarray`, (N,)
159 Array of bbox sizes.
160 """
161 outputBBoxSizes = np.empty(len(inputCatalog), dtype=int)
162 for idx, record in enumerate(inputCatalog):
163 footprintBBox = record.getFootprint().getBBox()
164 # Compute twice the size of the largest dimension of the footprint
165 # bounding box. This is the largest footprint we should need to cover
166 # the complete DiaSource assuming the centroid is withing the bounding
167 # box.
168 maxSize = 2 * np.max([footprintBBox.getWidth(),
169 footprintBBox.getHeight()])
170 recX = record.getCentroid().x
171 recY = record.getCentroid().y
172 bboxSize = int(
173 np.ceil(2 * np.max(np.fabs([footprintBBox.maxX - recX,
174 footprintBBox.minX - recX,
175 footprintBBox.maxY - recY,
176 footprintBBox.minY - recY]))))
177 if bboxSize > maxSize:
178 bboxSize = maxSize
179 outputBBoxSizes[idx] = bboxSize
181 return outputBBoxSizes