Coverage for python/lsst/ap/pipe/createApFakes.py: 35%
76 statements
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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# (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.
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22import numpy as np
23import pandas as pd
24import uuid
26import lsst.pex.config as pexConfig
27from lsst.pipe.base import PipelineTask, PipelineTaskConnections, Struct
28import lsst.pipe.base.connectionTypes as connTypes
29from lsst.pipe.tasks.insertFakes import InsertFakesConfig
30from lsst.skymap import BaseSkyMap
32__all__ = ["CreateRandomApFakesTask",
33 "CreateRandomApFakesConfig",
34 "CreateRandomApFakesConnections"]
37class CreateRandomApFakesConnections(PipelineTaskConnections,
38 defaultTemplates={"fakesType": "fakes_"},
39 dimensions=("tract", "skymap")):
40 skyMap = connTypes.Input(
41 doc="Input definition of geometry/bbox and projection/wcs for "
42 "template exposures",
43 name=BaseSkyMap.SKYMAP_DATASET_TYPE_NAME,
44 dimensions=("skymap",),
45 storageClass="SkyMap",
46 )
47 fakeCat = connTypes.Output(
48 doc="Catalog of fake sources to draw inputs from.",
49 name="{fakesType}fakeSourceCat",
50 storageClass="DataFrame",
51 dimensions=("tract", "skymap")
52 )
55class CreateRandomApFakesConfig(
56 InsertFakesConfig,
57 pipelineConnections=CreateRandomApFakesConnections):
58 """Config for CreateRandomApFakesTask. Copy from the InsertFakesConfig to
59 assert that columns created with in this task match that those expected in
60 the InsertFakes and related tasks.
61 """
62 fakeDensity = pexConfig.RangeField(
63 doc="Goal density of random fake sources per square degree. Default "
64 "value is roughly the density per square degree for ~10k sources "
65 "visit.",
66 dtype=float,
67 default=1000,
68 min=0,
69 )
70 filterSet = pexConfig.ListField(
71 doc="Set of Abstract filter names to produce magnitude columns for.",
72 dtype=str,
73 default=["u", "g", "r", "i", "z", "y"],
74 )
75 fraction = pexConfig.RangeField(
76 doc="Fraction of the created source that should be inserted into both "
77 "the visit and template images. Values less than 1 will result in "
78 "(1 - fraction) / 2 inserted into only visit or the template.",
79 dtype=float,
80 default=1/3,
81 min=0,
82 max=1,
83 )
84 magMin = pexConfig.RangeField(
85 doc="Minimum magnitude the mag distribution. All magnitudes requested "
86 "are set to the same value.",
87 dtype=float,
88 default=20,
89 min=1,
90 max=40,
91 )
92 magMax = pexConfig.RangeField(
93 doc="Maximum magnitude the mag distribution. All magnitudes requested "
94 "are set to the same value.",
95 dtype=float,
96 default=30,
97 min=1,
98 max=40,
99 )
100 visitSourceFlagCol = pexConfig.Field(
101 doc="Name of the column flagging objects for insertion into the visit "
102 "image.",
103 dtype=str,
104 default="isVisitSource"
105 )
106 templateSourceFlagCol = pexConfig.Field(
107 doc="Name of the column flagging objects for insertion into the "
108 "template image.",
109 dtype=str,
110 default="isTemplateSource"
111 )
114class CreateRandomApFakesTask(PipelineTask):
115 """Create and store a set of spatially uniform star fakes over the sphere
116 for use in AP processing. Additionally assign random magnitudes to said
117 fakes and assign them to be inserted into either a visit exposure or
118 template exposure.
119 """
121 _DefaultName = "createApFakes"
122 ConfigClass = CreateRandomApFakesConfig
124 def runQuantum(self, butlerQC, inputRefs, outputRefs):
125 inputs = butlerQC.get(inputRefs)
126 inputs["tractId"] = butlerQC.quantum.dataId["tract"]
128 outputs = self.run(**inputs)
129 butlerQC.put(outputs, outputRefs)
131 def run(self, tractId, skyMap):
132 """Create a set of uniform random points that covers a tract.
134 Parameters
135 ----------
136 tractId : `int`
137 Tract id to produce randoms over.
138 skyMap : `lsst.skymap.SkyMap`
139 Skymap to produce randoms over.
141 Returns
142 -------
143 randoms : `pandas.DataFrame`
144 Catalog of random points covering the given tract. Follows the
145 columns and format expected in `lsst.pipe.tasks.InsertFakes`.
146 """
147 # Use the tractId as the ranomd seed.
148 rng = np.random.default_rng(tractId)
149 tractBoundingCircle = \
150 skyMap.generateTract(tractId).getInnerSkyPolygon().getBoundingCircle()
151 tractArea = tractBoundingCircle.getArea() * (180 / np.pi) ** 2
152 nFakes = int(self.config.fakeDensity * tractArea)
154 self.log.info(
155 f"Creating {nFakes} star fakes over tractId={tractId} with "
156 f"bounding circle area: {tractArea:.4f} deg^2 and "
157 f"magnitude range: [{self.config.magMin, self.config.magMax}]")
159 # Concatenate the data and add dummy values for the unused variables.
160 # Set all data to PSF like objects.
161 randData = {
162 "fakeId": [uuid.uuid4().int & (1 << 64) - 1 for n in range(nFakes)],
163 **self.createRandomPositions(nFakes, tractBoundingCircle, rng),
164 **self.createVisitCoaddSubdivision(nFakes),
165 **self.createRandomMagnitudes(nFakes, rng),
166 self.config.disk_semimajor_col: np.ones(nFakes, dtype="float"),
167 self.config.bulge_semimajor_col: np.ones(nFakes, dtype="float"),
168 self.config.disk_n_col: np.ones(nFakes, dtype="float"),
169 self.config.bulge_n_col: np.ones(nFakes, dtype="float"),
170 self.config.disk_axis_ratio_col: np.ones(nFakes, dtype="float"),
171 self.config.bulge_axis_ratio_col: np.ones(nFakes, dtype="float"),
172 self.config.disk_pa_col: np.zeros(nFakes, dtype="float"),
173 self.config.bulge_pa_col: np.ones(nFakes, dtype="float"),
174 self.config.sourceType: nFakes * ["star"]}
176 return Struct(fakeCat=pd.DataFrame(data=randData))
178 def createRandomPositions(self, nFakes, boundingCircle, rng):
179 """Create a set of spatially uniform randoms over the tract bounding
180 circle on the sphere.
182 Parameters
183 ----------
184 nFakes : `int`
185 Number of fakes to create.
186 boundingCicle : `lsst.sphgeom.BoundingCircle`
187 Circle bound covering the tract.
188 rng : `numpy.random.Generator`
189 Initialized random number generator.
191 Returns
192 -------
193 data : `dict`[`str`, `numpy.ndarray`]
194 Dictionary of RA and Dec locations over the tract.
195 """
196 # Create uniform random vectors on the sky around the north pole.
197 randVect = np.empty((nFakes, 3))
198 randVect[:, 2] = rng.uniform(
199 np.cos(boundingCircle.getOpeningAngle().asRadians()),
200 1,
201 nFakes)
202 sinRawTheta = np.sin(np.arccos(randVect[:, 2]))
203 rawPhi = rng.uniform(0, 2 * np.pi, nFakes)
204 randVect[:, 0] = sinRawTheta * np.cos(rawPhi)
205 randVect[:, 1] = sinRawTheta * np.sin(rawPhi)
207 # Compute the rotation matrix to move our random points to the
208 # correct location.
209 rotMatrix = self._createRotMatrix(boundingCircle)
210 randVect = np.dot(rotMatrix, randVect.transpose()).transpose()
211 decs = np.arcsin(randVect[:, 2])
212 ras = np.arctan2(randVect[:, 1], randVect[:, 0])
214 return {self.config.dec_col: decs,
215 self.config.ra_col: ras}
217 def _createRotMatrix(self, boundingCircle):
218 """Compute the 3d rotation matrix to rotate the dec=90 pole to the
219 center of the circle bound.
221 Parameters
222 ----------
223 boundingCircle : `lsst.sphgeom.BoundingCircle`
224 Circle bound covering the tract.
226 Returns
227 -------
228 rotMatrix : `numpy.ndarray`, (3, 3)
229 3x3 rotation matrix to rotate the dec=90 pole to the location of
230 the circle bound.
232 Notes
233 -----
234 Rotation matrix follows
235 https://mathworld.wolfram.com/RodriguesRotationFormula.html
236 """
237 # Get the center point of our tract
238 center = boundingCircle.getCenter()
240 # Compute the axis to rotate around. This is done by taking the cross
241 # product of dec=90 pole into the tract center.
242 cross = np.array([-center.y(),
243 center.x(),
244 0])
245 cross /= np.sqrt(cross[0] ** 2 + cross[1] ** 2 + cross[2] ** 2)
247 # Get the cosine and sine of the dec angle of the tract center. This
248 # is the amount of rotation needed to move the points we created from
249 # around the pole to the tract location.
250 cosTheta = center.z()
251 sinTheta = np.sin(np.arccos(center.z()))
253 # Compose the rotation matrix for rotation around the axis created from
254 # the cross product.
255 rotMatrix = cosTheta * np.array([[1, 0, 0],
256 [0, 1, 0],
257 [0, 0, 1]])
258 rotMatrix += sinTheta * np.array([[0, -cross[2], cross[1]],
259 [cross[2], 0, -cross[0]],
260 [-cross[1], cross[0], 0]])
261 rotMatrix += (
262 (1 - cosTheta)
263 * np.array(
264 [[cross[0] ** 2, cross[0] * cross[1], cross[0] * cross[2]],
265 [cross[0] * cross[1], cross[1] ** 2, cross[1] * cross[2]],
266 [cross[0] * cross[2], cross[1] * cross[2], cross[2] ** 2]])
267 )
268 return rotMatrix
270 def createVisitCoaddSubdivision(self, nFakes):
271 """Assign a given fake either a visit image or coadd or both based on
272 the ``faction`` config value.
274 Parameters
275 ----------
276 nFakes : `int`
277 Number of fakes to create.
279 Returns
280 -------
281 output : `dict`[`str`, `numpy.ndarray`]
282 Dictionary of boolean arrays specifying which image to put a
283 given fake into.
284 """
285 nBoth = int(self.config.fraction * nFakes)
286 nOnly = int((1 - self.config.fraction) / 2 * nFakes)
287 isVisitSource = np.zeros(nFakes, dtype=bool)
288 isTemplateSource = np.zeros(nFakes, dtype=bool)
289 if nBoth > 0:
290 isVisitSource[:nBoth] = True
291 isTemplateSource[:nBoth] = True
292 if nOnly > 0:
293 isVisitSource[nBoth:(nBoth + nOnly)] = True
294 isTemplateSource[(nBoth + nOnly):] = True
296 return {self.config.visitSourceFlagCol: isVisitSource,
297 self.config.templateSourceFlagCol: isTemplateSource}
299 def createRandomMagnitudes(self, nFakes, rng):
300 """Create a random distribution of magnitudes for out fakes.
302 Parameters
303 ----------
304 nFakes : `int`
305 Number of fakes to create.
306 rng : `numpy.random.Generator`
307 Initialized random number generator.
309 Returns
310 -------
311 randMags : `dict`[`str`, `numpy.ndarray`]
312 Dictionary of magnitudes in the bands set by the ``filterSet``
313 config option.
314 """
315 mags = rng.uniform(self.config.magMin,
316 self.config.magMax,
317 size=nFakes)
318 randMags = {}
319 for fil in self.config.filterSet:
320 randMags[self.config.mag_col % fil] = mags
322 return randMags