Coverage for python/lsst/ap/pipe/createApFakes.py: 39%
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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.
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/>.
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} deg^2")
158 # Concatenate the data and add dummy values for the unused variables.
159 # Set all data to PSF like objects.
160 randData = {
161 "fakeId": [uuid.uuid4().int & (1 << 64) - 1 for n in range(nFakes)],
162 **self.createRandomPositions(nFakes, tractBoundingCircle, rng),
163 **self.createVisitCoaddSubdivision(nFakes),
164 **self.createRandomMagnitudes(nFakes, rng),
165 self.config.disk_semimajor_col: np.ones(nFakes, dtype="float"),
166 self.config.bulge_semimajor_col: np.ones(nFakes, dtype="float"),
167 self.config.disk_n_col: np.ones(nFakes, dtype="float"),
168 self.config.bulge_n_col: np.ones(nFakes, dtype="float"),
169 self.config.disk_axis_ratio_col: np.ones(nFakes, dtype="float"),
170 self.config.bulge_axis_ratio_col: np.ones(nFakes, dtype="float"),
171 self.config.disk_pa_col: np.zeros(nFakes, dtype="float"),
172 self.config.bulge_pa_col: np.ones(nFakes, dtype="float"),
173 self.config.sourceType: nFakes * ["star"]}
175 return Struct(fakeCat=pd.DataFrame(data=randData))
177 def createRandomPositions(self, nFakes, boundingCircle, rng):
178 """Create a set of spatially uniform randoms over the tract bounding
179 circle on the sphere.
181 Parameters
182 ----------
183 nFakes : `int`
184 Number of fakes to create.
185 boundingCicle : `lsst.sphgeom.BoundingCircle`
186 Circle bound covering the tract.
187 rng : `numpy.random.Generator`
188 Initialized random number generator.
190 Returns
191 -------
192 data : `dict`[`str`, `numpy.ndarray`]
193 Dictionary of RA and Dec locations over the tract.
194 """
195 # Create uniform random vectors on the sky around the north pole.
196 randVect = np.empty((nFakes, 3))
197 randVect[:, 2] = rng.uniform(
198 np.cos(boundingCircle.getOpeningAngle().asRadians()),
199 1,
200 nFakes)
201 sinRawTheta = np.sin(np.arccos(randVect[:, 2]))
202 rawPhi = rng.uniform(0, 2 * np.pi, nFakes)
203 randVect[:, 0] = sinRawTheta * np.cos(rawPhi)
204 randVect[:, 1] = sinRawTheta * np.sin(rawPhi)
206 # Compute the rotation matrix to move our random points to the
207 # correct location.
208 rotMatrix = self._createRotMatrix(boundingCircle)
209 randVect = np.dot(rotMatrix, randVect.transpose()).transpose()
210 decs = np.arcsin(randVect[:, 2])
211 ras = np.arctan2(randVect[:, 1], randVect[:, 0])
213 return {self.config.dec_col: decs,
214 self.config.ra_col: ras}
216 def _createRotMatrix(self, boundingCircle):
217 """Compute the 3d rotation matrix to rotate the dec=90 pole to the
218 center of the circle bound.
220 Parameters
221 ----------
222 boundingCircle : `lsst.sphgeom.BoundingCircle`
223 Circle bound covering the tract.
225 Returns
226 -------
227 rotMatrix : `numpy.ndarray`, (3, 3)
228 3x3 rotation matrix to rotate the dec=90 pole to the location of
229 the circle bound.
231 Notes
232 -----
233 Rotation matrix follows
234 https://mathworld.wolfram.com/RodriguesRotationFormula.html
235 """
236 # Get the center point of our tract
237 center = boundingCircle.getCenter()
239 # Compute the axis to rotate around. This is done by taking the cross
240 # product of dec=90 pole into the tract center.
241 cross = np.array([-center.y(),
242 center.x(),
243 0])
244 cross /= np.sqrt(cross[0] ** 2 + cross[1] ** 2 + cross[2] ** 2)
246 # Get the cosine and sine of the dec angle of the tract center. This
247 # is the amount of rotation needed to move the points we created from
248 # around the pole to the tract location.
249 cosTheta = center.z()
250 sinTheta = np.sin(np.arccos(center.z()))
252 # Compose the rotation matrix for rotation around the axis created from
253 # the cross product.
254 rotMatrix = cosTheta * np.array([[1, 0, 0],
255 [0, 1, 0],
256 [0, 0, 1]])
257 rotMatrix += sinTheta * np.array([[0, -cross[2], cross[1]],
258 [cross[2], 0, -cross[0]],
259 [-cross[1], cross[0], 0]])
260 rotMatrix += (
261 (1 - cosTheta)
262 * np.array(
263 [[cross[0] ** 2, cross[0] * cross[1], cross[0] * cross[2]],
264 [cross[0] * cross[1], cross[1] ** 2, cross[1] * cross[2]],
265 [cross[0] * cross[2], cross[1] * cross[2], cross[2] ** 2]])
266 )
267 return rotMatrix
269 def createVisitCoaddSubdivision(self, nFakes):
270 """Assign a given fake either a visit image or coadd or both based on
271 the ``faction`` config value.
273 Parameters
274 ----------
275 nFakes : `int`
276 Number of fakes to create.
278 Returns
279 -------
280 output : `dict`[`str`, `numpy.ndarray`]
281 Dictionary of boolean arrays specifying which image to put a
282 given fake into.
283 """
284 nBoth = int(self.config.fraction * nFakes)
285 nOnly = int((1 - self.config.fraction) / 2 * nFakes)
286 isVisitSource = np.zeros(nFakes, dtype=bool)
287 isTemplateSource = np.zeros(nFakes, dtype=bool)
288 if nBoth > 0:
289 isVisitSource[:nBoth] = True
290 isTemplateSource[:nBoth] = True
291 if nOnly > 0:
292 isVisitSource[nBoth:(nBoth + nOnly)] = True
293 isTemplateSource[(nBoth + nOnly):] = True
295 return {self.config.visitSourceFlagCol: isVisitSource,
296 self.config.templateSourceFlagCol: isTemplateSource}
298 def createRandomMagnitudes(self, nFakes, rng):
299 """Create a random distribution of magnitudes for out fakes.
301 Parameters
302 ----------
303 nFakes : `int`
304 Number of fakes to create.
305 rng : `numpy.random.Generator`
306 Initialized random number generator.
308 Returns
309 -------
310 randMags : `dict`[`str`, `numpy.ndarray`]
311 Dictionary of magnitudes in the bands set by the ``filterSet``
312 config option.
313 """
314 mags = rng.uniform(self.config.magMin,
315 self.config.magMax,
316 size=nFakes)
317 randMags = {}
318 for fil in self.config.filterSet:
319 randMags[self.config.mag_col % fil] = mags
321 return randMags