LSST Applications 27.0.0,g0265f82a02+469cd937ee,g02d81e74bb+21ad69e7e1,g1470d8bcf6+cbe83ee85a,g2079a07aa2+e67c6346a6,g212a7c68fe+04a9158687,g2305ad1205+94392ce272,g295015adf3+81dd352a9d,g2bbee38e9b+469cd937ee,g337abbeb29+469cd937ee,g3939d97d7f+72a9f7b576,g487adcacf7+71499e7cba,g50ff169b8f+5929b3527e,g52b1c1532d+a6fc98d2e7,g591dd9f2cf+df404f777f,g5a732f18d5+be83d3ecdb,g64a986408d+21ad69e7e1,g858d7b2824+21ad69e7e1,g8a8a8dda67+a6fc98d2e7,g99cad8db69+f62e5b0af5,g9ddcbc5298+d4bad12328,ga1e77700b3+9c366c4306,ga8c6da7877+71e4819109,gb0e22166c9+25ba2f69a1,gb6a65358fc+469cd937ee,gbb8dafda3b+69d3c0e320,gc07e1c2157+a98bf949bb,gc120e1dc64+615ec43309,gc28159a63d+469cd937ee,gcf0d15dbbd+72a9f7b576,gdaeeff99f8+a38ce5ea23,ge6526c86ff+3a7c1ac5f1,ge79ae78c31+469cd937ee,gee10cc3b42+a6fc98d2e7,gf1cff7945b+21ad69e7e1,gfbcc870c63+9a11dc8c8f
LSST Data Management Base Package
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data_factory.py
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1# This file is part of dax_apdb.
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
22from __future__ import annotations
23
24import random
25from collections.abc import Iterator
26from typing import Any
27
28import astropy.time
29import numpy
30import pandas
31from lsst.sphgeom import LonLat, Region, UnitVector3d
32
33
34def _genPointsInRegion(region: Region, count: int) -> Iterator[LonLat]:
35 """Generate bunch of SpherePoints inside given region.
36
37 Parameters
38 ----------
39 region : `lsst.sphgeom.Region`
40 Spherical region.
41 count : `int`
42 Number of points to generate.
43
44 Notes
45 -----
46 Returned points are random but not necessarily uniformly distributed.
47 """
48 bbox = region.getBoundingBox()
49 center = bbox.getCenter()
50 center_lon = center.getLon().asRadians()
51 center_lat = center.getLat().asRadians()
52 width = bbox.getWidth().asRadians()
53 height = bbox.getHeight().asRadians()
54 while count > 0:
55 lon = random.uniform(center_lon - width / 2, center_lon + width / 2)
56 lat = random.uniform(center_lat - height / 2, center_lat + height / 2)
57 lonlat = LonLat.fromRadians(lon, lat)
58 uv3d = UnitVector3d(lonlat)
59 if region.contains(uv3d):
60 yield lonlat
61 count -= 1
62
63
65 region: Region, count: int, visit_time: astropy.time.Time, *, start_id: int = 1, **kwargs: Any
66) -> pandas.DataFrame:
67 """Make a catalog containing a bunch of DiaObjects inside a region.
68
69 Parameters
70 ----------
71 region : `lsst.sphgeom.Region`
72 Spherical region.
73 count : `int`
74 Number of records to generate.
75 visit_time : `astropy.time.Time`
76 Time of the visit.
77 start_id : `int`
78 Starting diaObjectId.
79 **kwargs : `Any`
80 Additional columns and their values to add to catalog.
81
82 Returns
83 -------
84 catalog : `pandas.DataFrame`
85 Catalog of DiaObjects records.
86
87 Notes
88 -----
89 Returned catalog only contains three columns - ``diaObjectId`, ``ra``, and
90 ``dec`` (in degrees).
91 """
92 points = list(_genPointsInRegion(region, count))
93 # diaObjectId=0 may be used in some code for DiaSource foreign key to mean
94 # the same as ``None``.
95 ids = numpy.arange(start_id, len(points) + start_id, dtype=numpy.int64)
96 ras = numpy.array([lonlat.getLon().asDegrees() for lonlat in points], dtype=numpy.float64)
97 decs = numpy.array([lonlat.getLat().asDegrees() for lonlat in points], dtype=numpy.float64)
98 nDiaSources = numpy.ones(len(points), dtype=numpy.int32)
99 dt = visit_time.datetime
100 data = dict(
101 kwargs,
102 diaObjectId=ids,
103 ra=ras,
104 dec=decs,
105 nDiaSources=nDiaSources,
106 lastNonForcedSource=dt,
107 )
108 df = pandas.DataFrame(data)
109 return df
110
111
113 objects: pandas.DataFrame, visit_time: astropy.time.Time, start_id: int = 0, ccdVisitId: int = 1
114) -> pandas.DataFrame:
115 """Make a catalog containing a bunch of DiaSources associated with the
116 input DiaObjects.
117
118 Parameters
119 ----------
120 objects : `pandas.DataFrame`
121 Catalog of DiaObject records.
122 visit_time : `astropy.time.Time`
123 Time of the visit.
124 start_id : `int`
125 Starting value for ``diaObjectId``.
126 ccdVisitId : `int`
127 Value for ``ccdVisitId`` field.
128
129 Returns
130 -------
131 catalog : `pandas.DataFrame`
132 Catalog of DiaSource records.
133
134 Notes
135 -----
136 Returned catalog only contains small number of columns needed for tests.
137 """
138 nrows = len(objects)
139 midpointMjdTai = visit_time.mjd
140 df = pandas.DataFrame(
141 {
142 "diaSourceId": numpy.arange(start_id, start_id + nrows, dtype=numpy.int64),
143 "diaObjectId": objects["diaObjectId"],
144 "ccdVisitId": numpy.full(nrows, ccdVisitId, dtype=numpy.int64),
145 "parentDiaSourceId": 0,
146 "ra": objects["ra"],
147 "dec": objects["dec"],
148 "midpointMjdTai": numpy.full(nrows, midpointMjdTai, dtype=numpy.float64),
149 "flags": numpy.full(nrows, 0, dtype=numpy.int64),
150 }
151 )
152 return df
153
154
156 objects: pandas.DataFrame, visit_time: astropy.time.Time, ccdVisitId: int = 1
157) -> pandas.DataFrame:
158 """Make a catalog containing a bunch of DiaForcedSources associated with
159 the input DiaObjects.
160
161 Parameters
162 ----------
163 objects : `pandas.DataFrame`
164 Catalog of DiaObject records.
165 visit_time : `astropy.time.Time`
166 Time of the visit.
167 ccdVisitId : `int`
168 Value for ``ccdVisitId`` field.
169
170 Returns
171 -------
172 catalog : `pandas.DataFrame`
173 Catalog of DiaForcedSource records.
174
175 Notes
176 -----
177 Returned catalog only contains small number of columns needed for tests.
178 """
179 nrows = len(objects)
180 midpointMjdTai = visit_time.mjd
181 df = pandas.DataFrame(
182 {
183 "diaObjectId": objects["diaObjectId"],
184 "ccdVisitId": numpy.full(nrows, ccdVisitId, dtype=numpy.int64),
185 "midpointMjdTai": numpy.full(nrows, midpointMjdTai, dtype=numpy.float64),
186 "flags": numpy.full(nrows, 0, dtype=numpy.int64),
187 }
188 )
189 return df
190
191
192def makeSSObjectCatalog(count: int, start_id: int = 1, flags: int = 0) -> pandas.DataFrame:
193 """Make a catalog containing a bunch of SSObjects.
194
195 Parameters
196 ----------
197 count : `int`
198 Number of records to generate.
199 startID : `int`
200 Initial SSObject ID.
201 flags : `int`
202 Value for ``flags`` column.
203
204 Returns
205 -------
206 catalog : `pandas.DataFrame`
207 Catalog of SSObjects records.
208
209 Notes
210 -----
211 Returned catalog only contains three columns - ``ssObjectId`, ``arc``,
212 and ``flags``.
213 """
214 ids = numpy.arange(start_id, count + start_id, dtype=numpy.int64)
215 arc = numpy.full(count, 0.001, dtype=numpy.float32)
216 flags_array = numpy.full(count, flags, dtype=numpy.int64)
217 df = pandas.DataFrame({"ssObjectId": ids, "arc": arc, "flags": flags_array})
218 return df
UnitVector3d is a unit vector in ℝ³ with components stored in double precision.
pandas.DataFrame makeSourceCatalog(pandas.DataFrame objects, astropy.time.Time visit_time, int start_id=0, int ccdVisitId=1)
pandas.DataFrame makeObjectCatalog(Region region, int count, astropy.time.Time visit_time, *int start_id=1, **Any kwargs)
pandas.DataFrame makeForcedSourceCatalog(pandas.DataFrame objects, astropy.time.Time visit_time, int ccdVisitId=1)
Iterator[LonLat] _genPointsInRegion(Region region, int count)
pandas.DataFrame makeSSObjectCatalog(int count, int start_id=1, int flags=0)