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LSST Data Management Base Package
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reserveSourcesTask.py
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2# LSST Data Management System
3#
4# Copyright 2008-2017 AURA/LSST.
5#
6# This product includes software developed by the
7# LSST Project (http://www.lsst.org/).
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 LSST License Statement and
20# the GNU General Public License along with this program. If not,
21# see <https://www.lsstcorp.org/LegalNotices/>.
22#
23
24__all__ = ["ReserveSourcesConfig", "ReserveSourcesTask"]
25
26import numpy as np
27
28from lsst.pex.config import Config, Field
29from lsst.pipe.base import Task, Struct
30
31
33 """Configuration for reserving sources"""
34 fraction = Field(dtype=float, default=0.0,
35 doc="Fraction of candidates to reserve from fitting; none if <= 0")
36 seed = Field(dtype=int, default=1,
37 doc=("This number will be added to the exposure ID to set the random seed for "
38 "reserving candidates"))
39
40
42 """Reserve sources from analysis
43
44 We randomly select a fraction of sources that will be reserved
45 from analysis. This allows evaluation of the quality of model fits
46 using sources that were not involved in the fitting process.
47
48 Parameters
49 ----------
50 columnName : `str`, required
51 Name of flag column to add; we will suffix this with "_reserved".
52 schema : `lsst.afw.table.Schema`, required
53 Catalog schema.
54 doc : `str`
55 Documentation for column to add.
56 config : `ReserveSourcesConfig`
57 Configuration.
58 """
59 ConfigClass = ReserveSourcesConfig
60 _DefaultName = "reserveSources"
61
62 def __init__(self, columnName=None, schema=None, doc=None, **kwargs):
63 Task.__init__(self, **kwargs)
64 assert columnName is not None, "columnName not provided"
65 assert schema is not None, "schema not provided"
66 self.columnName = columnName
67 self.key = schema.addField(self.columnName + "_reserved", type="Flag", doc=doc)
68
69 def run(self, sources, prior=None, expId=0):
70 """Select sources to be reserved
71
72 Reserved sources will be flagged in the catalog, and we will return
73 boolean arrays that identify the sources to be reserved from and
74 used in the analysis. Typically you'll want to use the sources
75 from the `use` array in your fitting, and use the sources from the
76 `reserved` array as an independent test of your fitting.
77
78 Parameters
79 ----------
80 sources : `lsst.afw.table.Catalog` or `list` of `lsst.afw.table.Record`
81 Sources from which to select some to be reserved.
82 prior : `numpy.ndarray` of type `bool`, optional
83 Prior selection of sources. Should have the same length as
84 `sources`. If set, we will only consider for reservation sources
85 that are flagged `True` in this array.
86 expId : `int`
87 Exposure identifier; used for seeding the random number generator.
88
89 Returns
90 -------
91 results : `lsst.pipe.base.Struct`
92 The results in a `~lsst.pipe.base.Struct`:
93
94 ``reserved``
95 Sources to be reserved are flagged `True` in this array.
96 (`numpy.ndarray` of type `bool`)
97 ``use``
98 Sources the user should use in analysis are flagged `True`.
99 (`numpy.ndarray` of type `bool`)
100 """
101 if prior is not None:
102 assert len(prior) == len(sources), "Length mismatch: %s vs %s" % (len(prior), len(sources))
103 numSources = prior.sum()
104 else:
105 numSources = len(sources)
106 selection = self.select(numSources, expId)
107 if prior is not None:
108 selection = self.applySelectionPrior(prior, selection)
109 self.markSources(sources, selection)
110 self.log.info("Reserved %d/%d sources", selection.sum(), len(selection))
111 return Struct(reserved=selection,
112 use=prior & ~selection if prior is not None else np.logical_not(selection))
113
114 def select(self, numSources, expId=0):
115 """Randomly select some sources
116
117 We return a boolean array with a random selection. The fraction
118 of sources selected is specified by the config parameter `fraction`.
119
120 Parameters
121 ----------
122 numSources : `int`
123 Number of sources in catalog from which to select.
124 expId : `int`
125 Exposure identifier; used for seeding the random number generator.
126
127 Returns
128 -------
129 selection : `numpy.ndarray` of type `bool`
130 Selected sources are flagged `True` in this array.
131 """
132 selection = np.zeros(numSources, dtype=bool)
133 if self.config.fraction <= 0:
134 return selection
135 reserve = int(np.round(numSources*self.config.fraction))
136 selection[:reserve] = True
137 rng = np.random.RandomState((self.config.seed + expId) & 0xFFFFFFFF)
138 rng.shuffle(selection)
139 return selection
140
141 def applySelectionPrior(self, prior, selection):
142 """Apply selection to full catalog
143
144 The `select` method makes a random selection of sources. If those
145 sources don't represent the full population (because a sub-selection
146 has already been made), then we need to generate a selection covering
147 the entire population.
148
149 Parameters
150 ----------
151 prior : `numpy.ndarray` of type `bool`
152 Prior selection of sources, identifying the subset from which the
153 random selection has been made.
154 selection : `numpy.ndarray` of type `bool`
155 Selection of sources in subset identified by `prior`.
156
157 Returns
158 -------
159 full : `numpy.ndarray` of type `bool`
160 Selection applied to full population.
161 """
162 full = np.zeros(len(prior), dtype=bool)
163 full[prior] = selection
164 return full
165
166 def markSources(self, sources, selection):
167 """Mark sources in a list or catalog
168
169 This requires iterating through the list and setting the flag in
170 each source individually. Even if the `sources` is a `Catalog`
171 with contiguous records, it's not currently possible to set a boolean
172 column (DM-6981) so we need to iterate.
173
174 Parameters
175 ----------
176 catalog : `lsst.afw.table.Catalog` or `list` of `lsst.afw.table.Record`
177 Catalog in which to flag selected sources.
178 selection : `numpy.ndarray` of type `bool`
179 Selection of sources to mark.
180 """
181 for src, select in zip(sources, selection):
182 if select:
183 src.set(self.key, True)
__init__(self, columnName=None, schema=None, doc=None, **kwargs)