Coverage for python / lsst / analysis / tools / tasks / deltaSkyCorrAnalysis.py: 0%
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1# This file is part of analysis_tools.
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
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12# (at your option) any later version.
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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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22__all__ = (
23 "DeltaSkyCorrHistTask",
24 "DeltaSkyCorrHistConfig",
25 "DeltaSkyCorrHistConnections",
26 "DeltaSkyCorrAnalysisConnections",
27 "DeltaSkyCorrAnalysisConfig",
28 "DeltaSkyCorrAnalysisTask",
29)
31import logging
33import numpy as np
35from lsst.pex.config import Field, ListField
36from lsst.pipe.base import PipelineTask, PipelineTaskConfig, PipelineTaskConnections
37from lsst.pipe.base.connectionTypes import Input, Output
39from ..interfaces import AnalysisBaseConfig, AnalysisBaseConnections, AnalysisPipelineTask
41log = logging.getLogger(__name__)
44class DeltaSkyCorrHistConnections(PipelineTaskConnections, dimensions=("instrument", "visit")):
45 """Connections class for DeltaSkyCorrHistTask."""
47 skyCorrs = Input(
48 name="skyCorr",
49 storageClass="Background",
50 doc="Sky correction background models from a run without any synthetic source injection.",
51 multiple=True,
52 dimensions=("instrument", "visit", "detector"),
53 deferLoad=True,
54 )
55 injected_skyCorrs = Input(
56 name="injected_skyCorr",
57 storageClass="Background",
58 doc="Sky correction background models from a run with synthetic sources injected into the data.",
59 multiple=True,
60 dimensions=("instrument", "visit", "detector"),
61 deferLoad=True,
62 )
63 calexpBackgrounds = Input(
64 name="calexpBackground",
65 storageClass="Background",
66 doc="Initial per-detector background models associated with the calibrated exposure.",
67 multiple=True,
68 dimensions=("instrument", "visit", "detector"),
69 deferLoad=True,
70 )
71 photoCalib = Input(
72 name="calexp.photoCalib",
73 storageClass="PhotoCalib",
74 doc="Photometric calibration associated with the calibrated exposure.",
75 multiple=True,
76 dimensions=("instrument", "visit", "detector"),
77 deferLoad=True,
78 )
79 delta_skyCorr_hist = Output(
80 name="delta_skyCorr_hist",
81 storageClass="ArrowNumpyDict",
82 doc="A dictionary containing the histogram values, bin mid points, and bin lower/upper edges for the "
83 "aggregated skyCorr difference dataset, i.e., the difference between the injected and non-injected "
84 "sky correction background models.",
85 dimensions=("instrument", "visit"),
86 )
89class DeltaSkyCorrHistConfig(PipelineTaskConfig, pipelineConnections=DeltaSkyCorrHistConnections):
90 """Config class for DeltaSkyCorrHistTask."""
92 bin_range = ListField[float](
93 doc="The lower and upper range for the histogram bins, in nJy.",
94 default=[-1, 1],
95 )
96 bin_width = Field[float](
97 doc="The width of each histogram bin, in nJy.",
98 default=0.0001,
99 )
102class DeltaSkyCorrHistTask(PipelineTask):
103 """A task for generating a histogram of counts in the difference image
104 between an injected sky correction frame and a non-injected sky correction
105 frame (i.e., injected_skyCorr - skyCorr).
106 """
108 ConfigClass = DeltaSkyCorrHistConfig
109 _DefaultName = "deltaSkyCorrHist"
111 def __init__(self, initInputs=None, *args, **kwargs):
112 super().__init__(*args, **kwargs)
114 def runQuantum(self, butlerQC, inputRefs, outputRefs):
115 inputs = butlerQC.get(inputRefs)
116 inputs["num_initial_bgs"] = len(inputs["calexpBackgrounds"][0].get())
117 delta_skyCorr_hist = self.run(**{k: v for k, v in inputs.items() if k != "calexpBackgrounds"})
118 butlerQC.put(delta_skyCorr_hist, outputRefs.delta_skyCorr_hist)
120 def run(self, skyCorrs, injected_skyCorrs, num_initial_bgs, photoCalib):
121 """Generate a histogram of counts in the difference image between an
122 injected sky correction frame and a non-injected sky correction frame
123 (i.e., injected_skyCorr - skyCorr).
125 Parameters
126 ----------
127 skyCorrs : `list`[`~lsst.daf.butler.DeferredDatasetHandle`]
128 Sky correction background models from a run without any synthetic
129 source injection.
130 These deferred dataset handles should normally resolve to
131 `~lsst.afw.math.BackgroundList` objects.
132 injected_skyCorrs : `list`[`~lsst.daf.butler.DeferredDatasetHandle`]
133 Sky correction background models from a run with synthetic sources
134 injected into the data.
135 These deferred dataset handles should normally resolve to
136 `~lsst.afw.math.BackgroundList` objects.
137 num_initial_bgs : `int`
138 The length of the initial per-detector background model list.
139 This number of background models will be skipped from the start of
140 each skyCorr/injected_skyCorr background model list.
141 See the Notes section for more details.
142 photoCalib : `list`[`~lsst.daf.butler.DeferredDatasetHandle`]
143 Photometric calibration, for conversion from counts to nJy.
145 Returns
146 -------
147 delta_skyCorr_hist : `dict`[`str`, `~numpy.ndarray`]
148 A dictionary containing the histogram values and bin lower/upper
149 edges for the skyCorr difference dataset.
151 Notes
152 -----
153 The first N background elements in the skyCorr/injected_skyCorr
154 background list are the inverse of the initial per-detector background
155 solution.
156 The effect of this is that adding a sky correction frame to a
157 background-subtracted calibrated exposure will undo the per-detector
158 background solution and apply the full focal plane sky correction in
159 its place.
161 For this task, we only want to compare the extra (subtractive) sky
162 correction components, so we skip the first N background models from
163 the sky frame.
164 """
165 # Generate lookup tables for the skyCorr/injected_skyCorr data.
166 lookup_skyCorrs = {x.dataId: x for x in skyCorrs}
167 lookup_injected_skyCorrs = {x.dataId: x for x in injected_skyCorrs}
168 lookup_photoCalib = {x.dataId: x for x in photoCalib}
170 # Set up the global histogram.
171 bin_edges = np.arange(
172 self.config.bin_range[0],
173 self.config.bin_range[1] + self.config.bin_width,
174 self.config.bin_width,
175 )
176 hist = np.zeros(len(bin_edges) - 1)
177 log.info("Generating a histogram containing %d bins.", len(hist))
179 # Loop over the skyCorr/injected_skyCorr data.
180 for dataId in lookup_injected_skyCorrs.keys():
181 # Get the skyCorr/injected_skyCorr data.
182 skyCorr = lookup_skyCorrs[dataId].get()
183 injected_skyCorr = lookup_injected_skyCorrs[dataId].get()
184 # And the photometric calibration
185 instFluxToNanojansky = lookup_photoCalib[dataId].get().instFluxToNanojansky(1)
187 # Isolate the extra (subtractive) sky correction components.
188 skyCorr_extras = skyCorr.clone()
189 skyCorr_extras._backgrounds = skyCorr_extras._backgrounds[num_initial_bgs:]
190 injected_skyCorr_extras = injected_skyCorr.clone()
191 injected_skyCorr_extras._backgrounds = injected_skyCorr_extras._backgrounds[num_initial_bgs:]
193 # Create the delta_skyCorr array.
194 delta_skyCorr_det = injected_skyCorr_extras.getImage().array - skyCorr_extras.getImage().array
195 delta_skyCorr_det *= instFluxToNanojansky # Convert image to nJy
197 # Compute the per-detector histogram; update the global histogram.
198 hist_det, _ = np.histogram(delta_skyCorr_det, bins=bin_edges)
199 hist += hist_det
201 # Return results.
202 num_populated_bins = len([x for x in hist if x == 0])
203 log.info("Populated %d of %d histogram bins.", len(hist) - num_populated_bins, len(hist))
204 bin_mid = bin_edges[:-1] + (self.config.bin_width / 2)
205 delta_skyCorr_hist = dict(
206 hist=hist, bin_lower=bin_edges[:-1], bin_upper=bin_edges[1:], bin_mid=bin_mid
207 )
208 return delta_skyCorr_hist
211class DeltaSkyCorrAnalysisConnections(
212 AnalysisBaseConnections,
213 dimensions=("instrument", "visit"),
214 defaultTemplates={"outputName": "deltaSkyCorr"},
215):
216 data = Input(
217 name="delta_skyCorr_hist",
218 storageClass="ArrowNumpyDict",
219 doc="A dictionary containing the histogram values, bin mid points, and bin lower/upper edges for the "
220 "aggregated skyCorr difference dataset, i.e., the difference between the injected and non-injected "
221 "sky correction background models.",
222 deferLoad=True,
223 dimensions=("instrument", "visit"),
224 )
227class DeltaSkyCorrAnalysisConfig(AnalysisBaseConfig, pipelineConnections=DeltaSkyCorrAnalysisConnections):
228 pass
231class DeltaSkyCorrAnalysisTask(AnalysisPipelineTask):
232 ConfigClass = DeltaSkyCorrAnalysisConfig
233 _DefaultName = "deltaSkyCorrAnalysis"