Coverage for tests/test_ptcDataset.py: 11%
144 statements
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1# This file is part of ip_isr.
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
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21import unittest
22import tempfile
23import copy
25import numpy as np
27import lsst.utils.tests
29from lsst.ip.isr import PhotonTransferCurveDataset
30import lsst.ip.isr.isrMock as isrMock
33class PtcDatasetCases(lsst.utils.tests.TestCase):
34 """Test that write/read methods of PhotonTransferCurveDataset work
35 """
36 def setUp(self):
38 self.flatMean = 2000
39 self.readNoiseAdu = 10
40 mockImageConfig = isrMock.IsrMock.ConfigClass()
42 # flatDrop is not really relevant as we replace the data
43 # but good to note it in case we change how this image is made
44 mockImageConfig.flatDrop = 0.99999
45 mockImageConfig.isTrimmed = True
47 self.flatExp1 = isrMock.FlatMock(config=mockImageConfig).run()
48 self.flatExp2 = self.flatExp1.clone()
49 (shapeY, shapeX) = self.flatExp1.getDimensions()
51 self.flatWidth = np.sqrt(self.flatMean) + self.readNoiseAdu
53 self.rng1 = np.random.RandomState(1984)
54 flatData1 = self.rng1.normal(self.flatMean, self.flatWidth, (shapeX, shapeY))
55 self.rng2 = np.random.RandomState(666)
56 flatData2 = self.rng2.normal(self.flatMean, self.flatWidth, (shapeX, shapeY))
58 self.flatExp1.image.array[:] = flatData1
59 self.flatExp2.image.array[:] = flatData2
61 self.flux = 1000. # ADU/sec
62 self.gain = 1.5 # e-/ADU
63 self.noiseSq = 5*self.gain # 7.5 (e-)^2
64 self.c1 = 1./self.gain
65 self.timeVec = np.arange(1., 101., 5)
66 self.k2NonLinearity = -5e-6
67 # quadratic signal-chain non-linearity
68 muVec = self.flux*self.timeVec + self.k2NonLinearity*self.timeVec**2
70 self.ampNames = [amp.getName() for amp in self.flatExp1.getDetector().getAmplifiers()]
71 self.dataset = PhotonTransferCurveDataset(self.ampNames, " ") # pack raw data for fitting
72 self.covariancesSqrtWeights = {}
73 for ampName in self.ampNames: # just the expTimes and means here - vars vary per function
74 self.dataset.rawExpTimes[ampName] = self.timeVec
75 self.dataset.rawMeans[ampName] = muVec
76 self.covariancesSqrtWeights[ampName] = []
78 def test_emptyPtcDataset(self):
79 """Test an empty PTC dataset."""
80 emptyDataset = PhotonTransferCurveDataset(
81 self.ampNames,
82 ptcFitType="PARTIAL",
83 )
85 with tempfile.NamedTemporaryFile(suffix=".yaml") as f:
86 usedFilename = emptyDataset.writeText(f.name)
87 fromText = PhotonTransferCurveDataset.readText(usedFilename)
88 self.assertEqual(emptyDataset, fromText)
90 with tempfile.NamedTemporaryFile(suffix=".fits") as f:
91 usedFilename = emptyDataset.writeFits(f.name)
92 fromFits = PhotonTransferCurveDataset.readFits(usedFilename)
93 self.assertEqual(emptyDataset, fromFits)
95 def test_partialPtcDataset(self):
96 """Test of a partial PTC dataset."""
97 # Fill the dataset with made up data.
98 nSideCovMatrix = 2
100 partialDataset = PhotonTransferCurveDataset(
101 self.ampNames,
102 ptcFitType="PARTIAL",
103 covMatrixSide=nSideCovMatrix
104 )
106 for ampName in partialDataset.ampNames:
107 partialDataset.setAmpValuesPartialDataset(
108 ampName,
109 inputExpIdPair=(10, 11),
110 rawExpTime=10.0,
111 rawMean=10.0,
112 rawVar=10.0,
113 )
115 with tempfile.NamedTemporaryFile(suffix=".yaml") as f:
116 usedFilename = partialDataset.writeText(f.name)
117 fromText = PhotonTransferCurveDataset.readText(usedFilename)
118 self.assertEqual(partialDataset, fromText)
120 with tempfile.NamedTemporaryFile(suffix=".fits") as f:
121 usedFilename = partialDataset.writeFits(f.name)
122 fromFits = PhotonTransferCurveDataset.readFits(usedFilename)
123 self.assertEqual(partialDataset, fromFits)
125 def test_ptcDatset(self):
126 """Test of a full PTC dataset."""
127 # Fill the dataset with made up data.
128 nSignalPoints = 5
129 nSideCovMatrix = 2
130 for fitType in ['POLYNOMIAL', 'EXPAPPROXIMATION', 'FULLCOVARIANCE']:
131 localDataset = PhotonTransferCurveDataset(
132 self.ampNames,
133 ptcFitType=fitType,
134 covMatrixSide=nSideCovMatrix,
135 )
136 localDataset.badAmps = [localDataset.ampNames[0], localDataset.ampNames[1]]
137 for ampName in localDataset.ampNames:
139 localDataset.inputExpIdPairs[ampName] = [(1, 2)]*nSignalPoints
140 localDataset.expIdMask[ampName] = np.ones(nSignalPoints, dtype=bool)
141 localDataset.expIdMask[ampName][1] = False
142 localDataset.rawExpTimes[ampName] = np.arange(nSignalPoints)
143 localDataset.rawMeans[ampName] = self.flux*np.arange(nSignalPoints)
144 localDataset.rawVars[ampName] = self.c1*self.flux*np.arange(nSignalPoints)
145 localDataset.photoCharges[ampName] = np.full(nSignalPoints, np.nan)
146 localDataset.gain[ampName] = self.gain
147 localDataset.gainErr[ampName] = 0.1
148 localDataset.noise[ampName] = self.noiseSq
149 localDataset.noiseErr[ampName] = 2.0
151 localDataset.finalVars[ampName] = self.c1*self.flux*np.arange(nSignalPoints)
152 localDataset.finalModelVars[ampName] = np.full(nSignalPoints, 100.0)
153 localDataset.finalMeans[ampName] = self.flux*np.arange(nSignalPoints)
155 if fitType in ['POLYNOMIAL', 'EXPAPPROXIMATION', ]:
156 localDataset.ptcFitPars[ampName] = np.array([10.0, 1.5, 1e-6])
157 localDataset.ptcFitParsError[ampName] = np.array([1.0, 0.2, 1e-7])
158 localDataset.ptcFitChiSq[ampName] = 1.0
159 localDataset.ptcTurnoff[ampName] = localDataset.rawMeans[ampName][-1]
161 localDataset.covariances[ampName] = np.full(
162 (nSignalPoints, nSideCovMatrix, nSideCovMatrix), 105.0)
163 localDataset.covariancesModel[ampName] = np.full(
164 (nSignalPoints, nSideCovMatrix, nSideCovMatrix), np.nan)
165 localDataset.covariancesSqrtWeights[ampName] = np.full((nSignalPoints, nSideCovMatrix,
166 nSideCovMatrix), 10.0)
167 localDataset.aMatrix[ampName] = np.full((nSideCovMatrix, nSideCovMatrix), np.nan)
168 localDataset.bMatrix[ampName] = np.full((nSideCovMatrix, nSideCovMatrix), np.nan)
169 localDataset.covariancesModelNoB[ampName] = np.full((nSignalPoints, nSideCovMatrix,
170 nSideCovMatrix), np.nan)
171 localDataset.aMatrixNoB[ampName] = np.full(
172 (nSideCovMatrix, nSideCovMatrix), np.nan)
174 if localDataset.ptcFitType in ['FULLCOVARIANCE', ]:
175 localDataset.ptcFitPars[ampName] = np.array([np.nan, np.nan])
176 localDataset.ptcFitParsError[ampName] = np.array([np.nan, np.nan])
177 localDataset.ptcFitChiSq[ampName] = np.array([np.nan, np.nan])
178 localDataset.ptcTurnoff[ampName] = np.array([np.nan, np.nan])
180 localDataset.covariances[ampName] = np.full(
181 (nSignalPoints, nSideCovMatrix, nSideCovMatrix), 105.0)
182 localDataset.covariancesModel[ampName] = np.full(
183 (nSignalPoints, nSideCovMatrix, nSideCovMatrix), 100.0)
184 localDataset.covariancesSqrtWeights[ampName] = np.full((nSignalPoints, nSideCovMatrix,
185 nSideCovMatrix), 10.0)
186 localDataset.aMatrix[ampName] = np.full((nSideCovMatrix, nSideCovMatrix), 1e-6)
187 localDataset.bMatrix[ampName] = np.full((nSideCovMatrix, nSideCovMatrix), 1e-7)
188 localDataset.covariancesModelNoB[ampName] = np.full((nSignalPoints, nSideCovMatrix,
189 nSideCovMatrix), 15.0)
190 localDataset.aMatrixNoB[ampName] = np.full(
191 (nSideCovMatrix, nSideCovMatrix), 2e-6)
193 with tempfile.NamedTemporaryFile(suffix=".yaml") as f:
194 usedFilename = localDataset.writeText(f.name)
195 fromText = PhotonTransferCurveDataset.readText(usedFilename)
196 self.assertEqual(localDataset, fromText)
198 with tempfile.NamedTemporaryFile(suffix=".fits") as f:
199 usedFilename = localDataset.writeFits(f.name)
200 fromFits = PhotonTransferCurveDataset.readFits(usedFilename)
201 self.assertEqual(localDataset, fromFits)
203 def test_getExpIdsUsed(self):
204 localDataset = copy.copy(self.dataset)
206 for pair in [(12, 34), (56, 78), (90, 10)]:
207 localDataset.inputExpIdPairs["C:0,0"].append(pair)
208 localDataset.expIdMask["C:0,0"] = np.array([True, False, True])
209 self.assertTrue(np.all(localDataset.getExpIdsUsed("C:0,0") == [(12, 34), (90, 10)]))
211 localDataset.expIdMask["C:0,0"] = np.array([True, False, True, True]) # wrong length now
212 with self.assertRaises(AssertionError):
213 localDataset.getExpIdsUsed("C:0,0")
215 def test_getGoodAmps(self):
216 dataset = self.dataset
218 self.assertTrue(dataset.ampNames == self.ampNames)
219 dataset.badAmps.append("C:0,1")
220 self.assertTrue(dataset.getGoodAmps() == [amp for amp in self.ampNames if amp != "C:0,1"])
222 def test_ptcDataset_pre_dm38309(self):
223 """Test for PTC datasets created by cpSolvePtcTask prior to DM-38309.
224 """
225 localDataset = copy.copy(self.dataset)
227 for pair in [[(12, 34)], [(56, 78)], [(90, 10)]]:
228 localDataset.inputExpIdPairs["C:0,0"].append(pair)
229 localDataset.expIdMask["C:0,0"] = np.array([True, False, True])
231 with self.assertWarnsRegex(RuntimeWarning, "PTC file was written incorrectly"):
232 used = localDataset.getExpIdsUsed("C:0,0")
234 self.assertTrue(np.all(used == [(12, 34), (90, 10)]))
237class MemoryTester(lsst.utils.tests.MemoryTestCase):
238 pass
241def setup_module(module):
242 lsst.utils.tests.init()
245if __name__ == "__main__": 245 ↛ 246line 245 didn't jump to line 246, because the condition on line 245 was never true
246 import sys
247 setup_module(sys.modules[__name__])
248 unittest.main()