Coverage for tests/test_ptcDataset.py: 9%
205 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
20# along with this program. If not, see <https://www.gnu.org/licenses/>.
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 _checkTypes(self, ptcDataset):
79 """Check that all the types are correct for a ptc dataset."""
80 for ampName in ptcDataset.ampNames:
81 self.assertIsInstance(ptcDataset.expIdMask[ampName], np.ndarray)
82 self.assertEqual(ptcDataset.expIdMask[ampName].dtype, bool)
83 self.assertIsInstance(ptcDataset.rawExpTimes[ampName], np.ndarray)
84 self.assertEqual(ptcDataset.rawExpTimes[ampName].dtype, np.float64)
85 self.assertIsInstance(ptcDataset.rawMeans[ampName], np.ndarray)
86 self.assertEqual(ptcDataset.rawMeans[ampName].dtype, np.float64)
87 self.assertIsInstance(ptcDataset.rawVars[ampName], np.ndarray)
88 self.assertEqual(ptcDataset.rawVars[ampName].dtype, np.float64)
89 self.assertIsInstance(ptcDataset.gain[ampName], float)
90 self.assertIsInstance(ptcDataset.gainErr[ampName], float)
91 self.assertIsInstance(ptcDataset.noise[ampName], float)
92 self.assertIsInstance(ptcDataset.noiseErr[ampName], float)
93 self.assertIsInstance(ptcDataset.histVars[ampName], np.ndarray)
94 self.assertEqual(ptcDataset.histVars[ampName].dtype, np.float64)
95 self.assertIsInstance(ptcDataset.histChi2Dofs[ampName], np.ndarray)
96 self.assertEqual(ptcDataset.histChi2Dofs[ampName].dtype, np.float64)
97 self.assertIsInstance(ptcDataset.kspValues[ampName], np.ndarray)
98 self.assertEqual(ptcDataset.kspValues[ampName].dtype, np.float64)
99 self.assertIsInstance(ptcDataset.ptcFitPars[ampName], np.ndarray)
100 self.assertEqual(ptcDataset.ptcFitPars[ampName].dtype, np.float64)
101 self.assertIsInstance(ptcDataset.ptcFitParsError[ampName], np.ndarray)
102 self.assertEqual(ptcDataset.ptcFitParsError[ampName].dtype, np.float64)
103 self.assertIsInstance(ptcDataset.ptcFitChiSq[ampName], float)
104 self.assertIsInstance(ptcDataset.ptcTurnoff[ampName], float)
105 self.assertIsInstance(ptcDataset.covariances[ampName], np.ndarray)
106 self.assertEqual(ptcDataset.covariances[ampName].dtype, np.float64)
107 self.assertIsInstance(ptcDataset.covariancesModel[ampName], np.ndarray)
108 self.assertEqual(ptcDataset.covariancesModel[ampName].dtype, np.float64)
109 self.assertIsInstance(ptcDataset.covariancesSqrtWeights[ampName], np.ndarray)
110 self.assertEqual(ptcDataset.covariancesSqrtWeights[ampName].dtype, np.float64)
111 self.assertIsInstance(ptcDataset.aMatrix[ampName], np.ndarray)
112 self.assertEqual(ptcDataset.aMatrix[ampName].dtype, np.float64)
113 self.assertIsInstance(ptcDataset.bMatrix[ampName], np.ndarray)
114 self.assertEqual(ptcDataset.bMatrix[ampName].dtype, np.float64)
115 self.assertIsInstance(ptcDataset.covariancesModelNoB[ampName], np.ndarray)
116 self.assertEqual(ptcDataset.covariancesModelNoB[ampName].dtype, np.float64)
117 self.assertIsInstance(ptcDataset.aMatrixNoB[ampName], np.ndarray)
118 self.assertEqual(ptcDataset.aMatrixNoB[ampName].dtype, np.float64)
119 self.assertIsInstance(ptcDataset.finalVars[ampName], np.ndarray)
120 self.assertEqual(ptcDataset.finalVars[ampName].dtype, np.float64)
121 self.assertIsInstance(ptcDataset.finalModelVars[ampName], np.ndarray)
122 self.assertEqual(ptcDataset.finalModelVars[ampName].dtype, np.float64)
123 self.assertIsInstance(ptcDataset.finalMeans[ampName], np.ndarray)
124 self.assertEqual(ptcDataset.finalMeans[ampName].dtype, np.float64)
125 self.assertIsInstance(ptcDataset.photoCharges[ampName], np.ndarray)
126 self.assertEqual(ptcDataset.photoCharges[ampName].dtype, np.float64)
128 def test_emptyPtcDataset(self):
129 """Test an empty PTC dataset."""
130 emptyDataset = PhotonTransferCurveDataset(
131 self.ampNames,
132 ptcFitType="PARTIAL",
133 )
134 self._checkTypes(emptyDataset)
136 with tempfile.NamedTemporaryFile(suffix=".yaml") as f:
137 usedFilename = emptyDataset.writeText(f.name)
138 fromText = PhotonTransferCurveDataset.readText(usedFilename)
139 self.assertEqual(emptyDataset, fromText)
140 self._checkTypes(emptyDataset)
142 with tempfile.NamedTemporaryFile(suffix=".fits") as f:
143 usedFilename = emptyDataset.writeFits(f.name)
144 fromFits = PhotonTransferCurveDataset.readFits(usedFilename)
145 self.assertEqual(emptyDataset, fromFits)
146 self._checkTypes(emptyDataset)
148 def test_partialPtcDataset(self):
149 """Test of a partial PTC dataset."""
150 # Fill the dataset with made up data.
151 nSideCovMatrix = 2
153 partialDataset = PhotonTransferCurveDataset(
154 self.ampNames,
155 ptcFitType="PARTIAL",
156 covMatrixSide=nSideCovMatrix
157 )
158 self._checkTypes(partialDataset)
160 for ampName in partialDataset.ampNames:
161 partialDataset.setAmpValuesPartialDataset(
162 ampName,
163 inputExpIdPair=(10, 11),
164 rawExpTime=10.0,
165 rawMean=10.0,
166 rawVar=10.0,
167 )
168 self._checkTypes(partialDataset)
170 with tempfile.NamedTemporaryFile(suffix=".yaml") as f:
171 usedFilename = partialDataset.writeText(f.name)
172 fromText = PhotonTransferCurveDataset.readText(usedFilename)
173 self.assertEqual(fromText, partialDataset)
174 self._checkTypes(fromText)
176 with tempfile.NamedTemporaryFile(suffix=".fits") as f:
177 usedFilename = partialDataset.writeFits(f.name)
178 fromFits = PhotonTransferCurveDataset.readFits(usedFilename)
179 self.assertEqual(fromFits, partialDataset)
180 self._checkTypes(fromFits)
182 def test_ptcDatset(self):
183 """Test of a full PTC dataset."""
184 # Fill the dataset with made up data.
185 nSignalPoints = 5
186 nSideCovMatrix = 2
187 for fitType in ['POLYNOMIAL', 'EXPAPPROXIMATION', 'FULLCOVARIANCE']:
188 localDataset = PhotonTransferCurveDataset(
189 self.ampNames,
190 ptcFitType=fitType,
191 covMatrixSide=nSideCovMatrix,
192 )
193 localDataset.badAmps = [localDataset.ampNames[0], localDataset.ampNames[1]]
194 for ampName in localDataset.ampNames:
196 localDataset.inputExpIdPairs[ampName] = [(1, 2)]*nSignalPoints
197 localDataset.expIdMask[ampName] = np.ones(nSignalPoints, dtype=bool)
198 localDataset.expIdMask[ampName][1] = False
199 localDataset.rawExpTimes[ampName] = np.arange(nSignalPoints, dtype=np.float64)
200 localDataset.rawMeans[ampName] = self.flux*np.arange(nSignalPoints)
201 localDataset.rawVars[ampName] = self.c1*self.flux*np.arange(nSignalPoints)
202 localDataset.photoCharges[ampName] = np.full(nSignalPoints, np.nan)
203 localDataset.gain[ampName] = self.gain
204 localDataset.gainErr[ampName] = 0.1
205 localDataset.noise[ampName] = self.noiseSq
206 localDataset.noiseErr[ampName] = 2.0
207 localDataset.histVars[ampName] = localDataset.rawVars[ampName]
208 localDataset.histChi2Dofs[ampName] = np.full(nSignalPoints, 1.0)
209 localDataset.kspValues[ampName] = np.full(nSignalPoints, 0.5)
211 localDataset.finalVars[ampName] = self.c1*self.flux*np.arange(nSignalPoints)
212 localDataset.finalModelVars[ampName] = np.full(nSignalPoints, 100.0)
213 localDataset.finalMeans[ampName] = self.flux*np.arange(nSignalPoints)
215 if fitType in ['POLYNOMIAL', 'EXPAPPROXIMATION', ]:
216 localDataset.ptcFitPars[ampName] = np.array([10.0, 1.5, 1e-6])
217 localDataset.ptcFitParsError[ampName] = np.array([1.0, 0.2, 1e-7])
218 localDataset.ptcFitChiSq[ampName] = 1.0
219 localDataset.ptcTurnoff[ampName] = localDataset.rawMeans[ampName][-1]
221 localDataset.covariances[ampName] = np.full(
222 (nSignalPoints, nSideCovMatrix, nSideCovMatrix), 105.0)
223 localDataset.covariancesModel[ampName] = np.full(
224 (nSignalPoints, nSideCovMatrix, nSideCovMatrix), np.nan)
225 localDataset.covariancesSqrtWeights[ampName] = np.full((nSignalPoints, nSideCovMatrix,
226 nSideCovMatrix), 10.0)
227 localDataset.aMatrix[ampName] = np.full((nSideCovMatrix, nSideCovMatrix), np.nan)
228 localDataset.bMatrix[ampName] = np.full((nSideCovMatrix, nSideCovMatrix), np.nan)
229 localDataset.covariancesModelNoB[ampName] = np.full((nSignalPoints, nSideCovMatrix,
230 nSideCovMatrix), np.nan)
231 localDataset.aMatrixNoB[ampName] = np.full(
232 (nSideCovMatrix, nSideCovMatrix), np.nan)
234 if localDataset.ptcFitType in ['FULLCOVARIANCE', ]:
235 localDataset.ptcFitPars[ampName] = np.array([np.nan, np.nan])
236 localDataset.ptcFitParsError[ampName] = np.array([np.nan, np.nan])
237 localDataset.ptcFitChiSq[ampName] = np.nan
238 localDataset.ptcTurnoff[ampName] = np.nan
240 localDataset.covariances[ampName] = np.full(
241 (nSignalPoints, nSideCovMatrix, nSideCovMatrix), 105.0)
242 localDataset.covariancesModel[ampName] = np.full(
243 (nSignalPoints, nSideCovMatrix, nSideCovMatrix), 100.0)
244 localDataset.covariancesSqrtWeights[ampName] = np.full((nSignalPoints, nSideCovMatrix,
245 nSideCovMatrix), 10.0)
246 localDataset.aMatrix[ampName] = np.full((nSideCovMatrix, nSideCovMatrix), 1e-6)
247 localDataset.bMatrix[ampName] = np.full((nSideCovMatrix, nSideCovMatrix), 1e-7)
248 localDataset.covariancesModelNoB[ampName] = np.full((nSignalPoints, nSideCovMatrix,
249 nSideCovMatrix), 15.0)
250 localDataset.aMatrixNoB[ampName] = np.full(
251 (nSideCovMatrix, nSideCovMatrix), 2e-6)
253 self._checkTypes(localDataset)
255 with tempfile.NamedTemporaryFile(suffix=".yaml") as f:
256 usedFilename = localDataset.writeText(f.name)
257 fromText = PhotonTransferCurveDataset.readText(usedFilename)
258 self.assertEqual(fromText, localDataset)
259 self._checkTypes(fromText)
261 with tempfile.NamedTemporaryFile(suffix=".fits") as f:
262 usedFilename = localDataset.writeFits(f.name)
263 fromFits = PhotonTransferCurveDataset.readFits(usedFilename)
264 self.assertEqual(fromFits, localDataset)
265 self._checkTypes(fromFits)
267 def test_getExpIdsUsed(self):
268 localDataset = copy.copy(self.dataset)
270 for pair in [(12, 34), (56, 78), (90, 10)]:
271 localDataset.inputExpIdPairs["C:0,0"].append(pair)
272 localDataset.expIdMask["C:0,0"] = np.array([True, False, True])
273 self.assertTrue(np.all(localDataset.getExpIdsUsed("C:0,0") == [(12, 34), (90, 10)]))
275 localDataset.expIdMask["C:0,0"] = np.array([True, False, True, True]) # wrong length now
276 with self.assertRaises(AssertionError):
277 localDataset.getExpIdsUsed("C:0,0")
279 def test_getGoodAmps(self):
280 dataset = self.dataset
282 self.assertTrue(dataset.ampNames == self.ampNames)
283 dataset.badAmps.append("C:0,1")
284 self.assertTrue(dataset.getGoodAmps() == [amp for amp in self.ampNames if amp != "C:0,1"])
286 def test_ptcDataset_pre_dm38309(self):
287 """Test for PTC datasets created by cpSolvePtcTask prior to DM-38309.
288 """
289 localDataset = copy.copy(self.dataset)
291 for pair in [[(12, 34)], [(56, 78)], [(90, 10)]]:
292 localDataset.inputExpIdPairs["C:0,0"].append(pair)
293 localDataset.expIdMask["C:0,0"] = np.array([True, False, True])
295 with self.assertWarnsRegex(RuntimeWarning, "PTC file was written incorrectly"):
296 used = localDataset.getExpIdsUsed("C:0,0")
298 self.assertTrue(np.all(used == [(12, 34), (90, 10)]))
301class MemoryTester(lsst.utils.tests.MemoryTestCase):
302 pass
305def setup_module(module):
306 lsst.utils.tests.init()
309if __name__ == "__main__": 309 ↛ 310line 309 didn't jump to line 310, because the condition on line 309 was never true
310 import sys
311 setup_module(sys.modules[__name__])
312 unittest.main()