Coverage for tests/test_ptcDataset.py: 8%
222 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.
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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 _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.noiseMatrix[ampName], np.ndarray)
116 self.assertEqual(ptcDataset.noiseMatrix[ampName].dtype, np.float64)
117 self.assertIsInstance(ptcDataset.covariancesModelNoB[ampName], np.ndarray)
118 self.assertEqual(ptcDataset.covariancesModelNoB[ampName].dtype, np.float64)
119 self.assertIsInstance(ptcDataset.aMatrixNoB[ampName], np.ndarray)
120 self.assertEqual(ptcDataset.aMatrixNoB[ampName].dtype, np.float64)
121 self.assertIsInstance(ptcDataset.noiseMatrixNoB[ampName], np.ndarray)
122 self.assertEqual(ptcDataset.noiseMatrixNoB[ampName].dtype, np.float64)
123 self.assertIsInstance(ptcDataset.finalVars[ampName], np.ndarray)
124 self.assertEqual(ptcDataset.finalVars[ampName].dtype, np.float64)
125 self.assertIsInstance(ptcDataset.finalModelVars[ampName], np.ndarray)
126 self.assertEqual(ptcDataset.finalModelVars[ampName].dtype, np.float64)
127 self.assertIsInstance(ptcDataset.finalMeans[ampName], np.ndarray)
128 self.assertEqual(ptcDataset.finalMeans[ampName].dtype, np.float64)
129 self.assertIsInstance(ptcDataset.photoCharges[ampName], np.ndarray)
130 self.assertEqual(ptcDataset.photoCharges[ampName].dtype, np.float64)
132 for key, value in ptcDataset.auxValues.items():
133 self.assertIsInstance(value, np.ndarray)
134 self.assertEqual(value.dtype, np.float64)
136 def test_emptyPtcDataset(self):
137 """Test an empty PTC dataset."""
138 emptyDataset = PhotonTransferCurveDataset(
139 self.ampNames,
140 ptcFitType="PARTIAL",
141 )
142 self._checkTypes(emptyDataset)
144 with tempfile.NamedTemporaryFile(suffix=".yaml") as f:
145 usedFilename = emptyDataset.writeText(f.name)
146 fromText = PhotonTransferCurveDataset.readText(usedFilename)
147 self.assertEqual(emptyDataset, fromText)
148 self._checkTypes(emptyDataset)
150 with tempfile.NamedTemporaryFile(suffix=".fits") as f:
151 usedFilename = emptyDataset.writeFits(f.name)
152 fromFits = PhotonTransferCurveDataset.readFits(usedFilename)
153 self.assertEqual(emptyDataset, fromFits)
154 self._checkTypes(emptyDataset)
156 def test_partialPtcDataset(self):
157 """Test of a partial PTC dataset."""
158 # Fill the dataset with made up data.
159 nSideCovMatrix = 2
161 partialDataset = PhotonTransferCurveDataset(
162 self.ampNames,
163 ptcFitType="PARTIAL",
164 covMatrixSide=nSideCovMatrix
165 )
166 self._checkTypes(partialDataset)
168 for ampName in partialDataset.ampNames:
169 partialDataset.setAmpValuesPartialDataset(
170 ampName,
171 inputExpIdPair=(10, 11),
172 rawExpTime=10.0,
173 rawMean=10.0,
174 rawVar=10.0,
175 )
177 for useAuxValues in [False, True]:
178 if useAuxValues:
179 partialDataset.setAuxValuesPartialDataset(
180 {
181 "CCOBCURR": 1.0,
182 "CCDTEMP": 0.0,
183 }
184 )
185 self._checkTypes(partialDataset)
187 with tempfile.NamedTemporaryFile(suffix=".yaml") as f:
188 usedFilename = partialDataset.writeText(f.name)
189 fromText = PhotonTransferCurveDataset.readText(usedFilename)
190 self.assertEqual(fromText, partialDataset)
191 self._checkTypes(fromText)
193 with tempfile.NamedTemporaryFile(suffix=".fits") as f:
194 usedFilename = partialDataset.writeFits(f.name)
195 fromFits = PhotonTransferCurveDataset.readFits(usedFilename)
196 self.assertEqual(fromFits, partialDataset)
197 self._checkTypes(fromFits)
199 def test_ptcDatset(self):
200 """Test of a full PTC dataset."""
201 # Fill the dataset with made up data.
202 nSignalPoints = 5
203 nSideCovMatrix = 2
204 for fitType in ['POLYNOMIAL', 'EXPAPPROXIMATION', 'FULLCOVARIANCE']:
205 localDataset = PhotonTransferCurveDataset(
206 self.ampNames,
207 ptcFitType=fitType,
208 covMatrixSide=nSideCovMatrix,
209 )
210 localDataset.badAmps = [localDataset.ampNames[0], localDataset.ampNames[1]]
211 for ampName in localDataset.ampNames:
213 localDataset.inputExpIdPairs[ampName] = [(1, 2)]*nSignalPoints
214 localDataset.expIdMask[ampName] = np.ones(nSignalPoints, dtype=bool)
215 localDataset.expIdMask[ampName][1] = False
216 localDataset.rawExpTimes[ampName] = np.arange(nSignalPoints, dtype=np.float64)
217 localDataset.rawMeans[ampName] = self.flux*np.arange(nSignalPoints)
218 localDataset.rawVars[ampName] = self.c1*self.flux*np.arange(nSignalPoints)
219 localDataset.photoCharges[ampName] = np.full(nSignalPoints, np.nan)
220 localDataset.gain[ampName] = self.gain
221 localDataset.gainErr[ampName] = 0.1
222 localDataset.noise[ampName] = self.noiseSq
223 localDataset.noiseErr[ampName] = 2.0
224 localDataset.histVars[ampName] = localDataset.rawVars[ampName]
225 localDataset.histChi2Dofs[ampName] = np.full(nSignalPoints, 1.0)
226 localDataset.kspValues[ampName] = np.full(nSignalPoints, 0.5)
228 localDataset.finalVars[ampName] = self.c1*self.flux*np.arange(nSignalPoints)
229 localDataset.finalModelVars[ampName] = np.full(nSignalPoints, 100.0)
230 localDataset.finalMeans[ampName] = self.flux*np.arange(nSignalPoints)
232 if fitType in ['POLYNOMIAL', 'EXPAPPROXIMATION', ]:
233 localDataset.ptcFitPars[ampName] = np.array([10.0, 1.5, 1e-6])
234 localDataset.ptcFitParsError[ampName] = np.array([1.0, 0.2, 1e-7])
235 localDataset.ptcFitChiSq[ampName] = 1.0
236 localDataset.ptcTurnoff[ampName] = localDataset.rawMeans[ampName][-1]
238 localDataset.covariances[ampName] = np.full(
239 (nSignalPoints, nSideCovMatrix, nSideCovMatrix), 105.0)
240 localDataset.covariancesModel[ampName] = np.full(
241 (nSignalPoints, nSideCovMatrix, nSideCovMatrix), np.nan)
242 localDataset.covariancesSqrtWeights[ampName] = np.full((nSignalPoints, nSideCovMatrix,
243 nSideCovMatrix), 10.0)
244 localDataset.aMatrix[ampName] = np.full((nSideCovMatrix, nSideCovMatrix), np.nan)
245 localDataset.bMatrix[ampName] = np.full((nSideCovMatrix, nSideCovMatrix), np.nan)
246 localDataset.noiseMatrix[ampName] = np.full((nSideCovMatrix, nSideCovMatrix), np.nan)
247 localDataset.covariancesModelNoB[ampName] = np.full((nSignalPoints, nSideCovMatrix,
248 nSideCovMatrix), np.nan)
249 localDataset.aMatrixNoB[ampName] = np.full(
250 (nSideCovMatrix, nSideCovMatrix), np.nan)
251 localDataset.noiseMatrixNoB[ampName] = np.full(
252 (nSideCovMatrix, nSideCovMatrix), np.nan)
254 if localDataset.ptcFitType in ['FULLCOVARIANCE', ]:
255 localDataset.ptcFitPars[ampName] = np.array([np.nan, np.nan])
256 localDataset.ptcFitParsError[ampName] = np.array([np.nan, np.nan])
257 localDataset.ptcFitChiSq[ampName] = np.nan
258 localDataset.ptcTurnoff[ampName] = np.nan
260 localDataset.covariances[ampName] = np.full(
261 (nSignalPoints, nSideCovMatrix, nSideCovMatrix), 105.0)
262 localDataset.covariancesModel[ampName] = np.full(
263 (nSignalPoints, nSideCovMatrix, nSideCovMatrix), 100.0)
264 localDataset.covariancesSqrtWeights[ampName] = np.full((nSignalPoints, nSideCovMatrix,
265 nSideCovMatrix), 10.0)
266 localDataset.aMatrix[ampName] = np.full((nSideCovMatrix, nSideCovMatrix), 1e-6)
267 localDataset.bMatrix[ampName] = np.full((nSideCovMatrix, nSideCovMatrix), 1e-7)
268 localDataset.noiseMatrix[ampName] = np.full((nSideCovMatrix, nSideCovMatrix), 3.0)
269 localDataset.covariancesModelNoB[ampName] = np.full((nSignalPoints, nSideCovMatrix,
270 nSideCovMatrix), 15.0)
271 localDataset.aMatrixNoB[ampName] = np.full(
272 (nSideCovMatrix, nSideCovMatrix), 2e-6)
273 localDataset.noiseMatrixNoB[ampName] = np.full(
274 (nSideCovMatrix, nSideCovMatrix), 3.0)
276 for useAuxValues in [False, True]:
277 if useAuxValues:
278 localDataset.auxValues = {
279 "CCOBCURR": np.ones(nSignalPoints),
280 "CCDTEMP": np.zeros(nSignalPoints),
281 }
283 self._checkTypes(localDataset)
285 with tempfile.NamedTemporaryFile(suffix=".yaml") as f:
286 usedFilename = localDataset.writeText(f.name)
287 fromText = PhotonTransferCurveDataset.readText(usedFilename)
288 self.assertEqual(fromText, localDataset)
289 self._checkTypes(fromText)
291 with tempfile.NamedTemporaryFile(suffix=".fits") as f:
292 usedFilename = localDataset.writeFits(f.name)
293 fromFits = PhotonTransferCurveDataset.readFits(usedFilename)
294 self.assertEqual(fromFits, localDataset)
295 self._checkTypes(fromFits)
297 def test_getExpIdsUsed(self):
298 localDataset = copy.copy(self.dataset)
300 for pair in [(12, 34), (56, 78), (90, 10)]:
301 localDataset.inputExpIdPairs["C:0,0"].append(pair)
302 localDataset.expIdMask["C:0,0"] = np.array([True, False, True])
303 self.assertTrue(np.all(localDataset.getExpIdsUsed("C:0,0") == [(12, 34), (90, 10)]))
305 localDataset.expIdMask["C:0,0"] = np.array([True, False, True, True]) # wrong length now
306 with self.assertRaises(AssertionError):
307 localDataset.getExpIdsUsed("C:0,0")
309 def test_getGoodAmps(self):
310 dataset = self.dataset
312 self.assertTrue(dataset.ampNames == self.ampNames)
313 dataset.badAmps.append("C:0,1")
314 self.assertTrue(dataset.getGoodAmps() == [amp for amp in self.ampNames if amp != "C:0,1"])
316 def test_ptcDataset_pre_dm38309(self):
317 """Test for PTC datasets created by cpSolvePtcTask prior to DM-38309.
318 """
319 localDataset = copy.copy(self.dataset)
321 for pair in [[(12, 34)], [(56, 78)], [(90, 10)]]:
322 localDataset.inputExpIdPairs["C:0,0"].append(pair)
323 localDataset.expIdMask["C:0,0"] = np.array([True, False, True])
325 with self.assertWarnsRegex(RuntimeWarning, "PTC file was written incorrectly"):
326 used = localDataset.getExpIdsUsed("C:0,0")
328 self.assertTrue(np.all(used == [(12, 34), (90, 10)]))
331class MemoryTester(lsst.utils.tests.MemoryTestCase):
332 pass
335def setup_module(module):
336 lsst.utils.tests.init()
339if __name__ == "__main__": 339 ↛ 340line 339 didn't jump to line 340, because the condition on line 339 was never true
340 import sys
341 setup_module(sys.modules[__name__])
342 unittest.main()