Coverage for tests/test_ptcDataset.py: 8%

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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 

24import logging 

25 

26import numpy as np 

27 

28import lsst.utils.tests 

29 

30from lsst.ip.isr import PhotonTransferCurveDataset 

31import lsst.ip.isr.isrMock as isrMock 

32 

33 

34class PtcDatasetCases(lsst.utils.tests.TestCase): 

35 """Test that write/read methods of PhotonTransferCurveDataset work 

36 """ 

37 def setUp(self): 

38 

39 self.flatMean = 2000 

40 self.readNoiseAdu = 10 

41 mockImageConfig = isrMock.IsrMock.ConfigClass() 

42 

43 # flatDrop is not really relevant as we replace the data 

44 # but good to note it in case we change how this image is made 

45 mockImageConfig.flatDrop = 0.99999 

46 mockImageConfig.isTrimmed = True 

47 

48 self.flatExp1 = isrMock.FlatMock(config=mockImageConfig).run() 

49 self.flatExp2 = self.flatExp1.clone() 

50 (shapeY, shapeX) = self.flatExp1.getDimensions() 

51 

52 self.flatWidth = np.sqrt(self.flatMean) + self.readNoiseAdu 

53 

54 self.rng1 = np.random.RandomState(1984) 

55 flatData1 = self.rng1.normal(self.flatMean, self.flatWidth, (shapeX, shapeY)) 

56 self.rng2 = np.random.RandomState(666) 

57 flatData2 = self.rng2.normal(self.flatMean, self.flatWidth, (shapeX, shapeY)) 

58 

59 self.flatExp1.image.array[:] = flatData1 

60 self.flatExp2.image.array[:] = flatData2 

61 

62 self.flux = 1000. # ADU/sec 

63 self.gain = 1.5 # e-/ADU 

64 self.noiseSq = 5*self.gain # 7.5 (e-)^2 

65 self.c1 = 1./self.gain 

66 self.timeVec = np.arange(1., 101., 5) 

67 self.k2NonLinearity = -5e-6 

68 # quadratic signal-chain non-linearity 

69 muVec = self.flux*self.timeVec + self.k2NonLinearity*self.timeVec**2 

70 

71 self.ampNames = [amp.getName() for amp in self.flatExp1.getDetector().getAmplifiers()] 

72 self.dataset = PhotonTransferCurveDataset(self.ampNames, " ") # pack raw data for fitting 

73 self.covariancesSqrtWeights = {} 

74 for ampName in self.ampNames: # just the expTimes and means here - vars vary per function 

75 self.dataset.rawExpTimes[ampName] = self.timeVec 

76 self.dataset.rawMeans[ampName] = muVec 

77 self.covariancesSqrtWeights[ampName] = [] 

78 

79 def _checkTypes(self, ptcDataset): 

80 """Check that all the types are correct for a ptc dataset.""" 

81 for ampName in ptcDataset.ampNames: 

82 self.assertIsInstance(ptcDataset.expIdMask[ampName], np.ndarray) 

83 self.assertEqual(ptcDataset.expIdMask[ampName].dtype, bool) 

84 self.assertIsInstance(ptcDataset.rawExpTimes[ampName], np.ndarray) 

85 self.assertEqual(ptcDataset.rawExpTimes[ampName].dtype, np.float64) 

86 self.assertIsInstance(ptcDataset.rawMeans[ampName], np.ndarray) 

87 self.assertEqual(ptcDataset.rawMeans[ampName].dtype, np.float64) 

88 self.assertIsInstance(ptcDataset.rawVars[ampName], np.ndarray) 

89 self.assertEqual(ptcDataset.rawVars[ampName].dtype, np.float64) 

90 self.assertEqual(ptcDataset.rowMeanVariance[ampName].dtype, np.float64) 

91 self.assertIsInstance(ptcDataset.gain[ampName], float) 

92 self.assertIsInstance(ptcDataset.gainErr[ampName], float) 

93 self.assertIsInstance(ptcDataset.noise[ampName], float) 

94 self.assertIsInstance(ptcDataset.noiseErr[ampName], float) 

95 self.assertIsInstance(ptcDataset.histVars[ampName], np.ndarray) 

96 self.assertEqual(ptcDataset.histVars[ampName].dtype, np.float64) 

97 self.assertIsInstance(ptcDataset.histChi2Dofs[ampName], np.ndarray) 

98 self.assertEqual(ptcDataset.histChi2Dofs[ampName].dtype, np.float64) 

99 self.assertIsInstance(ptcDataset.kspValues[ampName], np.ndarray) 

100 self.assertEqual(ptcDataset.kspValues[ampName].dtype, np.float64) 

101 self.assertIsInstance(ptcDataset.ptcFitPars[ampName], np.ndarray) 

102 self.assertEqual(ptcDataset.ptcFitPars[ampName].dtype, np.float64) 

103 self.assertIsInstance(ptcDataset.ptcFitParsError[ampName], np.ndarray) 

104 self.assertEqual(ptcDataset.ptcFitParsError[ampName].dtype, np.float64) 

105 self.assertIsInstance(ptcDataset.ptcFitChiSq[ampName], float) 

106 self.assertIsInstance(ptcDataset.ptcTurnoff[ampName], float) 

107 self.assertIsInstance(ptcDataset.covariances[ampName], np.ndarray) 

108 self.assertEqual(ptcDataset.covariances[ampName].dtype, np.float64) 

109 self.assertIsInstance(ptcDataset.covariancesModel[ampName], np.ndarray) 

110 self.assertEqual(ptcDataset.covariancesModel[ampName].dtype, np.float64) 

111 self.assertIsInstance(ptcDataset.covariancesSqrtWeights[ampName], np.ndarray) 

112 self.assertEqual(ptcDataset.covariancesSqrtWeights[ampName].dtype, np.float64) 

113 self.assertIsInstance(ptcDataset.aMatrix[ampName], np.ndarray) 

114 self.assertEqual(ptcDataset.aMatrix[ampName].dtype, np.float64) 

115 self.assertIsInstance(ptcDataset.bMatrix[ampName], np.ndarray) 

116 self.assertEqual(ptcDataset.bMatrix[ampName].dtype, np.float64) 

117 self.assertIsInstance(ptcDataset.noiseMatrix[ampName], np.ndarray) 

118 self.assertEqual(ptcDataset.noiseMatrix[ampName].dtype, np.float64) 

119 self.assertIsInstance(ptcDataset.covariancesModelNoB[ampName], np.ndarray) 

120 self.assertEqual(ptcDataset.covariancesModelNoB[ampName].dtype, np.float64) 

121 self.assertIsInstance(ptcDataset.aMatrixNoB[ampName], np.ndarray) 

122 self.assertEqual(ptcDataset.aMatrixNoB[ampName].dtype, np.float64) 

123 self.assertIsInstance(ptcDataset.noiseMatrixNoB[ampName], np.ndarray) 

124 self.assertEqual(ptcDataset.noiseMatrixNoB[ampName].dtype, np.float64) 

125 self.assertIsInstance(ptcDataset.finalVars[ampName], np.ndarray) 

126 self.assertEqual(ptcDataset.finalVars[ampName].dtype, np.float64) 

127 self.assertIsInstance(ptcDataset.finalModelVars[ampName], np.ndarray) 

128 self.assertEqual(ptcDataset.finalModelVars[ampName].dtype, np.float64) 

129 self.assertIsInstance(ptcDataset.finalMeans[ampName], np.ndarray) 

130 self.assertEqual(ptcDataset.finalMeans[ampName].dtype, np.float64) 

131 self.assertIsInstance(ptcDataset.photoCharges[ampName], np.ndarray) 

132 self.assertEqual(ptcDataset.photoCharges[ampName].dtype, np.float64) 

133 

134 for key, value in ptcDataset.auxValues.items(): 

135 self.assertIsInstance(value, np.ndarray) 

136 self.assertEqual(value.dtype, np.float64) 

137 

138 def test_emptyPtcDataset(self): 

139 """Test an empty PTC dataset.""" 

140 emptyDataset = PhotonTransferCurveDataset( 

141 self.ampNames, 

142 ptcFitType="PARTIAL", 

143 ) 

144 self._checkTypes(emptyDataset) 

145 

146 with tempfile.NamedTemporaryFile(suffix=".yaml") as f: 

147 usedFilename = emptyDataset.writeText(f.name) 

148 fromText = PhotonTransferCurveDataset.readText(usedFilename) 

149 self.assertEqual(emptyDataset, fromText) 

150 self._checkTypes(emptyDataset) 

151 

152 with tempfile.NamedTemporaryFile(suffix=".fits") as f: 

153 usedFilename = emptyDataset.writeFits(f.name) 

154 fromFits = PhotonTransferCurveDataset.readFits(usedFilename) 

155 self.assertEqual(emptyDataset, fromFits) 

156 self._checkTypes(emptyDataset) 

157 

158 def test_partialPtcDataset(self): 

159 """Test of a partial PTC dataset.""" 

160 # Fill the dataset with made up data. 

161 nSideCovMatrix = 2 

162 nSideCovMatrixFullCovFit = 2 

163 

164 partialDataset = PhotonTransferCurveDataset( 

165 self.ampNames, 

166 ptcFitType="PARTIAL", 

167 covMatrixSide=nSideCovMatrix, 

168 covMatrixSideFullCovFit=nSideCovMatrixFullCovFit 

169 ) 

170 self._checkTypes(partialDataset) 

171 

172 for ampName in partialDataset.ampNames: 

173 partialDataset.setAmpValuesPartialDataset( 

174 ampName, 

175 inputExpIdPair=(10, 11), 

176 rawExpTime=10.0, 

177 rawMean=10.0, 

178 rawVar=10.0, 

179 ) 

180 

181 for useAuxValues in [False, True]: 

182 if useAuxValues: 

183 partialDataset.setAuxValuesPartialDataset( 

184 { 

185 "CCOBCURR": 1.0, 

186 "CCDTEMP": 0.0, 

187 } 

188 ) 

189 self._checkTypes(partialDataset) 

190 

191 with tempfile.NamedTemporaryFile(suffix=".yaml") as f: 

192 usedFilename = partialDataset.writeText(f.name) 

193 fromText = PhotonTransferCurveDataset.readText(usedFilename) 

194 self.assertEqual(fromText, partialDataset) 

195 self._checkTypes(fromText) 

196 

197 with tempfile.NamedTemporaryFile(suffix=".fits") as f: 

198 usedFilename = partialDataset.writeFits(f.name) 

199 fromFits = PhotonTransferCurveDataset.readFits(usedFilename) 

200 self.assertEqual(fromFits, partialDataset) 

201 self._checkTypes(fromFits) 

202 

203 def test_ptcDatset(self): 

204 """Test of a full PTC dataset.""" 

205 # Fill the dataset with made up data. 

206 nSignalPoints = 5 

207 nSideCovMatrixInput = 3 # Size of measured covariances 

208 

209 for nSideCovMatrixFullCovFitInput in np.arange(1, nSideCovMatrixInput + 2): 

210 for fitType in ['POLYNOMIAL', 'EXPAPPROXIMATION', 'FULLCOVARIANCE']: 

211 localDataset = PhotonTransferCurveDataset( 

212 self.ampNames, 

213 ptcFitType=fitType, 

214 covMatrixSide=nSideCovMatrixInput, 

215 covMatrixSideFullCovFit=nSideCovMatrixFullCovFitInput, 

216 ) 

217 nSideCovMatrix = localDataset.covMatrixSide 

218 nSideCovMatrixFullCovFit = localDataset.covMatrixSideFullCovFit 

219 localDataset.badAmps = [localDataset.ampNames[0], localDataset.ampNames[1]] 

220 for ampName in localDataset.ampNames: 

221 

222 localDataset.inputExpIdPairs[ampName] = [(1, 2)]*nSignalPoints 

223 localDataset.expIdMask[ampName] = np.ones(nSignalPoints, dtype=bool) 

224 localDataset.expIdMask[ampName][1] = False 

225 localDataset.rawExpTimes[ampName] = np.arange(nSignalPoints, dtype=np.float64) 

226 localDataset.rawMeans[ampName] = self.flux*np.arange(nSignalPoints) 

227 localDataset.rawVars[ampName] = self.c1*self.flux*np.arange(nSignalPoints) 

228 localDataset.photoCharges[ampName] = np.full(nSignalPoints, np.nan) 

229 localDataset.gain[ampName] = self.gain 

230 localDataset.gainErr[ampName] = 0.1 

231 localDataset.noise[ampName] = self.noiseSq 

232 localDataset.noiseErr[ampName] = 2.0 

233 localDataset.histVars[ampName] = localDataset.rawVars[ampName] 

234 localDataset.histChi2Dofs[ampName] = np.full(nSignalPoints, 1.0) 

235 localDataset.kspValues[ampName] = np.full(nSignalPoints, 0.5) 

236 

237 localDataset.finalVars[ampName] = self.c1*self.flux*np.arange(nSignalPoints) 

238 localDataset.finalModelVars[ampName] = np.full(nSignalPoints, 100.0) 

239 localDataset.finalMeans[ampName] = self.flux*np.arange(nSignalPoints) 

240 

241 if fitType in ['POLYNOMIAL', 'EXPAPPROXIMATION', ]: 

242 localDataset.ptcFitPars[ampName] = np.array([10.0, 1.5, 1e-6]) 

243 localDataset.ptcFitParsError[ampName] = np.array([1.0, 0.2, 1e-7]) 

244 localDataset.ptcFitChiSq[ampName] = 1.0 

245 localDataset.ptcTurnoff[ampName] = localDataset.rawMeans[ampName][-1] 

246 

247 localDataset.covariances[ampName] = np.full( 

248 (nSignalPoints, nSideCovMatrix, nSideCovMatrix), 105.0) 

249 localDataset.covariancesModel[ampName] = np.full( 

250 (nSignalPoints, nSideCovMatrixFullCovFit, nSideCovMatrixFullCovFit), np.nan) 

251 localDataset.covariancesSqrtWeights[ampName] = np.full((nSignalPoints, nSideCovMatrix, 

252 nSideCovMatrix), 10.0) 

253 localDataset.aMatrix[ampName] = np.full((nSideCovMatrixFullCovFit, 

254 nSideCovMatrixFullCovFit), np.nan) 

255 localDataset.bMatrix[ampName] = np.full((nSideCovMatrixFullCovFit, 

256 nSideCovMatrixFullCovFit), np.nan) 

257 localDataset.noiseMatrix[ampName] = np.full((nSideCovMatrixFullCovFit, 

258 nSideCovMatrixFullCovFit), np.nan) 

259 localDataset.covariancesModelNoB[ampName] = np.full((nSignalPoints, 

260 nSideCovMatrixFullCovFit, 

261 nSideCovMatrixFullCovFit), np.nan) 

262 localDataset.aMatrixNoB[ampName] = np.full( 

263 (nSideCovMatrixFullCovFit, nSideCovMatrixFullCovFit), np.nan) 

264 localDataset.noiseMatrixNoB[ampName] = np.full( 

265 (nSideCovMatrixFullCovFit, nSideCovMatrixFullCovFit), np.nan) 

266 

267 if localDataset.ptcFitType in ['FULLCOVARIANCE', ]: 

268 localDataset.ptcFitPars[ampName] = np.array([np.nan, np.nan]) 

269 localDataset.ptcFitParsError[ampName] = np.array([np.nan, np.nan]) 

270 localDataset.ptcFitChiSq[ampName] = np.nan 

271 localDataset.ptcTurnoff[ampName] = np.nan 

272 

273 localDataset.covariances[ampName] = np.full( 

274 (nSignalPoints, nSideCovMatrix, nSideCovMatrix), 105.0) 

275 localDataset.covariancesModel[ampName] = np.full( 

276 (nSignalPoints, nSideCovMatrixFullCovFit, nSideCovMatrixFullCovFit), 100.0) 

277 localDataset.covariancesSqrtWeights[ampName] = np.full((nSignalPoints, nSideCovMatrix, 

278 nSideCovMatrix), 10.0) 

279 localDataset.aMatrix[ampName] = np.full((nSideCovMatrixFullCovFit, 

280 nSideCovMatrixFullCovFit), 1e-6) 

281 localDataset.bMatrix[ampName] = np.full((nSideCovMatrixFullCovFit, 

282 nSideCovMatrixFullCovFit), 1e-7) 

283 localDataset.noiseMatrix[ampName] = np.full((nSideCovMatrixFullCovFit, 

284 nSideCovMatrixFullCovFit), 3.0) 

285 localDataset.covariancesModelNoB[ampName] = np.full((nSignalPoints, 

286 nSideCovMatrixFullCovFit, 

287 nSideCovMatrixFullCovFit), 15.0) 

288 localDataset.aMatrixNoB[ampName] = np.full( 

289 (nSideCovMatrixFullCovFit, nSideCovMatrixFullCovFit), 2e-6) 

290 localDataset.noiseMatrixNoB[ampName] = np.full( 

291 (nSideCovMatrixFullCovFit, nSideCovMatrixFullCovFit), 3.0) 

292 

293 for useAuxValues in [False, True]: 

294 if useAuxValues: 

295 localDataset.auxValues = { 

296 "CCOBCURR": np.ones(nSignalPoints), 

297 "CCDTEMP": np.zeros(nSignalPoints), 

298 } 

299 

300 self._checkTypes(localDataset) 

301 with tempfile.NamedTemporaryFile(suffix=".yaml") as f: 

302 usedFilename = localDataset.writeText(f.name) 

303 fromText = PhotonTransferCurveDataset.readText(usedFilename) 

304 self.assertEqual(fromText, localDataset) 

305 self._checkTypes(fromText) 

306 

307 with tempfile.NamedTemporaryFile(suffix=".fits") as f: 

308 usedFilename = localDataset.writeFits(f.name) 

309 fromFits = PhotonTransferCurveDataset.readFits(usedFilename) 

310 self.assertEqual(fromFits, localDataset) 

311 self._checkTypes(fromFits) 

312 

313 def test_getExpIdsUsed(self): 

314 localDataset = copy.copy(self.dataset) 

315 

316 for pair in [(12, 34), (56, 78), (90, 10)]: 

317 localDataset.inputExpIdPairs["C:0,0"].append(pair) 

318 localDataset.expIdMask["C:0,0"] = np.array([True, False, True]) 

319 self.assertTrue(np.all(localDataset.getExpIdsUsed("C:0,0") == [(12, 34), (90, 10)])) 

320 

321 localDataset.expIdMask["C:0,0"] = np.array([True, False, True, True]) # wrong length now 

322 with self.assertRaises(AssertionError): 

323 localDataset.getExpIdsUsed("C:0,0") 

324 

325 def test_getGoodAmps(self): 

326 dataset = self.dataset 

327 

328 self.assertTrue(dataset.ampNames == self.ampNames) 

329 dataset.badAmps.append("C:0,1") 

330 self.assertTrue(dataset.getGoodAmps() == [amp for amp in self.ampNames if amp != "C:0,1"]) 

331 

332 def test_ptcDataset_pre_dm38309(self): 

333 """Test for PTC datasets created by cpSolvePtcTask prior to DM-38309. 

334 """ 

335 localDataset = copy.copy(self.dataset) 

336 

337 for pair in [[(12, 34)], [(56, 78)], [(90, 10)]]: 

338 localDataset.inputExpIdPairs["C:0,0"].append(pair) 

339 localDataset.expIdMask["C:0,0"] = np.array([True, False, True]) 

340 

341 with self.assertLogs("lsst.ip.isr.calibType", logging.WARNING) as cm: 

342 used = localDataset.getExpIdsUsed("C:0,0") 

343 self.assertIn("PTC file was written incorrectly", cm.output[0]) 

344 

345 self.assertTrue(np.all(used == [(12, 34), (90, 10)])) 

346 

347 

348class MemoryTester(lsst.utils.tests.MemoryTestCase): 

349 pass 

350 

351 

352def setup_module(module): 

353 lsst.utils.tests.init() 

354 

355 

356if __name__ == "__main__": 356 ↛ 357line 356 didn't jump to line 357, because the condition on line 356 was never true

357 import sys 

358 setup_module(sys.modules[__name__]) 

359 unittest.main()