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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

132 

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

134 self.assertIsInstance(value, np.ndarray) 

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

136 

137 def test_emptyPtcDataset(self): 

138 """Test an empty PTC dataset.""" 

139 emptyDataset = PhotonTransferCurveDataset( 

140 self.ampNames, 

141 ptcFitType="PARTIAL", 

142 ) 

143 self._checkTypes(emptyDataset) 

144 

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

146 usedFilename = emptyDataset.writeText(f.name) 

147 fromText = PhotonTransferCurveDataset.readText(usedFilename) 

148 self.assertEqual(emptyDataset, fromText) 

149 self._checkTypes(emptyDataset) 

150 

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

152 usedFilename = emptyDataset.writeFits(f.name) 

153 fromFits = PhotonTransferCurveDataset.readFits(usedFilename) 

154 self.assertEqual(emptyDataset, fromFits) 

155 self._checkTypes(emptyDataset) 

156 

157 def test_partialPtcDataset(self): 

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

159 # Fill the dataset with made up data. 

160 nSideCovMatrix = 2 

161 nSideCovMatrixFullCovFit = 2 

162 

163 partialDataset = PhotonTransferCurveDataset( 

164 self.ampNames, 

165 ptcFitType="PARTIAL", 

166 covMatrixSide=nSideCovMatrix, 

167 covMatrixSideFullCovFit=nSideCovMatrixFullCovFit 

168 ) 

169 self._checkTypes(partialDataset) 

170 

171 for ampName in partialDataset.ampNames: 

172 partialDataset.setAmpValuesPartialDataset( 

173 ampName, 

174 inputExpIdPair=(10, 11), 

175 rawExpTime=10.0, 

176 rawMean=10.0, 

177 rawVar=10.0, 

178 ) 

179 

180 for useAuxValues in [False, True]: 

181 if useAuxValues: 

182 partialDataset.setAuxValuesPartialDataset( 

183 { 

184 "CCOBCURR": 1.0, 

185 "CCDTEMP": 0.0, 

186 } 

187 ) 

188 self._checkTypes(partialDataset) 

189 

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

191 usedFilename = partialDataset.writeText(f.name) 

192 fromText = PhotonTransferCurveDataset.readText(usedFilename) 

193 self.assertEqual(fromText, partialDataset) 

194 self._checkTypes(fromText) 

195 

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

197 usedFilename = partialDataset.writeFits(f.name) 

198 fromFits = PhotonTransferCurveDataset.readFits(usedFilename) 

199 self.assertEqual(fromFits, partialDataset) 

200 self._checkTypes(fromFits) 

201 

202 def test_ptcDatset(self): 

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

204 # Fill the dataset with made up data. 

205 nSignalPoints = 5 

206 nSideCovMatrixInput = 3 # Size of measured covariances 

207 

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

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

210 localDataset = PhotonTransferCurveDataset( 

211 self.ampNames, 

212 ptcFitType=fitType, 

213 covMatrixSide=nSideCovMatrixInput, 

214 covMatrixSideFullCovFit=nSideCovMatrixFullCovFitInput, 

215 ) 

216 nSideCovMatrix = localDataset.covMatrixSide 

217 nSideCovMatrixFullCovFit = localDataset.covMatrixSideFullCovFit 

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

219 for ampName in localDataset.ampNames: 

220 

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

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

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

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

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

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

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

228 localDataset.gain[ampName] = self.gain 

229 localDataset.gainErr[ampName] = 0.1 

230 localDataset.noise[ampName] = self.noiseSq 

231 localDataset.noiseErr[ampName] = 2.0 

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

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

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

235 

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

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

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

239 

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

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

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

243 localDataset.ptcFitChiSq[ampName] = 1.0 

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

245 

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

247 (nSignalPoints, nSideCovMatrix, nSideCovMatrix), 105.0) 

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

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

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

251 nSideCovMatrix), 10.0) 

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

253 nSideCovMatrixFullCovFit), np.nan) 

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

255 nSideCovMatrixFullCovFit), np.nan) 

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

257 nSideCovMatrixFullCovFit), np.nan) 

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

259 nSideCovMatrixFullCovFit, 

260 nSideCovMatrixFullCovFit), np.nan) 

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

262 (nSideCovMatrixFullCovFit, nSideCovMatrixFullCovFit), np.nan) 

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

264 (nSideCovMatrixFullCovFit, nSideCovMatrixFullCovFit), np.nan) 

265 

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

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

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

269 localDataset.ptcFitChiSq[ampName] = np.nan 

270 localDataset.ptcTurnoff[ampName] = np.nan 

271 

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

273 (nSignalPoints, nSideCovMatrix, nSideCovMatrix), 105.0) 

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

275 (nSignalPoints, nSideCovMatrixFullCovFit, nSideCovMatrixFullCovFit), 100.0) 

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

277 nSideCovMatrix), 10.0) 

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

279 nSideCovMatrixFullCovFit), 1e-6) 

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

281 nSideCovMatrixFullCovFit), 1e-7) 

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

283 nSideCovMatrixFullCovFit), 3.0) 

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

285 nSideCovMatrixFullCovFit, 

286 nSideCovMatrixFullCovFit), 15.0) 

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

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

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

290 (nSideCovMatrixFullCovFit, nSideCovMatrixFullCovFit), 3.0) 

291 

292 for useAuxValues in [False, True]: 

293 if useAuxValues: 

294 localDataset.auxValues = { 

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

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

297 } 

298 

299 self._checkTypes(localDataset) 

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

301 usedFilename = localDataset.writeText(f.name) 

302 fromText = PhotonTransferCurveDataset.readText(usedFilename) 

303 self.assertEqual(fromText, localDataset) 

304 self._checkTypes(fromText) 

305 

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

307 usedFilename = localDataset.writeFits(f.name) 

308 fromFits = PhotonTransferCurveDataset.readFits(usedFilename) 

309 self.assertEqual(fromFits, localDataset) 

310 self._checkTypes(fromFits) 

311 

312 def test_getExpIdsUsed(self): 

313 localDataset = copy.copy(self.dataset) 

314 

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

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

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

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

319 

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

321 with self.assertRaises(AssertionError): 

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

323 

324 def test_getGoodAmps(self): 

325 dataset = self.dataset 

326 

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

328 dataset.badAmps.append("C:0,1") 

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

330 

331 def test_ptcDataset_pre_dm38309(self): 

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

333 """ 

334 localDataset = copy.copy(self.dataset) 

335 

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

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

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

339 

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

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

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

343 

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

345 

346 

347class MemoryTester(lsst.utils.tests.MemoryTestCase): 

348 pass 

349 

350 

351def setup_module(module): 

352 lsst.utils.tests.init() 

353 

354 

355if __name__ == "__main__": 355 ↛ 356line 355 didn't jump to line 356, because the condition on line 355 was never true

356 import sys 

357 setup_module(sys.modules[__name__]) 

358 unittest.main()