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 

162 partialDataset = PhotonTransferCurveDataset( 

163 self.ampNames, 

164 ptcFitType="PARTIAL", 

165 covMatrixSide=nSideCovMatrix 

166 ) 

167 self._checkTypes(partialDataset) 

168 

169 for ampName in partialDataset.ampNames: 

170 partialDataset.setAmpValuesPartialDataset( 

171 ampName, 

172 inputExpIdPair=(10, 11), 

173 rawExpTime=10.0, 

174 rawMean=10.0, 

175 rawVar=10.0, 

176 ) 

177 

178 for useAuxValues in [False, True]: 

179 if useAuxValues: 

180 partialDataset.setAuxValuesPartialDataset( 

181 { 

182 "CCOBCURR": 1.0, 

183 "CCDTEMP": 0.0, 

184 } 

185 ) 

186 self._checkTypes(partialDataset) 

187 

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

189 usedFilename = partialDataset.writeText(f.name) 

190 fromText = PhotonTransferCurveDataset.readText(usedFilename) 

191 self.assertEqual(fromText, partialDataset) 

192 self._checkTypes(fromText) 

193 

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

195 usedFilename = partialDataset.writeFits(f.name) 

196 fromFits = PhotonTransferCurveDataset.readFits(usedFilename) 

197 self.assertEqual(fromFits, partialDataset) 

198 self._checkTypes(fromFits) 

199 

200 def test_ptcDatset(self): 

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

202 # Fill the dataset with made up data. 

203 nSignalPoints = 5 

204 nSideCovMatrix = 2 

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

206 localDataset = PhotonTransferCurveDataset( 

207 self.ampNames, 

208 ptcFitType=fitType, 

209 covMatrixSide=nSideCovMatrix, 

210 ) 

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

212 for ampName in localDataset.ampNames: 

213 

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

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

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

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

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

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

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

221 localDataset.gain[ampName] = self.gain 

222 localDataset.gainErr[ampName] = 0.1 

223 localDataset.noise[ampName] = self.noiseSq 

224 localDataset.noiseErr[ampName] = 2.0 

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

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

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

228 

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

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

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

232 

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

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

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

236 localDataset.ptcFitChiSq[ampName] = 1.0 

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

238 

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

240 (nSignalPoints, nSideCovMatrix, nSideCovMatrix), 105.0) 

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

242 (nSignalPoints, nSideCovMatrix, nSideCovMatrix), np.nan) 

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

244 nSideCovMatrix), 10.0) 

245 localDataset.aMatrix[ampName] = np.full((nSideCovMatrix, nSideCovMatrix), np.nan) 

246 localDataset.bMatrix[ampName] = np.full((nSideCovMatrix, nSideCovMatrix), np.nan) 

247 localDataset.noiseMatrix[ampName] = np.full((nSideCovMatrix, nSideCovMatrix), np.nan) 

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

249 nSideCovMatrix), np.nan) 

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

251 (nSideCovMatrix, nSideCovMatrix), np.nan) 

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

253 (nSideCovMatrix, nSideCovMatrix), np.nan) 

254 

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

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

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

258 localDataset.ptcFitChiSq[ampName] = np.nan 

259 localDataset.ptcTurnoff[ampName] = np.nan 

260 

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

262 (nSignalPoints, nSideCovMatrix, nSideCovMatrix), 105.0) 

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

264 (nSignalPoints, nSideCovMatrix, nSideCovMatrix), 100.0) 

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

266 nSideCovMatrix), 10.0) 

267 localDataset.aMatrix[ampName] = np.full((nSideCovMatrix, nSideCovMatrix), 1e-6) 

268 localDataset.bMatrix[ampName] = np.full((nSideCovMatrix, nSideCovMatrix), 1e-7) 

269 localDataset.noiseMatrix[ampName] = np.full((nSideCovMatrix, nSideCovMatrix), 3.0) 

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

271 nSideCovMatrix), 15.0) 

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

273 (nSideCovMatrix, nSideCovMatrix), 2e-6) 

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

275 (nSideCovMatrix, nSideCovMatrix), 3.0) 

276 

277 for useAuxValues in [False, True]: 

278 if useAuxValues: 

279 localDataset.auxValues = { 

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

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

282 } 

283 

284 self._checkTypes(localDataset) 

285 

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

287 usedFilename = localDataset.writeText(f.name) 

288 fromText = PhotonTransferCurveDataset.readText(usedFilename) 

289 self.assertEqual(fromText, localDataset) 

290 self._checkTypes(fromText) 

291 

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

293 usedFilename = localDataset.writeFits(f.name) 

294 fromFits = PhotonTransferCurveDataset.readFits(usedFilename) 

295 self.assertEqual(fromFits, localDataset) 

296 self._checkTypes(fromFits) 

297 

298 def test_getExpIdsUsed(self): 

299 localDataset = copy.copy(self.dataset) 

300 

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

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

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

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

305 

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

307 with self.assertRaises(AssertionError): 

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

309 

310 def test_getGoodAmps(self): 

311 dataset = self.dataset 

312 

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

314 dataset.badAmps.append("C:0,1") 

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

316 

317 def test_ptcDataset_pre_dm38309(self): 

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

319 """ 

320 localDataset = copy.copy(self.dataset) 

321 

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

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

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

325 

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

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

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

329 

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

331 

332 

333class MemoryTester(lsst.utils.tests.MemoryTestCase): 

334 pass 

335 

336 

337def setup_module(module): 

338 lsst.utils.tests.init() 

339 

340 

341if __name__ == "__main__": 341 ↛ 342line 341 didn't jump to line 342, because the condition on line 341 was never true

342 import sys 

343 setup_module(sys.modules[__name__]) 

344 unittest.main()