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 

24 

25import numpy as np 

26 

27import lsst.utils.tests 

28 

29from lsst.ip.isr import PhotonTransferCurveDataset 

30import lsst.ip.isr.isrMock as isrMock 

31 

32 

33class PtcDatasetCases(lsst.utils.tests.TestCase): 

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

35 """ 

36 def setUp(self): 

37 

38 self.flatMean = 2000 

39 self.readNoiseAdu = 10 

40 mockImageConfig = isrMock.IsrMock.ConfigClass() 

41 

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 

46 

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

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

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

50 

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

52 

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)) 

57 

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

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

60 

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 

69 

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] = [] 

77 

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) 

131 

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

133 self.assertIsInstance(value, np.ndarray) 

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

135 

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) 

143 

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) 

149 

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) 

155 

156 def test_partialPtcDataset(self): 

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

158 # Fill the dataset with made up data. 

159 nSideCovMatrix = 2 

160 

161 partialDataset = PhotonTransferCurveDataset( 

162 self.ampNames, 

163 ptcFitType="PARTIAL", 

164 covMatrixSide=nSideCovMatrix 

165 ) 

166 self._checkTypes(partialDataset) 

167 

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 ) 

176 

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) 

186 

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) 

192 

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) 

198 

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: 

212 

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) 

227 

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) 

231 

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] 

237 

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) 

253 

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 

259 

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) 

275 

276 for useAuxValues in [False, True]: 

277 if useAuxValues: 

278 localDataset.auxValues = { 

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

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

281 } 

282 

283 self._checkTypes(localDataset) 

284 

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) 

290 

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) 

296 

297 def test_getExpIdsUsed(self): 

298 localDataset = copy.copy(self.dataset) 

299 

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)])) 

304 

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") 

308 

309 def test_getGoodAmps(self): 

310 dataset = self.dataset 

311 

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"]) 

315 

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) 

320 

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]) 

324 

325 with self.assertWarnsRegex(RuntimeWarning, "PTC file was written incorrectly"): 

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

327 

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

329 

330 

331class MemoryTester(lsst.utils.tests.MemoryTestCase): 

332 pass 

333 

334 

335def setup_module(module): 

336 lsst.utils.tests.init() 

337 

338 

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()