Coverage for tests/test_ptcDataset.py: 9%

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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.covariancesModelNoB[ampName], np.ndarray) 

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

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

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

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

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

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

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

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

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

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

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

127 

128 def test_emptyPtcDataset(self): 

129 """Test an empty PTC dataset.""" 

130 emptyDataset = PhotonTransferCurveDataset( 

131 self.ampNames, 

132 ptcFitType="PARTIAL", 

133 ) 

134 self._checkTypes(emptyDataset) 

135 

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

137 usedFilename = emptyDataset.writeText(f.name) 

138 fromText = PhotonTransferCurveDataset.readText(usedFilename) 

139 self.assertEqual(emptyDataset, fromText) 

140 self._checkTypes(emptyDataset) 

141 

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

143 usedFilename = emptyDataset.writeFits(f.name) 

144 fromFits = PhotonTransferCurveDataset.readFits(usedFilename) 

145 self.assertEqual(emptyDataset, fromFits) 

146 self._checkTypes(emptyDataset) 

147 

148 def test_partialPtcDataset(self): 

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

150 # Fill the dataset with made up data. 

151 nSideCovMatrix = 2 

152 

153 partialDataset = PhotonTransferCurveDataset( 

154 self.ampNames, 

155 ptcFitType="PARTIAL", 

156 covMatrixSide=nSideCovMatrix 

157 ) 

158 self._checkTypes(partialDataset) 

159 

160 for ampName in partialDataset.ampNames: 

161 partialDataset.setAmpValuesPartialDataset( 

162 ampName, 

163 inputExpIdPair=(10, 11), 

164 rawExpTime=10.0, 

165 rawMean=10.0, 

166 rawVar=10.0, 

167 ) 

168 self._checkTypes(partialDataset) 

169 

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

171 usedFilename = partialDataset.writeText(f.name) 

172 fromText = PhotonTransferCurveDataset.readText(usedFilename) 

173 self.assertEqual(fromText, partialDataset) 

174 self._checkTypes(fromText) 

175 

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

177 usedFilename = partialDataset.writeFits(f.name) 

178 fromFits = PhotonTransferCurveDataset.readFits(usedFilename) 

179 self.assertEqual(fromFits, partialDataset) 

180 self._checkTypes(fromFits) 

181 

182 def test_ptcDatset(self): 

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

184 # Fill the dataset with made up data. 

185 nSignalPoints = 5 

186 nSideCovMatrix = 2 

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

188 localDataset = PhotonTransferCurveDataset( 

189 self.ampNames, 

190 ptcFitType=fitType, 

191 covMatrixSide=nSideCovMatrix, 

192 ) 

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

194 for ampName in localDataset.ampNames: 

195 

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

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

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

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

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

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

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

203 localDataset.gain[ampName] = self.gain 

204 localDataset.gainErr[ampName] = 0.1 

205 localDataset.noise[ampName] = self.noiseSq 

206 localDataset.noiseErr[ampName] = 2.0 

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

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

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

210 

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

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

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

214 

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

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

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

218 localDataset.ptcFitChiSq[ampName] = 1.0 

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

220 

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

222 (nSignalPoints, nSideCovMatrix, nSideCovMatrix), 105.0) 

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

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

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

226 nSideCovMatrix), 10.0) 

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

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

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

230 nSideCovMatrix), np.nan) 

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

232 (nSideCovMatrix, nSideCovMatrix), np.nan) 

233 

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

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

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

237 localDataset.ptcFitChiSq[ampName] = np.nan 

238 localDataset.ptcTurnoff[ampName] = np.nan 

239 

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

241 (nSignalPoints, nSideCovMatrix, nSideCovMatrix), 105.0) 

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

243 (nSignalPoints, nSideCovMatrix, nSideCovMatrix), 100.0) 

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

245 nSideCovMatrix), 10.0) 

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

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

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

249 nSideCovMatrix), 15.0) 

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

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

252 

253 self._checkTypes(localDataset) 

254 

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

256 usedFilename = localDataset.writeText(f.name) 

257 fromText = PhotonTransferCurveDataset.readText(usedFilename) 

258 self.assertEqual(fromText, localDataset) 

259 self._checkTypes(fromText) 

260 

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

262 usedFilename = localDataset.writeFits(f.name) 

263 fromFits = PhotonTransferCurveDataset.readFits(usedFilename) 

264 self.assertEqual(fromFits, localDataset) 

265 self._checkTypes(fromFits) 

266 

267 def test_getExpIdsUsed(self): 

268 localDataset = copy.copy(self.dataset) 

269 

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

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

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

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

274 

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

276 with self.assertRaises(AssertionError): 

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

278 

279 def test_getGoodAmps(self): 

280 dataset = self.dataset 

281 

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

283 dataset.badAmps.append("C:0,1") 

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

285 

286 def test_ptcDataset_pre_dm38309(self): 

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

288 """ 

289 localDataset = copy.copy(self.dataset) 

290 

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

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

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

294 

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

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

297 

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

299 

300 

301class MemoryTester(lsst.utils.tests.MemoryTestCase): 

302 pass 

303 

304 

305def setup_module(module): 

306 lsst.utils.tests.init() 

307 

308 

309if __name__ == "__main__": 309 ↛ 310line 309 didn't jump to line 310, because the condition on line 309 was never true

310 import sys 

311 setup_module(sys.modules[__name__]) 

312 unittest.main()