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.ptcFitPars[ampName], np.ndarray) 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

121 

122 def test_emptyPtcDataset(self): 

123 """Test an empty PTC dataset.""" 

124 emptyDataset = PhotonTransferCurveDataset( 

125 self.ampNames, 

126 ptcFitType="PARTIAL", 

127 ) 

128 self._checkTypes(emptyDataset) 

129 

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

131 usedFilename = emptyDataset.writeText(f.name) 

132 fromText = PhotonTransferCurveDataset.readText(usedFilename) 

133 self.assertEqual(emptyDataset, fromText) 

134 self._checkTypes(emptyDataset) 

135 

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

137 usedFilename = emptyDataset.writeFits(f.name) 

138 fromFits = PhotonTransferCurveDataset.readFits(usedFilename) 

139 self.assertEqual(emptyDataset, fromFits) 

140 self._checkTypes(emptyDataset) 

141 

142 def test_partialPtcDataset(self): 

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

144 # Fill the dataset with made up data. 

145 nSideCovMatrix = 2 

146 

147 partialDataset = PhotonTransferCurveDataset( 

148 self.ampNames, 

149 ptcFitType="PARTIAL", 

150 covMatrixSide=nSideCovMatrix 

151 ) 

152 self._checkTypes(partialDataset) 

153 

154 for ampName in partialDataset.ampNames: 

155 partialDataset.setAmpValuesPartialDataset( 

156 ampName, 

157 inputExpIdPair=(10, 11), 

158 rawExpTime=10.0, 

159 rawMean=10.0, 

160 rawVar=10.0, 

161 ) 

162 self._checkTypes(partialDataset) 

163 

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

165 usedFilename = partialDataset.writeText(f.name) 

166 fromText = PhotonTransferCurveDataset.readText(usedFilename) 

167 self.assertEqual(fromText, partialDataset) 

168 self._checkTypes(fromText) 

169 

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

171 usedFilename = partialDataset.writeFits(f.name) 

172 fromFits = PhotonTransferCurveDataset.readFits(usedFilename) 

173 self.assertEqual(fromFits, partialDataset) 

174 self._checkTypes(fromFits) 

175 

176 def test_ptcDatset(self): 

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

178 # Fill the dataset with made up data. 

179 nSignalPoints = 5 

180 nSideCovMatrix = 2 

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

182 localDataset = PhotonTransferCurveDataset( 

183 self.ampNames, 

184 ptcFitType=fitType, 

185 covMatrixSide=nSideCovMatrix, 

186 ) 

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

188 for ampName in localDataset.ampNames: 

189 

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

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

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

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

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

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

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

197 localDataset.gain[ampName] = self.gain 

198 localDataset.gainErr[ampName] = 0.1 

199 localDataset.noise[ampName] = self.noiseSq 

200 localDataset.noiseErr[ampName] = 2.0 

201 

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

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

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

205 

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

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

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

209 localDataset.ptcFitChiSq[ampName] = 1.0 

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

211 

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

213 (nSignalPoints, nSideCovMatrix, nSideCovMatrix), 105.0) 

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

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

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

217 nSideCovMatrix), 10.0) 

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

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

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

221 nSideCovMatrix), np.nan) 

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

223 (nSideCovMatrix, nSideCovMatrix), np.nan) 

224 

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

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

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

228 localDataset.ptcFitChiSq[ampName] = np.nan 

229 localDataset.ptcTurnoff[ampName] = np.nan 

230 

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

232 (nSignalPoints, nSideCovMatrix, nSideCovMatrix), 105.0) 

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

234 (nSignalPoints, nSideCovMatrix, nSideCovMatrix), 100.0) 

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

236 nSideCovMatrix), 10.0) 

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

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

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

240 nSideCovMatrix), 15.0) 

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

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

243 

244 self._checkTypes(localDataset) 

245 

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

247 usedFilename = localDataset.writeText(f.name) 

248 fromText = PhotonTransferCurveDataset.readText(usedFilename) 

249 self.assertEqual(fromText, localDataset) 

250 self._checkTypes(fromText) 

251 

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

253 usedFilename = localDataset.writeFits(f.name) 

254 fromFits = PhotonTransferCurveDataset.readFits(usedFilename) 

255 self.assertEqual(fromFits, localDataset) 

256 self._checkTypes(fromFits) 

257 

258 def test_getExpIdsUsed(self): 

259 localDataset = copy.copy(self.dataset) 

260 

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

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

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

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

265 

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

267 with self.assertRaises(AssertionError): 

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

269 

270 def test_getGoodAmps(self): 

271 dataset = self.dataset 

272 

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

274 dataset.badAmps.append("C:0,1") 

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

276 

277 def test_ptcDataset_pre_dm38309(self): 

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

279 """ 

280 localDataset = copy.copy(self.dataset) 

281 

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

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

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

285 

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

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

288 

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

290 

291 

292class MemoryTester(lsst.utils.tests.MemoryTestCase): 

293 pass 

294 

295 

296def setup_module(module): 

297 lsst.utils.tests.init() 

298 

299 

300if __name__ == "__main__": 300 ↛ 301line 300 didn't jump to line 301, because the condition on line 300 was never true

301 import sys 

302 setup_module(sys.modules[__name__]) 

303 unittest.main()