Coverage for tests/test_ptcDataset.py: 8%
224 statements
« prev ^ index » next coverage.py v7.3.2, created at 2023-12-08 11:41 +0000
« prev ^ index » next coverage.py v7.3.2, created at 2023-12-08 11:41 +0000
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
26import numpy as np
28import lsst.utils.tests
30from lsst.ip.isr import PhotonTransferCurveDataset
31import lsst.ip.isr.isrMock as isrMock
34class PtcDatasetCases(lsst.utils.tests.TestCase):
35 """Test that write/read methods of PhotonTransferCurveDataset work
36 """
37 def setUp(self):
39 self.flatMean = 2000
40 self.readNoiseAdu = 10
41 mockImageConfig = isrMock.IsrMock.ConfigClass()
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
48 self.flatExp1 = isrMock.FlatMock(config=mockImageConfig).run()
49 self.flatExp2 = self.flatExp1.clone()
50 (shapeY, shapeX) = self.flatExp1.getDimensions()
52 self.flatWidth = np.sqrt(self.flatMean) + self.readNoiseAdu
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))
59 self.flatExp1.image.array[:] = flatData1
60 self.flatExp2.image.array[:] = flatData2
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
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] = []
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)
133 for key, value in ptcDataset.auxValues.items():
134 self.assertIsInstance(value, np.ndarray)
135 self.assertEqual(value.dtype, np.float64)
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)
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)
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)
157 def test_partialPtcDataset(self):
158 """Test of a partial PTC dataset."""
159 # Fill the dataset with made up data.
160 nSideCovMatrix = 2
162 partialDataset = PhotonTransferCurveDataset(
163 self.ampNames,
164 ptcFitType="PARTIAL",
165 covMatrixSide=nSideCovMatrix
166 )
167 self._checkTypes(partialDataset)
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 )
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)
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)
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)
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:
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)
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)
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]
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)
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
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)
277 for useAuxValues in [False, True]:
278 if useAuxValues:
279 localDataset.auxValues = {
280 "CCOBCURR": np.ones(nSignalPoints),
281 "CCDTEMP": np.zeros(nSignalPoints),
282 }
284 self._checkTypes(localDataset)
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)
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)
298 def test_getExpIdsUsed(self):
299 localDataset = copy.copy(self.dataset)
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)]))
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")
310 def test_getGoodAmps(self):
311 dataset = self.dataset
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"])
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)
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])
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])
330 self.assertTrue(np.all(used == [(12, 34), (90, 10)]))
333class MemoryTester(lsst.utils.tests.MemoryTestCase):
334 pass
337def setup_module(module):
338 lsst.utils.tests.init()
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()