Coverage for tests/test_ptcDataset.py: 9%
196 statements
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« prev ^ index » next coverage.py v7.2.5, created at 2023-05-10 02:12 -0700
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
25import numpy as np
27import lsst.utils.tests
29from lsst.ip.isr import PhotonTransferCurveDataset
30import lsst.ip.isr.isrMock as isrMock
33class PtcDatasetCases(lsst.utils.tests.TestCase):
34 """Test that write/read methods of PhotonTransferCurveDataset work
35 """
36 def setUp(self):
38 self.flatMean = 2000
39 self.readNoiseAdu = 10
40 mockImageConfig = isrMock.IsrMock.ConfigClass()
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
47 self.flatExp1 = isrMock.FlatMock(config=mockImageConfig).run()
48 self.flatExp2 = self.flatExp1.clone()
49 (shapeY, shapeX) = self.flatExp1.getDimensions()
51 self.flatWidth = np.sqrt(self.flatMean) + self.readNoiseAdu
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))
58 self.flatExp1.image.array[:] = flatData1
59 self.flatExp2.image.array[:] = flatData2
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
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] = []
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)
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)
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)
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)
142 def test_partialPtcDataset(self):
143 """Test of a partial PTC dataset."""
144 # Fill the dataset with made up data.
145 nSideCovMatrix = 2
147 partialDataset = PhotonTransferCurveDataset(
148 self.ampNames,
149 ptcFitType="PARTIAL",
150 covMatrixSide=nSideCovMatrix
151 )
152 self._checkTypes(partialDataset)
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)
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)
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)
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:
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
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)
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]
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)
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
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)
244 self._checkTypes(localDataset)
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)
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)
258 def test_getExpIdsUsed(self):
259 localDataset = copy.copy(self.dataset)
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)]))
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")
270 def test_getGoodAmps(self):
271 dataset = self.dataset
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"])
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)
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])
286 with self.assertWarnsRegex(RuntimeWarning, "PTC file was written incorrectly"):
287 used = localDataset.getExpIdsUsed("C:0,0")
289 self.assertTrue(np.all(used == [(12, 34), (90, 10)]))
292class MemoryTester(lsst.utils.tests.MemoryTestCase):
293 pass
296def setup_module(module):
297 lsst.utils.tests.init()
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()