Convert linarizer array to int type

lsst · Mar 17, 2020 · 40eb018 · 40eb018
1 parent 81dd812
commit 40eb018
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Showing 2 changed files with 17 additions and 5 deletions.
diff --git a/python/lsst/cp/pipe/ptc.py b/python/lsst/cp/pipe/ptc.py
@@ -657,8 +657,8 @@ def calculateLinearityResidualAndLinearizers(self, exposureTimeVector, meanSigna
         # Use linear part to get time at wich signal is maxAduForLookupTableLinearizer ADU
         tMax = (self.config.maxAduForLookupTableLinearizer - parsFit[0])/parsFit[1]
         timeRange = np.linspace(0, tMax, self.config.maxAduForLookupTableLinearizer)
-        signalIdeal = parsFit[0] + parsFit[1]*timeRange
-        signalUncorrected = self.funcPolynomial(parsFit, timeRange)
+        signalIdeal = (parsFit[0] + parsFit[1]*timeRange).astype(int)
+        signalUncorrected = (self.funcPolynomial(parsFit, timeRange)).astype(int)
         linearizerTableRow = signalIdeal - signalUncorrected  # LinearizerLookupTable has corrections
 
         # Use quadratic and linear part of fit to produce c0 for LinearizeSquared

diff --git a/tests/test_ptc.py b/tests/test_ptc.py
@@ -121,7 +121,7 @@ def ptcFitQuadAndCubic(self, order):
         numberAmps = len(self.ampNames)
         numberAduValues = config.maxAduForLookupTableLinearizer
 
-        lookupTableArray = np.zeros((numberAmps, numberAduValues), dtype=np.float32)
+        lookupTableArray = np.zeros((numberAmps, numberAduValues), dtype=np.int)
         returnedDataset = task.fitPtcAndNonLinearity(localDataset, lookupTableArray, ptcFitType='POLYNOMIAL')
 
         # check that the linearizer table has been filled out properly
@@ -131,7 +131,7 @@ def ptcFitQuadAndCubic(self, order):
             signalIdeal = timeRange*self.flux
             signalUncorrected = task.funcPolynomial(np.array([0.0, self.flux, self.k2NonLinearity]),
                                                     timeRange)
-            linearizerTableRow = signalIdeal - signalUncorrected
+            linearizerTableRow = signalIdeal.astype(int) - signalUncorrected.astype(int)
             self.assertEqual(len(linearizerTableRow), len(lookupTableArray[i, :]))
             for j in np.arange(len(linearizerTableRow)):
                 self.assertAlmostEqual(linearizerTableRow[j], lookupTableArray[i, :][j], places=6)
@@ -200,11 +200,23 @@ def test_ptcFitAstier(self):
 
         numberAmps = len(self.ampNames)
         numberAduValues = config.maxAduForLookupTableLinearizer
-        lookupTableArray = np.zeros((numberAmps, numberAduValues), dtype=np.float32)
+        lookupTableArray = np.zeros((numberAmps, numberAduValues), dtype=np.int)
 
         returnedDataset = task.fitPtcAndNonLinearity(localDataset, lookupTableArray,
                                                      ptcFitType='ASTIERAPPROXIMATION')
 
+        # check that the linearizer table has been filled out properly
+        for i in np.arange(numberAmps):
+            tMax = (config.maxAduForLookupTableLinearizer)/self.flux
+            timeRange = np.linspace(0., tMax, config.maxAduForLookupTableLinearizer)
+            signalIdeal = timeRange*self.flux
+            signalUncorrected = task.funcPolynomial(np.array([0.0, self.flux, self.k2NonLinearity]),
+                                                    timeRange)
+            linearizerTableRow = signalIdeal.astype(int) - signalUncorrected.astype(int)
+            self.assertEqual(len(linearizerTableRow), len(lookupTableArray[i, :]))
+            for j in np.arange(len(linearizerTableRow)):
+                self.assertAlmostEqual(linearizerTableRow[j], lookupTableArray[i, :][j], places=6)
+
         # check entries in localDataset, which was modified by the function
         for ampName in self.ampNames:
             self.assertAlmostEqual('ASTIERAPPROXIMATION', localDataset.ptcFitType[ampName])