Update test_covAstier with new numbers

pipelines/LSSTComCam/cpCti.yaml

@@ -0,0 +1,4 @@
+description: LSSTCam cti calibration construction
+instrument: lsst.obs.lsst.LsstComCam
+imports:
+  - location: $CP_PIPE_DIR/pipelines/_ingredients/cpCtiLSST.yaml

python/lsst/cp/pipe/ptc/cpPtcSolve.py

@@ -613,17 +613,17 @@ def fitDataFullCovariance(self, dataset):
             fitResults = {'a': [], 'c': [], 'noiseMatrix': [], 'gain': [], 'paramsErr': []}
 
             # Pick the correct full covariance model function
-            model = self.funcFullCovarianceModel
+            ptcModel = self.funcFullCovarianceModel
             if dataset.ptcFitType == "FULLCOVARIANCE_NO_B":
-                model = self.funcFullCovarianceModelNoB
+                ptcModel = self.funcFullCovarianceModelNoB
                 pInit = np.concatenate((a0.ravel(), noiseMatrix0.ravel(), np.array(gain0)), axis=None)
 
             params, paramsErr, _ = fitLeastSq(
-                    pInit,
-                    muAtAmpMasked,
-                    covAtAmpForFitMasked.ravel(),
-                    model,
-                    weightsY=covSqrtWeightsAtAmpForFitMasked.ravel(),
+                pInit,
+                muAtAmpMasked,
+                covAtAmpForFitMasked.ravel(),
+                ptcModel,
+                weightsY=covSqrtWeightsAtAmpForFitMasked.ravel(),
             )
 
             if dataset.ptcFitType == "FULLCOVARIANCE_NO_B":
@@ -662,7 +662,7 @@ def fitDataFullCovariance(self, dataset):
                 fitResults['c'],
                 fitResults['noiseMatrix'],
                 fitResults['gain'],
-                setBtoZero=dataset.ptcFitType == "FULLCOVARIANCE_NO_B",
+                setBtoZero=(dataset.ptcFitType == "FULLCOVARIANCE_NO_B"),
             )
             dataset.covariancesSqrtWeights[ampName] = covSqrtWeightsAtAmp
             dataset.aMatrix[ampName] = fitResults['a']
@@ -782,7 +782,7 @@ def funcFullCovarianceModelNoB(self, params, x):
         Parameters
         ----------
         params : `list`
-            Parameters of the model: aMatrix, CMatrix, noiseMatrix,
+            Parameters of the model: aMatrix, noiseMatrix,
             gain (e/adu).
         x : `numpy.array`, (N,)
             Signal mu (adu)
@@ -796,7 +796,6 @@ def funcFullCovarianceModelNoB(self, params, x):
         lenParams = matrixSideFit*matrixSideFit
         aMatrix = params[:lenParams].reshape((matrixSideFit, matrixSideFit))
         cMatrix = np.zeros_like(aMatrix)
-        # params[lenParams:2*lenParams].reshape((matrixSideFit, matrixSideFit))
         noiseMatrix = params[lenParams:2*lenParams].reshape((matrixSideFit, matrixSideFit))
         gain = params[-1]
 

tests/test_defects.py

@@ -549,7 +549,7 @@ def test_maskBlocks_y_out_of_order_dm38103(self):
         """A test for maskBlocksIfIntermitentBadPixelsInColumn, y out of order.
 
         This test is a variant of
-        notest_maskBlocks_every_other_pixel_bad_greater_than_threshold with
+        test_maskBlocks_every_other_pixel_bad_greater_than_threshold with
         an extra out-of-y-order bad pixel to trigger DM-38103.
         """
         expectedDefects = [Box2I(corner=Point2I(50, 110), dimensions=Extent2I(1, 31))]

tests/test_ptc.py

@@ -260,17 +260,17 @@ def test_covAstier(self):
         expectedNoiseMatrix = {
             "FULLCOVARIANCE": np.array(
                 [[8.99474598, 9.94916264, -27.90587299],
-                [-2.95079527, -17.11827641, -47.88156244],
-                [5.24915021, -3.25786165, 26.09634067]],
+                 [-2.95079527, -17.11827641, -47.88156244],
+                 [5.24915021, -3.25786165, 26.09634067]],
             ),
             "FULLCOVARIANCE_NO_B": np.array(
                 [[8.71049338, 12.48584043, -37.06585088],
-                [-4.80523971, -23.29102809, -66.37815343],
-                [7.48654766, -4.10168337, 35.64469824]],
+                 [-4.80523971, -23.29102809, -66.37815343],
+                 [7.48654766, -4.10168337, 35.64469824]],
             ),
         }
 
-        for fitType in ["FULLCOVARIANCE_NO_B"]:
+        for fitType in ["FULLCOVARIANCE", "FULLCOVARIANCE_NO_B"]:
             solveConfig.ptcFitType = fitType
             solveTask = cpPipe.ptc.PhotonTransferCurveSolveTask(config=solveConfig)
 
@@ -293,14 +293,14 @@ def test_covAstier(self):
                 # Check that the PTC turnoff is correctly computed.
                 # This will be different for the C:0,0 amp.
                 if amp == "C:0,0":
-                    self.assertAlmostEqual(ptc.ptcTurnoff[amp], ptc.rawMeans[ampName][-3])
+                    self.assertAlmostEqual(ptc.ptcTurnoff[amp], ptc.rawMeans[amp][-3])
                 else:
-                    self.assertAlmostEqual(ptc.ptcTurnoff[amp], ptc.rawMeans[ampName][-1])
+                    self.assertAlmostEqual(ptc.ptcTurnoff[amp], ptc.rawMeans[amp][-1])
 
-                # Test that all the quantities are correctly ordered and have
-                # not accidentally been masked. We check every other output ([::2])
-                # because these datasets are in pairs of [real, dummy] to
-                # match the inputs to the extract task.
+                # Test that all the quantities are correctly ordered and
+                # have not accidentally been masked. We check every other
+                # output ([::2]) because these datasets are in pairs of
+                # [real, dummy] to match the inputs to the extract task.
                 for i, extractPtc in enumerate(resultsExtract.outputCovariances[::2]):
                     self.assertFloatsAlmostEqual(
                         extractPtc.rawExpTimes[ampName][0],
@@ -353,9 +353,9 @@ def test_covAstier(self):
                         np.nanmedian(ptc.noiseList[ampName]) * ptc.gain[ampName],
                         rtol=0.05,
                     )
-                    # If the noise error is greater than the noise, something
-                    # is seriously wrong. Possibly some kind of gain application
-                    # mismatch.
+                    # If the noise error is greater than the noise,
+                    # something is seriously wrong. Possibly some
+                    # kind of gain application mismatch.
                     self.assertLess(
                         ptc.noiseErr[ampName],
                         ptc.noise[ampName],
@@ -368,17 +368,19 @@ def test_covAstier(self):
                 ) / ptc.covariancesModel[amp][mask, 0, 0]
                 np.testing.assert_array_less(np.abs(values), 2e-3)
 
-                values = (
-                    ptc.covariancesModel[amp][mask, 1, 1] - ptc.covariances[amp][mask, 1, 1]
-                ) / ptc.covariancesModel[amp][mask, 1, 1]
-                np.testing.assert_array_less(np.abs(values), 0.3)
+                if ptc.ptcFitType == "FULLCOVARIANCE":
+                    values = (
+                        ptc.covariancesModel[amp][mask, 0, 1] - ptc.covariances[amp][mask, 0, 1]
+                    ) / ptc.covariancesModel[amp][mask, 0, 1]
+                    np.testing.assert_array_less(np.abs(values), 0.3)
 
-                values = (
-                    ptc.covariancesModel[amp][mask, 1, 2] - ptc.covariances[amp][mask, 1, 2]
-                ) / ptc.covariancesModel[amp][mask, 1, 2]
-                np.testing.assert_array_less(np.abs(values), 0.3)
+                    values = (
+                        ptc.covariancesModel[amp][mask, 1, 0] - ptc.covariances[amp][mask, 1, 0]
+                    ) / ptc.covariancesModel[amp][mask, 1, 0]
+                    np.testing.assert_array_less(np.abs(values), 0.3)
 
-            # And test that the auxiliary values are there and correctly ordered.
+            # And test that the auxiliary values are there and
+            # correctly ordered.
             self.assertIn('CCOBCURR', ptc.auxValues)
             self.assertIn('CCDTEMP', ptc.auxValues)
             firstExpIds = np.array([i for i, _ in ptc.inputExpIdPairs['C:0,0']], dtype=np.float64)