Added tests.

pipelines/production/comCamRapidAnalysisPipeline.yaml

@@ -19,6 +19,7 @@ tasks:
     config:
       estimateZernikes.maxNollIndex: 22
       estimateZernikes.convergeTol: 10.0e-9
+      estimateZernikes.requireConverge: True
       estimateZernikes.saveHistory: False
       estimateZernikes.maskKwargs: { "doMaskBlends": False }
       donutStampSelector.maxSelect: 5

python/lsst/ts/wep/estimation/tie.py

@@ -99,7 +99,7 @@ class TieAlgorithm(WfAlgorithm):
     requireConverge : bool, optional
         Whether to require that the TIE converges. If True, and the TIE
         did not converge, the TIE returns NaNs.
-        (the default is True)
+        (the default is False)
     """
 
     def __init__(
@@ -114,7 +114,7 @@ def __init__(
         maskKwargs: Optional[dict] = None,
         modelPupilKernelSize: float = 2,
         binning: int = 1,
-        requireConverge: bool = True,
+        requireConverge: bool = False,
     ) -> None:
         self.opticalModel = opticalModel
         self.maxIter = maxIter

python/lsst/ts/wep/task/estimateZernikesTieTask.py

@@ -112,7 +112,7 @@ class EstimateZernikesTieConfig(EstimateZernikesBaseConfig):
     )
     requireConverge = pexConfig.Field(
         dtype=bool,
-        default=True,
+        default=False,
         doc="Whether to require that the TIE converges. "
         + "If True, and the TIE did not converge, the TIE returns NaNs. ",
     )

tests/task/test_calcZernikesTieTaskCwfs.py

@@ -112,6 +112,9 @@ def testValidateConfigs(self):
 
         self.assertEqual(type(self.task.combineZernikes), CombineZernikesMeanTask)
 
+        self.config.estimateZernikes.binning = 2
+        self.assertEqual(self.task.estimateZernikes.wfAlgoConfig.binning, 2)
+
     def testEstimateZernikes(self):
         zernCoeff = self.task.estimateZernikes.run(
             self.donutStampsExtra, self.donutStampsIntra
@@ -281,3 +284,23 @@ def testUnevenPairs(self):
 
         # Now estimate Zernikes
         self.task.run(stampsExtra, stampsIntra)
+
+    def testRequireConverge(self):
+        config = CalcZernikesTaskConfig()
+        config.estimateZernikes.requireConverge = True  # Require to converge
+        config.estimateZernikes.convergeTol = 0  # But don't allow convergence
+        task = CalcZernikesTask(config=config, name="Test requireConverge")
+
+        # Estimate zernikes
+        donutStampDir = os.path.join(self.testDataDir, "donutImg", "donutStamps")
+        donutStampsExtra = DonutStamps.readFits(
+            os.path.join(donutStampDir, "R04_SW0_donutStamps.fits")
+        )
+        donutStampsIntra = DonutStamps.readFits(
+            os.path.join(donutStampDir, "R04_SW1_donutStamps.fits")
+        )
+        output = task.estimateZernikes.run(donutStampsExtra, donutStampsIntra)
+        zernikes = output.zernikes
+
+        # Everything should be NaN because we did not converge
+        self.assertTrue(np.isnan(zernikes).all())