requireConverge in TIE; moved binning config to estimateZernikes deri…

…ved classes; fixed Nones in task history.

doc/versionHistory.rst

@@ -6,6 +6,15 @@
 Version History
 ##################
 
+.. _lsst.ts.wep-12.7.0:
+
+-------------
+12.7.0
+-------------
+
+* Added requireConverge to TIE and defaulted to True in task
+* Fixed bug with None types in EstimateZernikeTask metadata histories
+
 .. _lsst.ts.wep-12.6.1:
 
 -------------

pipelines/production/comCamRapidAnalysisPipeline.yaml

@@ -17,7 +17,9 @@ tasks:
   calcZernikesTask:
     class: lsst.ts.wep.task.calcZernikesTask.CalcZernikesTask
     config:
-      estimateZernikes.maxNollIndex: 28
+      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

@@ -95,6 +95,11 @@ class TieAlgorithm(WfAlgorithm):
     binning : int, optional
         Binning factor to apply to the donut stamps before estimating
         Zernike coefficients. The default value of 1 means no binning.
+        (the default is 1)
+    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 False)
     """
 
     def __init__(
@@ -109,6 +114,7 @@ def __init__(
         maskKwargs: Optional[dict] = None,
         modelPupilKernelSize: float = 2,
         binning: int = 1,
+        requireConverge: bool = False,
     ) -> None:
         self.opticalModel = opticalModel
         self.maxIter = maxIter
@@ -120,6 +126,7 @@ def __init__(
         self.maskKwargs = maskKwargs
         self.modelPupilKernelSize = modelPupilKernelSize
         self.binning = binning
+        self.requireConverge = requireConverge
 
     @property
     def requiresPairs(self) -> bool:
@@ -429,6 +436,24 @@ def binning(self, value: int) -> None:
             raise ValueError("binning must be greater than or equal to 1.")
         self._binning = value
 
+    @property
+    def requireConverge(self) -> int:
+        """Whether to require that the TIE converges."""
+        return self._requireConverge
+
+    @requireConverge.setter
+    def requireConverge(self, value: int) -> None:
+        """Whether to require that the TIE converges.
+
+        Parameters
+        ----------
+        value : bool
+            Whether to require that the TIE converges.
+        """
+        if not isinstance(value, bool):
+            raise TypeError("requireConverge must be a bool.")
+        self._requireConverge = value
+
     @property
     def history(self) -> dict:
         """The algorithm history.
@@ -867,4 +892,8 @@ def _estimateZk(
             if caustic or converged:
                 break
 
+        # If we never converged, return NaNs?
+        if self.requireConverge and not converged:
+            zkSum *= np.nan
+
         return zkSum

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

@@ -69,12 +69,6 @@ class EstimateZernikesBaseConfig(pexConfig.Config):
         + "to the Noll index. In this case, indices 0-3 are always set to zero, "
         + "because they are not estimated by our pipeline.",
     )
-    binning = pexConfig.Field(
-        dtype=int,
-        default=1,
-        doc="Binning factor to apply to the donut stamps before estimating "
-        + "Zernike coefficients. A value of 1 means no binning.",
-    )
     saveHistory = pexConfig.Field(
         dtype=bool,
         default=False,

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

@@ -38,6 +38,12 @@ class EstimateZernikesDanishConfig(EstimateZernikesBaseConfig):
         doc="A dictionary containing any of the keyword arguments for "
         + "scipy.optimize.least_squares, except `fun`, `x0`, `jac`, or `args`.",
     )
+    binning = pexConfig.Field(
+        dtype=int,
+        default=1,
+        doc="Binning factor to apply to the donut stamps before estimating "
+        + "Zernike coefficients. A value of 1 means no binning.",
+    )
 
 
 class EstimateZernikesDanishTask(EstimateZernikesBaseTask):

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

@@ -104,6 +104,18 @@ class EstimateZernikesTieConfig(EstimateZernikesBaseConfig):
         + "when estimating Zernikes with a single donut. "
         + "(the default is 2)",
     )
+    binning = pexConfig.Field(
+        dtype=int,
+        default=1,
+        doc="Binning factor to apply to the donut stamps before estimating "
+        + "Zernike coefficients. A value of 1 means no binning.",
+    )
+    requireConverge = pexConfig.Field(
+        dtype=bool,
+        default=False,
+        doc="Whether to require that the TIE converges. "
+        + "If True, and the TIE did not converge, the TIE returns NaNs. ",
+    )
 
 
 class EstimateZernikesTieTask(EstimateZernikesBaseTask):

python/lsst/ts/wep/utils/taskUtils.py

@@ -446,7 +446,8 @@ def convertHistoryToMetadata(history: dict) -> pipeBase.TaskMetadata:
     """
     if isinstance(history, dict):
         history = {
-            str(key): convertHistoryToMetadata(val) for key, val in history.items()
+            str(key): ("None" if val is None else convertHistoryToMetadata(val))
+            for key, val in history.items()
         }
         history = pipeBase.TaskMetadata.from_dict(history)
     elif isinstance(history, np.ndarray):
@@ -493,9 +494,11 @@ def convertMetadataToHistory(metadata: pipeBase.TaskMetadata) -> dict:
                 for key, val in metadata.items()
             }
 
-    # Convert numeric strings back to floats and ints
+    # Convert "None" strings back to None and numeric strings to floats/ints
     elif isinstance(metadata, str):
-        if "." in metadata:
+        if metadata == "None":
+            metadata = None
+        elif "." in metadata:
             try:
                 metadata = float(metadata)
             except:  # noqa: E722

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())