Raise error when data is insufficient

python/lsst/drp/tasks/gbdesAstrometricFit.py

@@ -436,6 +436,11 @@ def run(
             ``outputCatalog`` : `pyarrow.Table`
                 Catalog with fit residuals of all sources used.
         """
+        if (len(inputVisitSummaries) == 1) and (self.config.deviceModel and self.config.exposureModel):
+            raise RuntimeError(
+                "More than one exposure is necessary to break the degeneracy between the"
+                " device model and the exposure model."
+            )
         self.log.info("Gathering instrument, exposure, and field info")
         # Set up an instrument object
         instrument = wcsfit.Instrument(instrumentName)
@@ -469,6 +474,7 @@ def run(
         sourceIndices, usedColumns = self._load_catalogs_and_associate(
             associations, inputCatalogRefs, extensionInfo
         )
+        self._check_degeneracies(associations, extensionInfo)
 
         self.log.info("Fit the WCSs")
         # Set up a YAML-type string using the config variables and a sample
@@ -854,7 +860,8 @@ class `wcsfit.FoFClass`, associating them into matches as you go.
         Parameters
         ----------
         associations : `wcsfit.FoFClass`
-            Object to which to add the catalog of reference objects.
+            Object to which to add the catalog of source and which performs
+            the source association.
         inputCatalogRefs : `list`
             List of DeferredDatasetHandles pointing to visit-level source
             tables.
@@ -948,6 +955,64 @@ class `wcsfit.FoFClass`, associating them into matches as you go.
 
         return sourceIndices, columns
 
+    def _check_degeneracies(self, associations, extensionInfo):
+        """Check that the minimum number of visits and sources needed to
+        constrain the model are present.
+
+        This does not guarantee that the Hessian matrix of the chi-square,
+        which is used to fit the model, will be positive-definite, but if the
+        checks here do not pass, the matrix is certain to not be
+        positive-definite and the model cannot be fit.
+
+        Parameters
+        ----------
+        associations : `wcsfit.FoFClass`
+            Object holding the source association information.
+        extensionInfo : `lsst.pipe.base.Struct`
+            Struct containing properties for each extension.
+        """
+        # As a baseline, need to have more stars per detector than per-detector
+        # parameters, and more stars per visit than per-visit parameters.
+        whichExtension = np.array(associations.extn)
+        whichDetector = np.zeros(len(whichExtension))
+        whichVisit = np.zeros(len(whichExtension))
+
+        for extension, (detector, visit) in enumerate(zip(extensionInfo.detector, extensionInfo.visit)):
+            ex_ind = whichExtension == extension
+            whichDetector[ex_ind] = detector
+            whichVisit[ex_ind] = visit
+
+        nCoeffVisitModel = int(((2 * self.config.exposurePolyOrder + 3) ** 2 - 1) / 8)
+
+        if "BAND/DEVICE/poly" in self.config.deviceModel:
+            nCoeffDetectorModel = int(((2 * self.config.devicePolyOrder + 3) ** 2 - 1) / 8)
+            unconstrainedDetectors = []
+            for detector in np.unique(extensionInfo.detector):
+                numSources = (whichDetector == detector).sum()
+                if numSources < nCoeffDetectorModel:
+                    unconstrainedDetectors.append(str(detector))
+
+            if unconstrainedDetectors:
+                raise RuntimeError(
+                    "The model is not constrained. The following detectors do not have enough "
+                    f"sources ({nCoeffDetectorModel} required): " + ", ".join(unconstrainedDetectors)
+                )
+
+        if "EXPOSURE/poly" in self.config.exposureModel:
+            nCoeffVisitModel = int(((2 * self.config.exposurePolyOrder + 3) ** 2 - 1) / 8)
+
+            unconstrainedVisits = []
+            for visit in np.unique(extensionInfo.visit):
+                numSources = (whichVisit == visit).sum()
+                if numSources < nCoeffVisitModel:
+                    unconstrainedVisits.append(str(visit))
+
+            if unconstrainedVisits:
+                raise RuntimeError(
+                    "The model is not constrained. The following visits do not have enough "
+                    f"sources ({nCoeffVisitModel} required):" + ", ".join(unconstrainedVisits)
+                )
+
     def make_yaml(self, inputVisitSummary, inputFile=None):
         """Make a YAML-type object that describes the parameters of the fit
         model.