adress comments

pyirf/interpolation/component_estimators.py

@@ -541,6 +541,12 @@ def __init__(
         else:
             self.fill_val = fill_value
 
+        # Raise error if fill-values should be handled in >=3 dims
+        if self.fill_val and self.grid_dim >= 3:
+            raise ValueError(
+                "Fill-value handling only supported in up to two grid dimensions."
+            )
+
         # If fill-values should be handled in 2D, construct a trinangulation
         # to later determine in which simplex the target values is
         if self.fill_val and (self.grid_dim == 2):
@@ -566,19 +572,15 @@ def __call__(self, target_point):
         # First, construct estimation without handling fill-values
         full_estimation = super().__call__(target_point)
         # Safeguard against extreme extrapolation cases
-        full_estimation[full_estimation < 0] = 0
+        np.clip(full_estimation, 0, None, out=full_estimation)
 
         # Early exit if fill_values should not be handled
         if not self.fill_val:
             return full_estimation
 
-        # Raise error if fill-values should be handled in >=3 dims
-        if self.grid_dim >= 3:
-            raise ValueError(
-                "Fill-value handling only supported in up to two grid dimensions."
-            )
-
         # Early exit if a nearest neighbor estimation would be overwritten
+        # Complex setup is needed to catch settings where the user mixes approaches and
+        # e.g. uses nearest neighbors for extrapolation and an actual interpolation otherwise
         if self.grid_dim == 1:
             if (
                 (target_point < self.grid_points.min())
@@ -631,7 +633,9 @@ def __call__(self, target_point):
 
             # This collected mask now counts for each entry in the estimation how many
             # of the entries used for extrapolation contained fill-values
-            intermediate_mask = mask0.astype("int") + mask1.astype("int") + mask2.astype("int")
+            intermediate_mask = (
+                mask0.astype("int") + mask1.astype("int") + mask2.astype("int")
+            )
             mask = np.full_like(intermediate_mask, True, dtype=bool)
 
             # Simplest cases: All or none entries were fill-values, so either return

pyirf/interpolation/tests/test_component_estimator_specific_classes.py

@@ -358,18 +358,16 @@ def test_RadMaxEstimator_fill_val_handling_3D():
 
     rad_max = np.array([[0.95], [0.95], [0.95], [0.95]])
 
-    estim = RadMaxEstimator(
-        grid_points=grid_points_3D,
-        rad_max=rad_max,
-        fill_value=0.95,
-        interpolator_cls=GridDataInterpolator,
-        interpolator_kwargs=None,
-        extrapolator_cls=None,
-        extrapolator_kwargs=None,
-    )
-
     with pytest.raises(
         ValueError,
         match="Fill-value handling only supported in up to two grid dimensions.",
     ):
-        estim(np.array([0.25, 0.25, 0.25]))
+        RadMaxEstimator(
+            grid_points=grid_points_3D,
+            rad_max=rad_max,
+            fill_value=0.95,
+            interpolator_cls=GridDataInterpolator,
+            interpolator_kwargs=None,
+            extrapolator_cls=None,
+            extrapolator_kwargs=None,
+        )