Simplify interp validator

tests/test_components/test_custom.py

@@ -1208,3 +1208,39 @@ def test_warn_diffraction_monitor_intersection(log_capture):
     )
     sim.updated_copy(structures=[box])
     assert_log_level(log_capture, "WARNING")
+
+
+@pytest.mark.parametrize(
+    "custom_class, data_key",
+    [
+        (CustomMedium, "permittivity"),
+        (td.CustomFieldSource, "field_dataset"),
+        (td.CustomCurrentSource, "current_dataset"),
+    ],
+)
+def test_custom_medium_duplicate_coords(custom_class, data_key):
+    """Test that creating components with duplicate coordinates raises validation error."""
+    coords = {
+        "x": np.array([0.0, 1.0, 1.0, 2.0]),  # Duplicate at x=1.0
+        "y": np.array([0.0, 1.0]),
+        "z": np.array([0.0, 1.0]),
+    }
+
+    if custom_class != CustomMedium:
+        coords["f"] = np.array([2e14])
+
+    shape = tuple(len(c) for c in coords.values())
+    data = np.random.random(shape) + 1
+    spatial_data = td.SpatialDataArray(data, coords=coords)
+
+    if custom_class == CustomMedium:
+        with pytest.raises(pydantic.ValidationError, match="duplicate coordinates"):
+            _ = custom_class(permittivity=spatial_data)
+    else:
+        field_components = {
+            f"{field}{component}": spatial_data.copy() for field in "EH" for component in "xyz"
+        }
+        field_dataset = td.FieldDataset(**field_components)
+
+        with pytest.raises(pydantic.ValidationError, match="duplicate coordinates"):
+            _ = custom_class(size=SIZE, source_time=ST, **{data_key: field_dataset})

tidy3d/components/data/data_array.py

@@ -9,7 +9,6 @@
 import dask
 import h5py
 import numpy as np
-import pandas
 import xarray as xr
 from autograd.tracer import isbox
 from xarray.core import alignment, missing
@@ -119,7 +118,7 @@ def assign_data_attrs(cls, val):
         return val
 
     def _interp_validator(self, field_name: str = None) -> None:
-        """Make sure we can interp()/sel() the data.
+        """Ensure the data can be interpolated or selected by checking for duplicate coordinates.
 
         NOTE
         ----
@@ -129,39 +128,13 @@ def _interp_validator(self, field_name: str = None) -> None:
         if field_name is None:
             field_name = "DataArray"
 
-        dims = self.coords.dims
-
-        for dim in dims:
-            # in case we encounter some /0 or /NaN we'll ignore the warnings here
-            with np.errstate(divide="ignore", invalid="ignore"):
-                # check that we can interpolate
-                try:
-                    x0 = np.array(self.coords[dim][0])
-                    self.interp({dim: x0}, method="linear")
-                    self.interp({dim: x0}, method="nearest")
-                    # self.interp_like(self.isel({self.dim: 0}))
-                except pandas.errors.InvalidIndexError as e:
-                    raise DataError(
-                        f"'{field_name}.interp()' fails to interpolate along {dim} which is used by the solver. "
-                        "This may be caused, for instance, by duplicated data "
-                        f"in this dimension (you can verify this by running "
-                        f"'{field_name}={field_name}.drop_duplicates(dim=\"{dim}\")' "
-                        f"and interpolate with the new '{field_name}'). "
-                        "Plase make sure data can be interpolated."
-                    ) from e
-                # in case it can interpolate, try also to sel
-                try:
-                    x0 = np.array(self.coords[dim][0])
-                    self.sel({dim: x0}, method="nearest")
-                except pandas.errors.InvalidIndexError as e:
-                    raise DataError(
-                        f"'{field_name}.sel()' fails to select along {dim} which is used by the solver. "
-                        "This may be caused, for instance, by duplicated data "
-                        f"in this dimension (you can verify this by running "
-                        f"'{field_name}={field_name}.drop_duplicates(dim=\"{dim}\")' "
-                        f"and run 'sel()' with the new '{field_name}'). "
-                        "Plase make sure 'sel()' can be used on the 'DataArray'."
-                    ) from e
+        for dim, coord in self.coords.items():
+            if coord.to_index().duplicated().any():
+                raise DataError(
+                    f"Field '{field_name}' contains duplicate coordinates in dimension '{dim}'. "
+                    "Duplicates can be removed by running "
+                    f"'{field_name}={field_name}.drop_duplicates(dim=\"{dim}\")'."
+                )
 
     @classmethod
     def assign_coord_attrs(cls, val):