add check for partial patches & corresponding test

xbatcher/generators.py

@@ -89,6 +89,16 @@ def __init__(
         self._all_sliced_dims: dict[Hashable, int] = dict(
             **self._unique_batch_dims, **self.input_dims
         )
+
+        # Check that duplicate dims imply whole patches per batch
+        for dim, length in self.batch_dims.items():
+            input_dim_length = self.input_dims.get(dim)
+            if input_dim_length is not None and length % input_dim_length != 0:
+                raise ValueError(
+                    f'Input and batch dimension sizes imply partial batches '
+                    f'on dimension {dim}. Input size: {input_dim_length}; Batch size: {length}'
+                )
+
         self.selectors: BatchSelectorSet = self._gen_batch_selectors(ds)
 
     def _gen_batch_selectors(self, ds: xr.DataArray | xr.Dataset) -> BatchSelectorSet:

xbatcher/tests/test_generators.py

@@ -265,6 +265,20 @@ def test_batch_3d_1d_input_batch_concat_duplicate_dim(sample_ds_3d):
         validate_batch_dimensions(expected_dims=expected_dims, batch=ds_batch)
 
 
+def test_batch_3d_uneven_batch_input_dim(sample_ds_3d):
+    """
+    Test for error when a batch dimension is not a multiple of the
+    corresponding input dimension.
+    """
+    with pytest.raises(ValueError, match='imply partial batches'):
+        _ = BatchGenerator(
+            sample_ds_3d,
+            input_dims={'x': 5, 'y': 10},
+            batch_dims={'x': 11, 'y': 21},
+            concat_input_dims=True,
+        )
+
+
 @pytest.mark.parametrize('input_size', [5, 10])
 def test_batch_3d_2d_input(sample_ds_3d, input_size):
     """