Fix __getitem__ in LabelTensor

pina/label_tensor.py

@@ -505,50 +505,40 @@ def vstack(tensors):
         return LabelTensor.cat(tensors, dim=0)
 
     # This method is used to update labels
-    def _update_single_label(
-        self, old_labels, to_update_labels, index, dim, to_update_dim
-    ):
+    def _update_single_label(self, index, dim):
         """
         Update the labels of the tensor based on the index (or list of indices).
 
-        :param dict old_labels: Labels from which retrieve data.
-        :param dict to_update_labels: Labels to update.
         :param index: Index of dof to retain.
         :type index: int | slice | list[int] | tuple[int] | torch.Tensor
-        :param int dim: The dimension to update.
-
+        :param int dim: Dimension of the indexes in the original tensor.
+        :return: The updated labels for the specified dimension.
+        :rtype: list[int]
         :raises: ValueError: If the index type is not supported.
         """
-
-        old_dof = old_labels[to_update_dim]["dof"]
-        label_name = old_labels[dim]["name"]
+        old_dof = self._labels[dim]["dof"]
         # Handle slicing
         if isinstance(index, slice):
-            to_update_labels[dim] = {"dof": old_dof[index], "name": label_name}
+            new_dof = old_dof[index]
         # Handle single integer index
         elif isinstance(index, int):
-            to_update_labels[dim] = {
-                "dof": [old_dof[index]],
-                "name": label_name,
-            }
+            new_dof = [old_dof[index]]
         # Handle lists or tensors
         elif isinstance(index, (list, torch.Tensor)):
             # Handle list of bools
             if isinstance(index, torch.Tensor) and index.dtype == torch.bool:
                 index = index.nonzero().squeeze()
-            to_update_labels[dim] = {
-                "dof": (
-                    [old_dof[i] for i in index]
-                    if isinstance(old_dof, list)
-                    else index
-                ),
-                "name": label_name,
-            }
+            new_dof = (
+                [old_dof[i] for i in index]
+                if isinstance(old_dof, list)
+                else index
+            )
         else:
             raise NotImplementedError(
                 f"Unsupported index type: {type(index)}. Expected slice, int, "
                 f"list, or torch.Tensor."
             )
+        return new_dof
 
     def __getitem__(self, index):
         """ "
@@ -589,14 +579,20 @@ def __getitem__(self, index):
 
         # Update labels based on the index
         offset = 0
+        removed = 0
         for dim, idx in enumerate(index):
-            if dim in self.stored_labels:
+            if dim in original_labels:
                 if isinstance(idx, int):
-                    selected_tensor = selected_tensor.unsqueeze(dim)
+                    # Compute the working dimension considering the removed
+                    # dimensions due to int index on a non labled dimension
+                    dim_ = dim - removed
+                    selected_tensor = selected_tensor.unsqueeze(dim_)
                 if idx != slice(None):
-                    self._update_single_label(
-                        original_labels, updated_labels, idx, dim, offset
-                    )
+                    # Update the labels for the selected dimension
+                    updated_labels[offset] = {
+                        "dof": self._update_single_label(idx, dim),
+                        "name": original_labels[dim]["name"],
+                    }
             else:
                 # Adjust label keys if dimension is reduced (case of integer
                 # index on a non-labeled dimension)
@@ -605,6 +601,7 @@ def __getitem__(self, index):
                         key - 1 if key > dim else key: value
                         for key, value in updated_labels.items()
                     }
+                    removed += 1
                     continue
             offset += 1
 

tests/test_label_tensor/test_label_tensor.py

@@ -278,3 +278,63 @@ def test_cat_bool(labels):
     assert selected.stored_labels[1]["dof"] == [f"s{i}" for i in range(10)]
     if isinstance(labels, dict):
         assert selected.stored_labels[0]["dof"] == ["a", "b"]
+
+
+def test_getitem_int():
+    data = torch.rand(20, 3)
+    labels = {1: {"name": 1, "dof": ["x", "y", "z"]}}
+    lt = LabelTensor(data, labels)
+    new = lt[0, 0]
+    assert new.ndim == 1
+    assert new.shape[0] == 1
+    assert torch.all(torch.isclose(data[0, 0], new))
+
+    data = torch.rand(20, 3, 2)
+    labels = {
+        1: {"name": 1, "dof": ["x", "y", "z"]},
+        2: {"name": 2, "dof": ["a", "b"]},
+    }
+    lt = LabelTensor(data, labels)
+    new = lt[0, 0, 0]
+    assert new.ndim == 2
+    assert new.shape[0] == 1
+    assert new.shape[1] == 1
+    assert torch.all(torch.isclose(data[0, 0, 0], new))
+    assert new.stored_labels[0]["dof"] == ["x"]
+    assert new.stored_labels[1]["dof"] == ["a"]
+
+    new = lt[0, 0, :]
+    assert new.ndim == 2
+    assert new.shape[0] == 1
+    assert new.shape[1] == 2
+    assert torch.all(torch.isclose(data[0, 0, :], new))
+    assert new.stored_labels[0]["dof"] == ["x"]
+    assert new.stored_labels[1]["dof"] == ["a", "b"]
+
+    new = lt[0, :, 1]
+    assert new.ndim == 2
+    assert new.shape[0] == 3
+    assert new.shape[1] == 1
+    assert torch.all(torch.isclose(data[0, :, 1], new.squeeze()))
+    assert new.stored_labels[0]["dof"] == ["x", "y", "z"]
+    assert new.stored_labels[1]["dof"] == ["b"]
+
+    labels.pop(2)
+    lt = LabelTensor(data, labels)
+    new = lt[0, 0, 0]
+    assert new.ndim == 1
+    assert new.shape[0] == 1
+    assert new.stored_labels[0]["dof"] == ["x"]
+
+    new = lt[:, 0, 0]
+    assert new.ndim == 2
+    assert new.shape[0] == 20
+    assert new.shape[1] == 1
+    assert new.stored_labels[1]["dof"] == ["x"]
+
+    new = lt[:, 0, :]
+    assert new.ndim == 3
+    assert new.shape[0] == 20
+    assert new.shape[1] == 1
+    assert new.shape[2] == 2
+    assert new.stored_labels[1]["dof"] == ["x"]