Additional improvement related to #395

pina/label_tensor.py

@@ -380,20 +380,21 @@ def _update_single_label(self, old_labels, to_update_labels, index, dim,
         """
         old_dof = old_labels[to_update_dim]['dof']
         label_name = old_labels[dim]['name']
-
+        # Handle slicing
         if isinstance(index, slice):
-            # Handle slicing
             to_update_labels[dim] = {'dof': old_dof[index], 'name': label_name}
+        # Handle single integer index
         elif isinstance(index, int):
-            # Handle single integer index
             to_update_labels[dim] = {'dof': [old_dof[index]],
                                      'name': label_name}
+        # Handle lists or tensors
         elif isinstance(index, (list, torch.Tensor)):
-            # Handle lists or tensors
-            indices = [index] if isinstance(index, (int, str)) else index
+            # 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 indices] if isinstance(old_dof,
-                                                                   list) else indices,
+                'dof': [old_dof[i] for i in index] if isinstance(old_dof,
+                                                                 list) else index,
                 'name': label_name
             }
         else:

tests/test_label_tensor/test_label_tensor.py

@@ -281,3 +281,17 @@ def test_sorting():
     assert torch.eq(lt_sorted.tensor[:, 1, :], torch.ones(20, 5) * 2).all()
     assert torch.eq(lt_sorted.tensor[:, 2, :], torch.ones(20, 5) * 3).all()
     assert torch.eq(lt_sorted.tensor[:, 3, :], torch.ones(20, 5) * 4).all()
+
+
+@pytest.mark.parametrize("labels",
+                         [[f's{i}' for i in range(10)],
+                          {0: {'dof': ['a', 'b', 'c']},
+                           1: {'dof': [f's{i}' for i in range(10)]}}])
+def test_cat_bool(labels):
+    out = torch.randn((3, 10))
+    out = LabelTensor(out, labels)
+    selected = out[torch.tensor([True, True, False])]
+    assert selected.shape == (2, 10)
+    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']