Filippo0.2 (#361)

* Add summation and remove deepcopy (only for tensors) in LabelTensor class
* Update operators for compatibility with updated LabelTensor implementation
* Implement labels.setter in LabelTensor class
* Update LabelTensor

---------

Co-authored-by: FilippoOlivo <filippo@filippoolivo.com>

tests/test_label_tensor.py

@@ -17,12 +17,14 @@ labels_row = {
         "dof": range(20)
     }
 }
+labels_list = ['x', 'y', 'z']
 labels_all = labels_column | labels_row
 
-@pytest.mark.parametrize("labels", [labels_column, labels_row, labels_all])
+@pytest.mark.parametrize("labels", [labels_column, labels_row, labels_all, labels_list])
 def test_constructor(labels):
     LabelTensor(data, labels)
 
+
 def test_wrong_constructor():
     with pytest.raises(ValueError):
         LabelTensor(data, ['a', 'b'])
@@ -61,7 +63,6 @@ def test_extract_2D(labels_te):
     assert torch.all(torch.isclose(data[2,2].reshape(1, 1), new))
 
 def test_extract_3D():
-    labels = labels_all
     data = torch.rand(20, 3, 4)
     labels = {
         1: {
@@ -80,6 +81,7 @@ def test_extract_3D():
 
     tensor = LabelTensor(data, labels)
     new = tensor.extract(labels_te)
+    tensor2 = LabelTensor(data, labels)
     assert new.ndim == tensor.ndim
     assert new.shape[0] == 20
     assert new.shape[1] == 2
@@ -88,6 +90,10 @@ def test_extract_3D():
         data[:, 0::2, 1:4].reshape(20, 2, 3),
         new
     ))
+    assert tensor2.ndim == tensor.ndim
+    assert tensor2.shape == tensor.shape
+    assert tensor.labels == tensor2.labels
+    assert new.shape != tensor.shape
 
 def test_concatenation_3D():
     data_1 = torch.rand(20, 3, 4)
@@ -146,3 +152,51 @@ def test_concatenation_3D():
     assert lt_cat.labels[2]['dof'] == range(5)
     assert lt_cat.labels[0]['dof'] == range(20)
     assert lt_cat.labels[1]['dof'] == range(3)
+
+
+def test_summation():
+    lt1 = LabelTensor(torch.ones(20,3), labels_all)
+    lt2 = LabelTensor(torch.ones(30,3), ['x', 'y', 'z'])
+    with pytest.raises(RuntimeError):
+        LabelTensor.summation([lt1, lt2])
+    lt1 = LabelTensor(torch.ones(20,3), labels_all)
+    lt2 = LabelTensor(torch.ones(20,3), labels_all)
+    lt_sum = LabelTensor.summation([lt1, lt2])
+    assert lt_sum.ndim == lt_sum.ndim
+    assert lt_sum.shape[0] == 20
+    assert lt_sum.shape[1] == 3
+    assert lt_sum.labels == labels_all
+    assert torch.eq(lt_sum.tensor, torch.ones(20,3)*2).all()
+    lt1 = LabelTensor(torch.ones(20,3), labels_all)
+    lt2 = LabelTensor(torch.ones(20,3), labels_all)
+    lt3 = LabelTensor(torch.zeros(20, 3), labels_all)
+    lt_sum = LabelTensor.summation([lt1, lt2, lt3])
+    assert lt_sum.ndim == lt_sum.ndim
+    assert lt_sum.shape[0] == 20
+    assert lt_sum.shape[1] == 3
+    assert lt_sum.labels == labels_all
+    assert torch.eq(lt_sum.tensor, torch.ones(20,3)*2).all()
+
+def test_append_3D():
+    data_1 = torch.rand(20, 3, 4)
+    labels_1 = ['x', 'y', 'z', 'w']
+    lt1 = LabelTensor(data_1, labels_1)
+    data_2 = torch.rand(50, 3, 4)
+    labels_2 = ['x', 'y', 'z', 'w']
+    lt2 = LabelTensor(data_2, labels_2)
+    lt1 = lt1.append(lt2)
+    assert lt1.shape == (70, 3, 4)
+    assert lt1.labels[0]['dof'] == range(70)
+    assert lt1.labels[1]['dof'] == range(3)
+    assert lt1.labels[2]['dof'] == ['x', 'y', 'z', 'w']
+    data_1 = torch.rand(20, 3, 2)
+    labels_1 = ['x', 'y']
+    lt1 = LabelTensor(data_1, labels_1)
+    data_2 = torch.rand(20, 3, 2)
+    labels_2 = ['z', 'w']
+    lt2 = LabelTensor(data_2, labels_2)
+    lt1 = lt1.append(lt2, mode='cross')
+    assert lt1.shape == (20, 3, 4)
+    assert lt1.labels[0]['dof'] == range(20)
+    assert lt1.labels[1]['dof'] == range(3)
+    assert lt1.labels[2]['dof'] == ['x', 'y', 'z', 'w']