fix laplacian

pina/operators.py

@@ -209,49 +209,21 @@ def laplacian(output_, input_, components=None, d=None, method="std"):
 
     elif method == "std":
         if len(components) == 1:
-            # result = scalar_laplace(output_, input_, components, d) # TODO check (from 0.1)
-            grad_output = grad(output_, input_, components=components, d=d)
-            to_append_tensors = []
-            for i, label in enumerate(grad_output.labels):
-                gg = grad(grad_output, input_, d=d, components=[label])
-                gg = gg.extract([gg.labels[i]])
-
-                to_append_tensors.append(gg)
+            result = scalar_laplace(output_, input_, components, d)
             labels = [f"dd{components[0]}"]
-            result = LabelTensor.summation(tensors=to_append_tensors)
-            result.labels = labels
-        else:
-    #         result = torch.empty( # TODO check (from 0.1)
-    #             size=(input_.shape[0], len(components)),
-    #             dtype=output_.dtype,
-    #             device=output_.device,
-    #         )
-    #         labels = [None] * len(components)
-    #         for idx, c in enumerate(components):
-    #             result[:, idx] = scalar_laplace(output_, input_, c, d).flatten()
-    #             labels[idx] = f"dd{c}"
 
-    # result = result.as_subclass(LabelTensor)
-    # result.labels = labels
+        else:
             result = torch.empty(
                 input_.shape[0], len(components), device=output_.device
             )
             labels = [None] * len(components)
-            to_append_tensors = [None] * len(components)
-            for idx, (ci, di) in enumerate(zip(components, d)):
+            for idx, ci in enumerate(components):
+                result[:, idx] = scalar_laplace(output_, input_, ci, d).flatten()
+                labels[idx] = f"dd{ci}"
 
-                if not isinstance(ci, list):
-                    ci = [ci]
-                if not isinstance(di, list):
-                    di = [di]
-
-                grad_output = grad(output_, input_, components=ci, d=di)
-                result[:, idx] = grad(grad_output, input_, d=di).flatten()
-                to_append_tensors[idx] = grad(grad_output, input_, d=di)
-                labels[idx] = f"dd{ci[0]}dd{di[0]}"
-            result = LabelTensor.cat(tensors=to_append_tensors,
-                                     dim=output_.tensor.ndim - 1)
+        result = result.as_subclass(LabelTensor)
         result.labels = labels
+    
     return result