add update_data and input functions

pina/data/dataset.py

@@ -54,6 +54,7 @@ class PinaDataset(Dataset):
             automatic_batching if automatic_batching is not None else True
         )
         self.stack_fn = {}
+        self.is_graph_dataset = False
         # Determine stacking functions for each data type (used in collate_fn)
         for k, v in data_dict.items():
             if isinstance(v, LabelTensor):
@@ -64,6 +65,7 @@ class PinaDataset(Dataset):
                 isinstance(item, (Data, Graph)) for item in v
             ):
                 self.stack_fn[k] = LabelBatch.from_data_list
+                self.is_graph_dataset = True
             else:
                 raise ValueError(
                     f"Unsupported data type for stacking: {type(v)}"
@@ -104,55 +106,34 @@ class PinaDataset(Dataset):
                     [data[i] for i in idx_list]
                 )
             else:
+                print(data)
                 to_return[field_name] = data[idx_list]
         return to_return
 
-
-class PinaGraphDataset(Dataset):
-    def __init__(self, data_dict, automatic_batching=None):
+    def update_data(self, update_dict):
         """
-        Initialize the instance by storing the conditions dictionary.
-
-        :param dict conditions_dict: A dictionary mapping condition names to
-            their respective data. Each key represents a condition name, and the
-            corresponding value is a dictionary containing the associated data.
+        Update the dataset's data in-place.
+        :param dict update_dict: A dictionary where keys are condition names
+            and values are dictionaries with updated data for those conditions.
         """
+        for field_name, updates in update_dict.items():
+            if field_name not in self.data:
+                raise KeyError(
+                    f"Condition '{field_name}' not found in dataset."
+                )
+            if not isinstance(updates, (LabelTensor, torch.Tensor)):
+                raise ValueError(
+                    f"Updates for condition '{field_name}' must be of type "
+                    f"LabelTensor or torch.Tensor."
+                )
+            self.data[field_name] = updates
 
-        # Store the conditions dictionary
-        self.data = data_dict
-        self.automatic_batching = (
-            automatic_batching if automatic_batching is not None else True
-        )
-
-    def __len__(self):
-        return len(next(iter(self.data.values())))
-
-    def __getitem__(self, idx):
+    @property
+    def input(self):
         """
-        Return the data at the given index in the dataset.
+        Get the input data from the dataset.
 
-        :param int idx: Index.
-        :return: A dictionary containing the data at the given index.
-        :rtype: dict
+        :return: The input data.
+        :rtype: torch.Tensor | LabelTensor | Data | Graph
         """
-
-        if self.automatic_batching:
-            # Return the data at the given index
-            return {
-                field_name: data[idx] for field_name, data in self.data.items()
-            }
-        return idx
-
-    def _getitem_from_list(self, idx_list):
-        """
-        Return data from the dataset given a list of indices.
-
-        :param list[int] idx_list: List of indices.
-        :return: A dictionary containing the data at the given indices.
-        :rtype: dict
-        """
-
-        return {
-            field_name: [data[i] for i in idx_list]
-            for field_name, data in self.data.items()
-        }
+        return self.data["input"]