Documentation for v0.1 version (#199)

* Adding Equations, solving typos
* improve _code.rst
* the team rst and restuctore index.rst
* fixing errors

---------

Co-authored-by: Dario Coscia <dariocoscia@dhcp-015.eduroam.sissa.it>

pina/callbacks/processing_callbacks.py

@@ -6,20 +6,53 @@ import copy
 
 
 class MetricTracker(Callback):
-    """
-    PINA implementation of a Lightining Callback to track relevant
-    metrics during training.
-    """
+    
     def __init__(self):
+        """
+        PINA Implementation of a Lightning Callback for Metric Tracking.
+
+        This class provides functionality to track relevant metrics during the training process.
+
+        :ivar _collection: A list to store collected metrics after each training epoch.
+
+        :param trainer: The trainer object managing the training process.
+        :type trainer: pytorch_lightning.Trainer
+
+        :return: A dictionary containing aggregated metric values.
+        :rtype: dict
+
+        Example:
+            >>> tracker = MetricTracker()
+            >>> # ... Perform training ...
+            >>> metrics = tracker.metrics
+        """
         self._collection = []
 
     def on_train_epoch_end(self, trainer, __):
-        self._collection.append(copy.deepcopy(trainer.logged_metrics)) # track them
+        """
+        Collect and track metrics at the end of each training epoch.
+
+        :param trainer: The trainer object managing the training process.
+        :type trainer: pytorch_lightning.Trainer
+        :param _: Placeholder argument.
+
+        :return: None
+        :rtype: None
+        """
+        self._collection.append(copy.deepcopy(
+            trainer.logged_metrics))  # track them
 
     @property
     def metrics(self):
-        common_keys = set.intersection(*map(set, self._collection))
-        v = {k: torch.stack([dic[k] for dic in self._collection]) for k in common_keys}
-        return v
+        """
+        Aggregate collected metrics during training.
 
-    
+        :return: A dictionary containing aggregated metric values.
+        :rtype: dict
+        """
+        common_keys = set.intersection(*map(set, self._collection))
+        v = {
+            k: torch.stack([dic[k] for dic in self._collection])
+            for k in common_keys
+        }
+        return v