make minor formatting changes

deepsensor/data/loader.py

@@ -810,7 +810,7 @@ def time_slice_variable(self, var, date, delta_t=0):
             raise ValueError(f"Unknown variable type {type(var)}")
         return var
 
-    def task_generation(     # noqa: D102
+    def task_generation(  # noqa: D102
         self,
         date: pd.Timestamp,
         context_sampling: Union[

deepsensor/eval/metrics.py

@@ -3,8 +3,7 @@
 
 
 def compute_errors(pred: Prediction, target: xr.Dataset) -> xr.Dataset:
-    """
-    Compute errors between predictions and targets.
+    """Compute errors between predictions and targets.
 
     Args:
         pred: Prediction object.

deepsensor/model/convnp.py

@@ -1,4 +1,4 @@
-# ruff: noqa: D102 
+# ruff: noqa: D102
 
 import copy
 import os.path

deepsensor/model/model.py

@@ -650,8 +650,7 @@ def unnormalise_pred_array(arr, **kwargs):
 
 
 def add_valid_time_coord_to_pred_and_move_time_dims(pred: Prediction) -> Prediction:
-    """
-    Add a valid time coordinate "time" to a Prediction object based on the
+    """Add a valid time coordinate "time" to a Prediction object based on the
     initialisation times "init_time" and lead times "lead_time", and
     reorder the time dims from ("lead_time", "init_time") to ("init_time", "lead_time").
 
@@ -680,7 +679,7 @@ def add_valid_time_coord_to_pred_and_move_time_dims(pred: Prediction) -> Predict
     return pred
 
 
-def main():  # pragma: no cover # noqa
+def main():  # pragma: no cover # noqa: D103
     import deepsensor.tensorflow
     from deepsensor.data.loader import TaskLoader
     from deepsensor.data.processor import DataProcessor

deepsensor/model/pred.py

@@ -145,18 +145,16 @@ def assign(
         data: np.ndarray,
         lead_times: Optional[List[pd.Timedelta]] = None,
     ):
-        """
-
-        Args:
-            prediction_parameter (str)
-                ...
-            date (Union[str, pd.Timestamp])
-                ...
-            data (np.ndarray)
-                If off-grid: Shape (N_var, N_targets) or (N_samples, N_var, N_targets).
-                If on-grid: Shape (N_var, N_x1, N_x2) or (N_samples, N_var, N_x1, N_x2).
-            lead_time (pd.Timedelta, optional)
-                Lead time of the forecast. Required if forecasting_mode is True. Default None.
+        """Args:
+        prediction_parameter (str)
+            ...
+        date (Union[str, pd.Timestamp])
+            ...
+        data (np.ndarray)
+            If off-grid: Shape (N_var, N_targets) or (N_samples, N_var, N_targets).
+            If on-grid: Shape (N_var, N_x1, N_x2) or (N_samples, N_var, N_x1, N_x2).
+        lead_time (pd.Timedelta, optional)
+            Lead time of the forecast. Required if forecasting_mode is True. Default None.
         """
         if self.forecasting_mode:
             assert (