black formatting

deepsensor/data/loader.py

@@ -50,7 +50,7 @@ def __init__(
     ) -> None:
         """
         Initialise a TaskLoader object.
-    
+
         The behaviour is the following:
         - If all data passed as paths, load the data and overwrite the paths with the loaded data
         - Either all data is passed as paths, or all data is passed as loaded data (else ValueError)

deepsensor/data/processor.py

@@ -240,7 +240,7 @@ def check_params_computed(self, var_ID, method) -> bool:
             and "params" in self.config[var_ID]
         ):
             return True
-        
+
         return False
 
     def add_to_config(self, var_ID, **kwargs):
@@ -251,7 +251,7 @@ def get_norm_params(self, var_ID, data, method=None):
         """
         Get pre-computed normalisation params or compute them for variable
         ``var_ID``.
-        
+
         .. note:
 
             TODO do we need to pass var_ID? Can we just use name of data?

deepsensor/data/task.py

@@ -99,7 +99,7 @@ def op(self, f, op_flag=None):
 
         Returns
         -------
-        task : dict. 
+        task : dict.
             Task dictionary with f applied to the array elements and
             op_flag set in the ``ops`` key.
         """

deepsensor/model/convnp.py

@@ -241,7 +241,7 @@ def __init__(self, model_ID: str):
         super().__init__()
 
         self.load(model_ID)
-        
+
     @dispatch
     def __init__(
         self,
@@ -251,7 +251,7 @@ def __init__(
     ):
         """Instantiate a model from a folder containing model weights and config."""
         super().__init__(data_processor, task_loader)
-        
+
         self.load(model_ID)
 
     def save(self, model_ID: str):
@@ -290,7 +290,6 @@ def load(self, model_ID: str):
 
     @classmethod
     def modify_task(cls, task):
-
         """
         Cast numpy arrays to TensorFlow or PyTorch tensors, add batch dim, and
         mask NaNs.
@@ -305,7 +304,7 @@ def modify_task(cls, task):
         ...
             ...
         """
- 
+
         if "target_nans_removed" not in task["ops"]:
             task = task.remove_nans_from_task_Y_t_if_present()
         if "batch_dim" not in task["ops"]:

deepsensor/model/model.py

@@ -563,7 +563,7 @@ def predict(
                 X_t_arr = mask_coord_array_normalised(X_t_arr, X_t_mask_normalised)
             else:
                 X_t_arr = (X_t_normalised["x1"].values, X_t_normalised["x2"].values)
-                
+
         elif mode == "off-grid":
             X_t_arr = X_t_normalised.reset_index()[["x1", "x2"]].values.T