black

deepsensor/config.py

@@ -3,4 +3,4 @@
 """
 
 # Magnitude of diagonal to regularise matrices with in `backends` library used by `neuralprocesses`
-DEFAULT_LAB_EPSILON = 1e-6
+DEFAULT_LAB_EPSILON = 1e-6

deepsensor/data/processor.py

@@ -75,20 +75,22 @@ def _validate_maps(self, x1_map, x2_map):
         return x1_map, x2_map
 
     def _validate_xr(self, data: Union[xr.DataArray, xr.Dataset]):
-
         def _validate_da(da: xr.DataArray):
-            coord_names = [self.norm_params["coords"][coord]["name"] for coord in ["time", "x1", "x2"]]
+            coord_names = [
+                self.norm_params["coords"][coord]["name"]
+                for coord in ["time", "x1", "x2"]
+            ]
             if coord_names[0] not in da.dims:
                 # We don't have a time dimension.
                 coord_names = coord_names[1:]
             if list(da.dims) != coord_names:
                 raise ValueError(
                     f"Dimensions of {da.name} need to be {coord_names} but are {list(da.dims)}."
                 )
-            
+
         if isinstance(data, xr.DataArray):
             _validate_da(data)
-            
+
         elif isinstance(data, xr.Dataset):
             for var_ID, da in data.data_vars.items():
                 _validate_da(da)

deepsensor/tensorflow/__init__.py

@@ -19,6 +19,7 @@
 def convert_to_tensor(arr):
     return tf.convert_to_tensor(arr)
 
+
 from deepsensor import config as deepsensor_config
 from deepsensor import backend
 
@@ -27,4 +28,4 @@ def convert_to_tensor(arr):
 backend.convert_to_tensor = convert_to_tensor
 backend.str = "tf"
 
-B.epsilon = deepsensor_config.DEFAULT_LAB_EPSILON
+B.epsilon = deepsensor_config.DEFAULT_LAB_EPSILON

deepsensor/train/train.py

@@ -33,9 +33,7 @@ def set_gpu_default_device():
         # print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))
 
     else:
-        raise NotImplementedError(
-            f"Backend {deepsensor.backend.str} not implemented"
-        )
+        raise NotImplementedError(f"Backend {deepsensor.backend.str} not implemented")
 
 
 def train_epoch(

tests/test_data_processor.py

@@ -110,6 +110,7 @@ def test_same_names_xr(self):
             self.assert_allclose_xr(da_unnorm, da_raw),
             f"Original {type(da_raw).__name__} not restored.",
         )
+
     def test_wrong_order_xr_ds(self):
         """Order of dimensions in xarray must be: time, x1, x2"""
         ds_raw = _gen_data_xr(dims=("time", "lat", "lon"), data_vars=["var1", "var2"])