black

.gitignore

@@ -10,3 +10,4 @@ dist/*
 .tox/
 _build
 *.png
+deepsensor.egg-info/

deepsensor/active_learning/algorithms.py

@@ -377,9 +377,9 @@ def _search(self, acquisition_fn: AcquisitionFunction):
                     self.X_s_mask.data
                 ] = importances
             else:
-                self.acquisition_fn_ds.loc[
-                    self.iteration, task["time"]
-                ] = importances.reshape(self.acquisition_fn_ds.shape[-2:])
+                self.acquisition_fn_ds.loc[self.iteration, task["time"]] = (
+                    importances.reshape(self.acquisition_fn_ds.shape[-2:])
+                )
 
         return np.mean(importances_list, axis=0)
 

deepsensor/config.py

@@ -2,7 +2,6 @@
 Configuration file for deepsensor
 """
 
-
 DEFAULT_LAB_EPSILON = 1e-6
 """
 Magnitude of diagonal to regularise matrices with in ``backends`` library used

deepsensor/model/model.py

@@ -577,10 +577,10 @@ def unnormalise_pred_array(arr, **kwargs):
                 if unnormalise:
                     if param == "samples":
                         for sample_i in range(n_samples):
-                            prediction_arrs["samples"][
-                                sample_i
-                            ] = unnormalise_pred_array(
-                                prediction_arrs["samples"][sample_i]
+                            prediction_arrs["samples"][sample_i] = (
+                                unnormalise_pred_array(
+                                    prediction_arrs["samples"][sample_i]
+                                )
                             )
                     elif param in scale_and_offset_params:
                         prediction_arrs[param] = unnormalise_pred_array(arr)

tests/test_model.py

@@ -269,9 +269,9 @@ def test_prediction_shapes_highlevel(self, target_dim):
             tasks,
             X_t=self.da,
             n_samples=n_samples,
-            unnormalise=True
-            if target_dim == 1
-            else False,  # TODO fix unnormalising for multiple equally named targets
+            unnormalise=(
+                True if target_dim == 1 else False
+            ),  # TODO fix unnormalising for multiple equally named targets
         )
         assert [isinstance(ds, xr.Dataset) for ds in pred.values()]
         for var_ID in pred: