Format Python code with psf/black push (#328)

There appear to be some python formatting errors in
1813190. This pull request
uses the [psf/black](https://github.com/psf/black) formatter to fix
these issues.

dacapo/experiments/tasks/post_processors/threshold_post_processor.py

@@ -22,6 +22,7 @@
 
 logger = logging.getLogger(__name__)
 
+
 class ThresholdPostProcessor(PostProcessor):
     """
     A post-processor that applies a threshold to the prediction.

dacapo/experiments/tasks/predictors/distance_predictor.py

@@ -320,10 +320,8 @@ def process(
                 sampling = tuple(float(v) / 2 for v in voxel_size)
                 # fixing the sampling for 2D images
                 if len(boundaries.shape) < len(sampling):
-                    sampling = sampling[-len(boundaries.shape):]
-                distances = distance_transform_edt(
-                    boundaries, sampling=sampling
-                )
+                    sampling = sampling[-len(boundaries.shape) :]
+                distances = distance_transform_edt(boundaries, sampling=sampling)
                 distances = distances.astype(np.float32)
 
                 # restore original shape

dacapo/validate.py

@@ -247,7 +247,7 @@ def validate_run(run: Run, iteration: int, datasets_config=None):
                 #                 criterion,
                 #             )
                 dataset_iteration_scores.append(
-                [getattr(scores, criterion) for criterion in scores.criteria]
+                    [getattr(scores, criterion) for criterion in scores.criteria]
                 )
             except:
                 logger.error(
@@ -260,8 +260,6 @@ def validate_run(run: Run, iteration: int, datasets_config=None):
             # the evaluator
             # array_store.remove(output_array_identifier)
 
-
-
         iteration_scores.append(dataset_iteration_scores)
         # array_store.remove(prediction_array_identifier)
 

tests/operations/test_train.py

@@ -15,32 +15,35 @@
 
 logging.basicConfig(level=logging.INFO)
 
-from dacapo.experiments.architectures import DummyArchitectureConfig, CNNectomeUNetConfig
+from dacapo.experiments.architectures import (
+    DummyArchitectureConfig,
+    CNNectomeUNetConfig,
+)
 
 import pytest
 
 
-def unet_architecture(batch_norm, upsample,use_attention, three_d):
+def unet_architecture(batch_norm, upsample, use_attention, three_d):
     name = "3d_unet" if three_d else "2d_unet"
     name = f"{name}_bn" if batch_norm else name
     name = f"{name}_up" if upsample else name
     name = f"{name}_att" if use_attention else name
 
     if three_d:
-         return CNNectomeUNetConfig(
-                name=name,
-                input_shape=(188, 188, 188),
-                eval_shape_increase=(72, 72, 72),
-                fmaps_in=1,
-                num_fmaps=6,
-                fmaps_out=6,
-                fmap_inc_factor=2,
-                downsample_factors=[(2, 2, 2), (2, 2, 2), (2, 2, 2)],
-                constant_upsample=True,
-                upsample_factors=[(2, 2, 2)] if upsample else [],
-                batch_norm=batch_norm,
-                use_attention=use_attention,
-            )
+        return CNNectomeUNetConfig(
+            name=name,
+            input_shape=(188, 188, 188),
+            eval_shape_increase=(72, 72, 72),
+            fmaps_in=1,
+            num_fmaps=6,
+            fmaps_out=6,
+            fmap_inc_factor=2,
+            downsample_factors=[(2, 2, 2), (2, 2, 2), (2, 2, 2)],
+            constant_upsample=True,
+            upsample_factors=[(2, 2, 2)] if upsample else [],
+            batch_norm=batch_norm,
+            use_attention=use_attention,
+        )
     else:
         return CNNectomeUNetConfig(
             name=name,
@@ -61,7 +64,6 @@ def unet_architecture(batch_norm, upsample,use_attention, three_d):
         )
 
 
-
 # skip the test for the Apple Paravirtual device
 # that does not support Metal 2.0
 @pytest.mark.filterwarnings("ignore:.*Metal 2.0.*:UserWarning")
@@ -117,19 +119,15 @@ def test_train(
 @pytest.mark.parametrize("use_attention", [True, False])
 @pytest.mark.parametrize("three_d", [True, False])
 def test_train_unet(
-        datasplit,
-        task,
-        trainer,
-        batch_norm,
-        upsample,
-        use_attention,
-        three_d):
-
+    datasplit, task, trainer, batch_norm, upsample, use_attention, three_d
+):
     store = create_config_store()
     stats_store = create_stats_store()
     weights_store = create_weights_store()
 
-    architecture_config = unet_architecture(batch_norm, upsample,use_attention, three_d)
+    architecture_config = unet_architecture(
+        batch_norm, upsample, use_attention, three_d
+    )
 
     run_config = RunConfig(
         name=f"{architecture_config.name}_run",
@@ -167,6 +165,3 @@ def test_train_unet(
     training_stats = stats_store.retrieve_training_stats(run_config.name)
 
     assert training_stats.trained_until() == run_config.num_iterations
-
-
-