🎨 Format Python code with psf/black

dacapo/experiments/tasks/hot_distance_task.py

@@ -6,6 +6,7 @@
 
 import warnings
 
+
 class HotDistanceTask(Task):
     """
     A class to represent a hot distance task that use binary prediction and distance prediction.

dacapo/experiments/tasks/hot_distance_task_config.py

@@ -57,7 +57,6 @@ class HotDistanceTaskConfig(TaskConfig):
         },
     )
 
-
     kernel_size: int | None = attr.ib(
         default=None,
-    )
+    )

dacapo/experiments/tasks/post_processors/argmax_post_processor.py

@@ -133,7 +133,7 @@ def process(
             overwrite=True,
         )
 
-        read_roi = Roi((0,)*block_size.dims, block_size)
+        read_roi = Roi((0,) * block_size.dims, block_size)
         input_array = open_ds(
             f"{self.prediction_array_identifier.container.path}/{self.prediction_array_identifier.dataset}"
         )

dacapo/experiments/tasks/post_processors/threshold_post_processor.py

@@ -111,13 +111,15 @@ def process(
         if self.prediction_array._source_data.chunks is not None:
             block_size = self.prediction_array._source_data.chunks
 
-        write_size = Coordinate([
-            b * v
-            for b, v in zip(
-                block_size[-self.prediction_array.dims :],
-                self.prediction_array.voxel_size,
-            )
-        ])
+        write_size = Coordinate(
+            [
+                b * v
+                for b, v in zip(
+                    block_size[-self.prediction_array.dims :],
+                    self.prediction_array.voxel_size,
+                )
+            ]
+        )
         output_array = create_from_identifier(
             output_array_identifier,
             self.prediction_array.axis_names,

dacapo/experiments/tasks/predictors/distance_predictor.py

@@ -314,7 +314,7 @@ def process(
             channel_dim = True
         else:
             raise ValueError("Cannot handle multiple channel dims")
-        
+
         if not channel_dim:
             labels = labels[np.newaxis]
 

dacapo/experiments/tasks/predictors/hot_distance_predictor.py

@@ -49,7 +49,13 @@ class HotDistancePredictor(Predictor):
         This is a subclass of Predictor.
     """
 
-    def __init__(self, channels: List[str], scale_factor: float, mask_distances: bool, kernel_size: int):
+    def __init__(
+        self,
+        channels: List[str],
+        scale_factor: float,
+        mask_distances: bool,
+        kernel_size: int,
+    ):
         """
         Initializes the HotDistancePredictor.
 

dacapo/predict_local.py

@@ -75,8 +75,8 @@ def predict(
 
     model_device = str(next(model.parameters()).device).split(":")[0]
 
-    assert (
-        model_device == str(device)
+    assert model_device == str(
+        device
     ), f"Model is not on the right device, Model: {model_device}, Compute device: {device}"
 
     def predict_fn(block):
@@ -122,7 +122,7 @@ def predict_fn(block):
     task = daisy.Task(
         f"predict_{out_container}_{out_dataset}",
         total_roi=input_roi,
-        read_roi=Roi((0,)*input_size.dims, input_size),
+        read_roi=Roi((0,) * input_size.dims, input_size),
         write_roi=Roi(context, output_size),
         process_function=predict_fn,
         check_function=None,

tests/operations/test_architecture.py

@@ -68,7 +68,6 @@ def test_conv_dims(
             raise ValueError(f"Conv2d found in 3d unet {name}")
 
 
-
 @pytest.mark.parametrize(
     "run_config",
     [

tests/operations/test_mini.py

@@ -59,7 +59,13 @@ def test_mini(
     )
     task_config = build_test_task_config(task, data_dims, architecture_dims)
     architecture_config = build_test_architecture_config(
-        data_dims, architecture_dims, channels, batch_norm, upsample, use_attention, padding
+        data_dims,
+        architecture_dims,
+        channels,
+        batch_norm,
+        upsample,
+        use_attention,
+        padding,
     )
 
     run_config = RunConfig(

tests/operations/test_train.py

@@ -58,4 +58,3 @@ def test_large(
     training_stats = stats_store.retrieve_training_stats(run_config.name)
 
     assert training_stats.trained_until() == run_config.num_iterations
-

tests/operations/test_validate.py

@@ -38,4 +38,3 @@ def test_large(
     # test validating weights that don't exist
     with pytest.raises(FileNotFoundError):
         validate(run_config.name, 2)
-