black formatting

dacapo/experiments/datasplits/datasets/arrays/concat_array_config.py

@@ -42,7 +42,7 @@ class ConcatArrayConfig(ArrayConfig):
         },
     )
 
-    def array(self, mode:str="r") -> Array:
+    def array(self, mode: str = "r") -> Array:
         arrays = [config.array(mode) for _, config in self.source_array_configs]
 
         out_array = Array(

dacapo/experiments/datasplits/datasets/arrays/dvid_array_config.py

@@ -32,6 +32,7 @@ def array(self, mode: str = "r") -> Array:
         # directly read from DVID
         raise NotImplementedError("NotImplemented")
         from dacapo.ext import NoSuchModule
+
         try:
             from neuclease.dvid import fetch_info, fetch_labelmap_voxels, fetch_raw
         except ImportError:

dacapo/experiments/datasplits/datasets/arrays/logical_or_array_config.py

@@ -41,4 +41,4 @@ def array(self, mode: str = "r") -> Array:
 
         # mark data as non-writable
         out_array.lazy_op(lambda data: data)
-        return out_array
+        return out_array

dacapo/experiments/datasplits/datasets/arrays/ones_array_config.py

@@ -34,4 +34,4 @@ def array(self, mode: str = "r") -> Array:
             voxel_size=source_array.voxel_size,
             axis_names=source_array.axis_names,
             units=source_array.units,
-        )
+        )

dacapo/experiments/datasplits/datasets/arrays/resampled_array_config.py

@@ -40,4 +40,4 @@ class ResampledArrayConfig(ArrayConfig):
     def array(self, mode: str = "r") -> Array:
         # This is non trivial. We want to upsample or downsample the source
         # array lazily. Not entirely sure how to do this with dask arrays.
-        raise NotImplementedError()
+        raise NotImplementedError()

dacapo/experiments/datasplits/datasets/arrays/tiff_array_config.py

@@ -40,7 +40,9 @@ class TiffArrayConfig(ArrayConfig):
     voxel_size: Coordinate = attr.ib(
         metadata={"help_text": "The size of each voxel in each dimension."}
     )
-    axis_names: list[str] = attr.ib(metadata={"help_text": "The axis_names of your array"})
+    axis_names: list[str] = attr.ib(
+        metadata={"help_text": "The axis_names of your array"}
+    )
     units: list[str] = attr.ib(metadata={"help_text": "The units of your array"})
 
     def array(self, mode: str = "r") -> Array:
@@ -51,4 +53,4 @@ def array(self, mode: str = "r") -> Array:
             voxel_size=self.voxel_size,
             axis_names=self.axis_names,
             units=self.units,
-        )
+        )

dacapo/experiments/datasplits/datasets/dummy_dataset.py

@@ -1,6 +1,7 @@
 from .dataset import Dataset
 from funlib.persistence import Array
 
+
 class DummyDataset(Dataset):
     """
     DummyDataset is a child class of the Dataset. This class has property 'raw' of Array type and a name.

dacapo/experiments/tasks/predictors/distance_predictor.py

@@ -130,9 +130,7 @@ def create_target(self, gt: Array):
         """
         Turn the ground truth labels into a distance transform.
         """
-        distances = self.process(
-            gt[:], gt.voxel_size, self.norm, self.dt_scale_factor
-        )
+        distances = self.process(gt[:], gt.voxel_size, self.norm, self.dt_scale_factor)
         return np_to_funlib_array(
             distances,
             gt.roi.offset,

dacapo/experiments/trainers/gunpowder_trainer.py

@@ -374,9 +374,11 @@ def iterate(self, num_iterations, model, optimizer, device):
                             snapshot_array_identifier,
                             v.axis_names,
                             v.roi,
-                            v.shape[0]
-                            if (v.channel_dims == 1 and v.shape[0] > 1)
-                            else None,
+                            (
+                                v.shape[0]
+                                if (v.channel_dims == 1 and v.shape[0] > 1)
+                                else None
+                            ),
                             v.voxel_size,
                             v.dtype if not v.dtype == bool else np.float32,
                             model.output_shape * v.voxel_size,
@@ -386,7 +388,7 @@ def iterate(self, num_iterations, model, optimizer, device):
                         array = open_from_identifier(
                             snapshot_array_identifier, mode="a"
                         )
-                    
+
                     # neuroglancer doesn't allow bools
                     if not v.dtype == bool:
                         data = v[:]

dacapo/store/array_store.py

@@ -1,5 +1,3 @@
-
-
 import zarr
 import neuroglancer
 import attr

dacapo/tmp.py

@@ -75,9 +75,11 @@ def create_from_identifier(
         voxel_size=voxel_size,
         axis_names=axis_names,
         dtype=dtype,
-        chunk_shape=(*num_channels, *write_size / voxel_size)
-        if write_size is not None
-        else None,
+        chunk_shape=(
+            (*num_channels, *write_size / voxel_size)
+            if write_size is not None
+            else None
+        ),
         mode=mode if overwrite is False else "w",
     )
 

tests/conf.py

@@ -1,3 +1,3 @@
 import multiprocessing as mp
 
-mp.set_start_method('fork', force=True)
+mp.set_start_method("fork", force=True)