black formatting

em_workflows/brt/flow.py

@@ -322,6 +322,7 @@ def list_paired_files(fnames: List[Path]) -> List[Path]:
                 pairs.append(Path(fname_no_b))
     return pairs
 
+
 @task
 def cleanup_files(file_path: FilePath, pattern=str):
     f = f"{file_path.working_dir.as_posix()}/{pattern}"
@@ -331,6 +332,7 @@ def cleanup_files(file_path: FilePath, pattern=str):
         os.remove(_file)
     print(files_to_rm)
 
+
 # @task
 # def check_inputs_paired(fps: List[Path]):
 #    """
@@ -424,10 +426,16 @@ def cleanup_files(file_path: FilePath, pattern=str):
     tilt_movie_assets = gen_tilt_movie.map(
         file_path=fps, upstream_tasks=[keyimg_assets]
     )
-    cleanup_files.map(file_path=fps, pattern=unmapped("*_align_*.mrc"),
-            upstream_tasks=[tilt_movie_assets, thumb_assets, keyimg_assets])
-    cleanup_files.map(file_path=fps, pattern=unmapped("*ali*.jpg"),
-            upstream_tasks=[tilt_movie_assets, thumb_assets, keyimg_assets])
+    cleanup_files.map(
+        file_path=fps,
+        pattern=unmapped("*_align_*.mrc"),
+        upstream_tasks=[tilt_movie_assets, thumb_assets, keyimg_assets],
+    )
+    cleanup_files.map(
+        file_path=fps,
+        pattern=unmapped("*ali*.jpg"),
+        upstream_tasks=[tilt_movie_assets, thumb_assets, keyimg_assets],
+    )
     # END TILT MOVIE GENERATION
 
     # START RECONSTR MOVIE GENERATION:
@@ -444,10 +452,16 @@ def cleanup_files(file_path: FilePath, pattern=str):
         file_path=fps, upstream_tasks=[averagedVolume_assets]
     )
     recon_movie_assets = gen_recon_movie.map(file_path=fps, upstream_tasks=[ave_jpgs])
-    cleanup_files.map(file_path=fps, pattern=unmapped("*_mp4.*.jpg"),
-            upstream_tasks=[recon_movie_assets, ave_jpgs])
-    cleanup_files.map(file_path=fps, pattern=unmapped("*_ave*.mrc"),
-            upstream_tasks=[recon_movie_assets, ave_jpgs])
+    cleanup_files.map(
+        file_path=fps,
+        pattern=unmapped("*_mp4.*.jpg"),
+        upstream_tasks=[recon_movie_assets, ave_jpgs],
+    )
+    cleanup_files.map(
+        file_path=fps,
+        pattern=unmapped("*_ave*.mrc"),
+        upstream_tasks=[recon_movie_assets, ave_jpgs],
+    )
     #    # END RECONSTR MOVIE
 
     # Binned volume assets, for volslicer.

em_workflows/dm_conversion/flow.py

@@ -55,9 +55,9 @@ def convert_2d_mrc_to_tiff(file_path: FilePath) -> None:
     used the smaller of x and y (4092/1024)
     """
     utils.log(f"Checking {file_path} for mrc inputs")
-    large_dim = 1024 # TODO probably config this
+    large_dim = 1024  # TODO probably config this
     if file_path.fp_in.suffix.lower() == ".mrc":
-        # nifti_fp = neuroglancer.gen_niftis.__wrapped__(file_path) 
+        # nifti_fp = neuroglancer.gen_niftis.__wrapped__(file_path)
         # utils.log(f"+++++++++++++++++++++++++++++++++++++++++++++")
         # min_max_histo = neuroglancer.gen_min_max_histo(file_path)
         # utils.log(min_max_histo)
@@ -179,7 +179,6 @@ def scale_jpegs(file_path: FilePath, size: str) -> Optional[dict]:
     return asset_elt
 
 
-
 # @task
 # def list_files(input_dir: Path, exts: List[str], single_file: str = None) -> List[Path]:
 #    """
@@ -212,8 +211,6 @@ def scale_jpegs(file_path: FilePath, size: str) -> Optional[dict]:
 #    return _files
 
 
-
-
 with Flow(
     "dm_to_jpeg",
     state_handlers=[utils.notify_api_completion, utils.notify_api_running],
@@ -269,7 +266,9 @@ def scale_jpegs(file_path: FilePath, size: str) -> Optional[dict]:
         prim_fp=callback_with_thumbs, asset=keyimg_assets
     )
     # finally filter error states, and convert to JSON and send.
-    rm_workdirs = utils.cleanup_workdir.map(fp=fps, upstream_tasks=[callback_with_keyimgs])
+    rm_workdirs = utils.cleanup_workdir.map(
+        fp=fps, upstream_tasks=[callback_with_keyimgs]
+    )
     filtered_callback = utils.filter_results(callback_with_keyimgs)
 
     callback_sent = utils.send_callback_body(

em_workflows/sem_tomo/flow.py

@@ -6,6 +6,7 @@
 from prefect import Flow, task, Parameter, unmapped
 from prefect.run_configs import LocalRun
 from em_workflows.config import Config
+
 # from em_workflows.shell_task_echo import ShellTaskEcho
 from em_workflows.utils import utils
 from em_workflows.utils import neuroglancer as ng
@@ -261,7 +262,7 @@ def gen_keyimg_small(fp_in: FilePath) -> Dict:
     - The corrected mrc file is then contrast adjusted with mean std dev magic numbers "150,40" in gen_newstack_norm_command(), this is referred to as the base mrc
     - A movie is created using the base.mrc file.
     - the midpoint of that file is computed, and snapshots are created using this midpoint.
-    - We now want to create the pyramid assets, for neuroglancer / viewer. 
+    - We now want to create the pyramid assets, for neuroglancer / viewer.
     - Firstly create nifti file using the base mrc, then convert this to ng format.
     - To conclude, send callback stating the location of the various outputs.
     """
@@ -298,7 +299,6 @@ def gen_keyimg_small(fp_in: FilePath) -> Dict:
         fp_in=fps, upstream_tasks=[stretchs, align_mrcs]
     )
 
-
     base_mrcs = gen_newstack_norm_command.map(
         fp_in=fps, upstream_tasks=[corrected_mrc_assets]
     )
@@ -338,7 +338,9 @@ def gen_keyimg_small(fp_in: FilePath) -> Dict:
     callback_with_corr_movies = utils.add_asset.map(
         prim_fp=callback_with_corr_mrcs, asset=corrected_movie_assets
     )
-    rm_workdirs = utils.cleanup_workdir.map(fp=fps, upstream_tasks=[callback_with_corr_movies])
+    rm_workdirs = utils.cleanup_workdir.map(
+        fp=fps, upstream_tasks=[callback_with_corr_movies]
+    )
 
     # finally filter error states, and convert to JSON and send.
     filtered_callback = utils.filter_results(callback_with_corr_movies)