used new version of black to reformat

src/allencell_ml_segmenter/core/progress_tracker.py

@@ -1,7 +1,7 @@
 from abc import abstractmethod
 
 
-class ProgressTracker():
+class ProgressTracker:
     """
     Base class for all ProgressTrackers to inherit from. A ProgressTracker
     maintains an integer measure of progress between progress_minimum and
@@ -30,7 +30,12 @@ def set_progress_maximum(self, progress_maximum: int) -> None:
         self._progress_maximum = progress_maximum
 
     def set_progress(self, progress: int) -> None:
-        self._progress = progress if progress >= self._progress_minimum and progress <= self._progress_maximum else self._progress
+        self._progress = (
+            progress
+            if progress >= self._progress_minimum
+            and progress <= self._progress_maximum
+            else self._progress
+        )
 
     @abstractmethod
     def start_tracker(self) -> None:

src/allencell_ml_segmenter/core/view.py

@@ -35,7 +35,10 @@ def __init__(self, progress_tracker: ProgressTracker, parent=None):
         self._progress_tracker: ProgressTracker = progress_tracker
 
     def run(self):
-        while self._progress_tracker.get_progress() < self._progress_tracker.get_progress_maximum():
+        while (
+            self._progress_tracker.get_progress()
+            < self._progress_tracker.get_progress_maximum()
+        ):
             self.task_progress.emit(self._progress_tracker.get_progress())
             self.msleep(100)
 
@@ -59,7 +62,7 @@ def startLongTaskWithProgressBar(self, progress_tracker: ProgressTracker):
             "Cancel",
             progress_tracker.get_progress_minimum(),
             progress_tracker.get_progress_maximum(),
-            self
+            self,
         )
         self.progressDialog.setWindowTitle(f"{self.getTypeOfWork()} Progress")
         self.progressDialog.setWindowModality(Qt.ApplicationModal)

src/allencell_ml_segmenter/training/metrics_csv_event_handler.py

@@ -23,10 +23,14 @@ def _get_latest_epoch(self) -> int:
         with self._target_path.open("r", newline="") as fr:
             dict_reader: DictReader = DictReader(fr)
             for row in dict_reader:
-                latest = int(row["epoch"]) if int(row["epoch"]) > latest else latest
+                latest = (
+                    int(row["epoch"]) if int(row["epoch"]) > latest else latest
+                )
 
         return latest
 
     def on_any_event(self, event: FileSystemEvent) -> None:
-        if self._target_path.exists() and self._target_path.samefile(event.src_path):
+        if self._target_path.exists() and self._target_path.samefile(
+            event.src_path
+        ):
             self._progress_callback(self._get_latest_epoch())

src/allencell_ml_segmenter/training/metrics_csv_progress_tracker.py

@@ -2,7 +2,9 @@
 from watchdog.observers.api import BaseObserver
 from watchdog.observers import Observer
 from allencell_ml_segmenter.core.progress_tracker import ProgressTracker
-from allencell_ml_segmenter.training.metrics_csv_event_handler import MetricsCSVEventHandler
+from allencell_ml_segmenter.training.metrics_csv_event_handler import (
+    MetricsCSVEventHandler,
+)
 
 
 class MetricsCSVProgressTracker(ProgressTracker):
@@ -12,23 +14,34 @@ class MetricsCSVProgressTracker(ProgressTracker):
     measure of progress. Relies heavily on current cyto-dl file logging procedure.
     """
 
-    def __init__(self, csv_path: Path, progress_minimum: int = 0, progress_maximum: int = 0):
+    def __init__(
+        self,
+        csv_path: Path,
+        progress_minimum: int = 0,
+        progress_maximum: int = 0,
+    ):
         super().__init__(progress_minimum, progress_maximum)
 
         self._csv_path: Path = csv_path
         if not csv_path.exists():
             csv_path.mkdir(parents=True)
 
         self._target_path: Path = (
-            csv_path / f"version_{self._get_last_csv_version() + 1}" / "metrics.csv"
+            csv_path
+            / f"version_{self._get_last_csv_version() + 1}"
+            / "metrics.csv"
         )
         self._observer: BaseObserver = None
 
     def start_tracker(self) -> None:
         self.stop_tracker()
         self._observer = Observer()
-        event_handler: MetricsCSVEventHandler = MetricsCSVEventHandler(self._target_path, self.set_progress)
-        self._observer.schedule(event_handler, path=self._csv_path, recursive=True)
+        event_handler: MetricsCSVEventHandler = MetricsCSVEventHandler(
+            self._target_path, self.set_progress
+        )
+        self._observer.schedule(
+            event_handler, path=self._csv_path, recursive=True
+        )
         self._observer.start()
 
     def stop_tracker(self) -> None:
@@ -47,7 +60,11 @@ def _get_last_csv_version(self) -> int:
                 if child.is_dir():
                     version_str: str = child.name.split("_")[-1]
                     try:
-                        last_version = int(version_str) if int(version_str) > last_version else last_version
+                        last_version = (
+                            int(version_str)
+                            if int(version_str) > last_version
+                            else last_version
+                        )
                     except ValueError:
                         continue
         return last_version

src/allencell_ml_segmenter/training/view.py

@@ -24,14 +24,17 @@
     ImageSelectionWidget,
 )
 from allencell_ml_segmenter.training.training_model import TrainingModel
+
 # from hydra.core.global_hydra import GlobalHydra
 from aicsimageio import AICSImage
 from aicsimageio.readers import TiffReader
 
 from allencell_ml_segmenter.widgets.label_with_hint_widget import LabelWithHint
 from qtpy.QtGui import QIntValidator
 from allencell_ml_segmenter.training.training_model import PatchSize
-from allencell_ml_segmenter.training.metrics_csv_progress_tracker import MetricsCSVProgressTracker
+from allencell_ml_segmenter.training.metrics_csv_progress_tracker import (
+    MetricsCSVProgressTracker,
+)
 
 
 class TrainingView(View):
@@ -205,8 +208,11 @@ def train_btn_handler(self) -> None:
         """
         Starts training process
         """
-        progress_tracker: MetricsCSVProgressTracker = MetricsCSVProgressTracker(
-            self._experiments_model.get_metrics_csv_path(), progress_maximum=self._training_model.get_max_epoch()
+        progress_tracker: MetricsCSVProgressTracker = (
+            MetricsCSVProgressTracker(
+                self._experiments_model.get_metrics_csv_path(),
+                progress_maximum=self._training_model.get_max_epoch(),
+            )
         )
         self.startLongTaskWithProgressBar(progress_tracker)