Autoformat with black

examples/example_spine_statistics.py

@@ -1,7 +1,13 @@
 from auxiliary.nifti.io import read_nifti
 from auxiliary.turbopath import turbopath
 
-from panoptica import Panoptica_Evaluator, Panoptica_Aggregator, InputType, NaiveThresholdMatching, Metric
+from panoptica import (
+    Panoptica_Evaluator,
+    Panoptica_Aggregator,
+    InputType,
+    NaiveThresholdMatching,
+    Metric,
+)
 from panoptica.utils import SegmentationClassGroups, LabelGroup
 from panoptica.panoptica_statistics import make_curve_over_setups
 from pathlib import Path

panoptica/_functionals.py

@@ -150,4 +150,10 @@ def _get_paired_crop(
 
 
 def _round_to_n(value: float | int, n_significant_digits: int = 2):
-    return value if value == 0 else round(value, -int(math.floor(math.log10(abs(value)))) + (n_significant_digits - 1))
+    return (
+        value
+        if value == 0
+        else round(
+            value, -int(math.floor(math.log10(abs(value)))) + (n_significant_digits - 1)
+        )
+    )

panoptica/instance_evaluator.py

@@ -5,6 +5,7 @@
 from panoptica.utils.processing_pair import MatchedInstancePair, EvaluateInstancePair
 from panoptica._functionals import _get_paired_crop
 
+
 def evaluate_matched_instance(
     matched_instance_pair: MatchedInstancePair,
     eval_metrics: list[Metric] = [Metric.DSC, Metric.IOU, Metric.ASSD],
@@ -38,7 +39,10 @@ def evaluate_matched_instance(
     )
     ref_matched_labels = matched_instance_pair.matched_instances
 
-    instance_pairs = [(reference_arr, prediction_arr, ref_idx, eval_metrics) for ref_idx in ref_matched_labels]
+    instance_pairs = [
+        (reference_arr, prediction_arr, ref_idx, eval_metrics)
+        for ref_idx in ref_matched_labels
+    ]
 
     # metric_dicts: list[dict[Metric, float]] = [_evaluate_instance(*i) for i in instance_pairs]
     with Pool() as pool:

panoptica/panoptica_aggregator.py

@@ -14,6 +14,7 @@
 
 COMPUTATION_TIME_KEY = "computation_time"
 
+
 #
 class Panoptica_Aggregator:
     # internal_list_lock = Lock()
@@ -84,7 +85,9 @@ def __init__(
                 continue_file = True
             else:
                 # TODO should also hash panoptica_evaluator just to make sure! and then save into header of file
-                assert header_hash == hash("+".join(header_list)), "Hash of header not the same! You are using a different setup!"
+                assert header_hash == hash(
+                    "+".join(header_list)
+                ), "Hash of header not the same! You are using a different setup!"
 
         if out_buffer_file.exists():
             os.remove(out_buffer_file)

panoptica/panoptica_evaluator.py

@@ -19,7 +19,12 @@
 )
 import numpy as np
 from panoptica.utils.config import SupportsConfig
-from panoptica.utils.segmentation_class import SegmentationClassGroups, LabelGroup, _NoSegmentationClassGroups
+from panoptica.utils.segmentation_class import (
+    SegmentationClassGroups,
+    LabelGroup,
+    _NoSegmentationClassGroups,
+)
+
 
 class Panoptica_Evaluator(SupportsConfig):
 
@@ -132,7 +137,11 @@ def evaluate(
                 label_group,
                 processing_pair,
                 result_all,
-                save_group_times=self.__save_group_times if save_group_times is None else save_group_times,
+                save_group_times=(
+                    self.__save_group_times
+                    if save_group_times is None
+                    else save_group_times
+                ),
                 log_times=log_times,
                 verbose=verbose,
             )[1:]

panoptica/panoptica_result.py

@@ -438,10 +438,15 @@ def __getattribute__(self, __name: str) -> Any:
                 raise e
         if __name == "_evaluation_metrics":
             return attr
-        if object.__getattribute__(self, "_evaluation_metrics") is not None and __name in self._evaluation_metrics.keys():
+        if (
+            object.__getattribute__(self, "_evaluation_metrics") is not None
+            and __name in self._evaluation_metrics.keys()
+        ):
             if attr is None:
                 if self._evaluation_metrics[__name]._error:
-                    raise MetricCouldNotBeComputedException(f"Requested metric {__name} that could not be computed")
+                    raise MetricCouldNotBeComputedException(
+                        f"Requested metric {__name} that could not be computed"
+                    )
                 elif not self._evaluation_metrics[__name]._was_calculated:
                     value = self._calc_metric(__name)
                     setattr(self, __name, value)

panoptica/utils/segmentation_class.py

@@ -5,6 +5,7 @@
 
 NO_GROUP_KEY = "ungrouped"
 
+
 class SegmentationClassGroups(SupportsConfig):
     #
     def __init__(
@@ -103,7 +104,9 @@ class _NoSegmentationClassGroups(SegmentationClassGroups):
     def __init__(self) -> None:
         self.__group_dictionary = {NO_GROUP_KEY: _LabelGroupAny()}
 
-    def has_defined_labels_for(self, arr: np.ndarray | list[int], raise_error: bool = False):
+    def has_defined_labels_for(
+        self, arr: np.ndarray | list[int], raise_error: bool = False
+    ):
         return True
 
     def __str__(self) -> str:
@@ -124,7 +127,9 @@ def keys(self) -> list[str]:
 
     @property
     def labels(self):
-        raise Exception("_NoSegmentationClassGroups has no explicit definition of labels")
+        raise Exception(
+            "_NoSegmentationClassGroups has no explicit definition of labels"
+        )
 
     @classmethod
     def _yaml_repr(cls, node):