Autoformat with black

panoptica/panoptica_result.py

@@ -243,18 +243,24 @@ def __init__(
                     num_pred_instances=self.num_pred_instances,
                     num_ref_instances=self.num_ref_instances,
                 )
-                self._list_metrics[m] = Evaluation_List_Metric(m, empty_list_std, list_metrics[m], is_edge_case, edge_case_result)
+                self._list_metrics[m] = Evaluation_List_Metric(
+                    m, empty_list_std, list_metrics[m], is_edge_case, edge_case_result
+                )
             # even if not available, set the global vars
             default_value = None
             was_calculated = False
             if m in self._global_metrics:
-                default_value = self._calc_global_bin_metric(m, prediction_arr, reference_arr)
+                default_value = self._calc_global_bin_metric(
+                    m, prediction_arr, reference_arr
+                )
                 was_calculated = True
 
             self._add_metric(
                 f"global_bin_{m.name.lower()}",
                 MetricType.GLOBAL,
-                lambda x: MetricCouldNotBeComputedException(f"Global Metric {m} not set"),
+                lambda x: MetricCouldNotBeComputedException(
+                    f"Global Metric {m} not set"
+                ),
                 long_name="Global Binary " + m.value.long_name,
                 default_value=default_value,
                 was_calculated=was_calculated,
@@ -264,7 +270,9 @@ def _calc_global_bin_metric(self, metric: Metric, prediction_arr, reference_arr)
         if metric not in self._global_metrics:
             raise MetricCouldNotBeComputedException(f"Global Metric {metric} not set")
         if self.tp == 0:
-            is_edgecase, result = self._edge_case_handler.handle_zero_tp(metric, self.tp, res.num_pred_instances, res.num_ref_instances)
+            is_edgecase, result = self._edge_case_handler.handle_zero_tp(
+                metric, self.tp, res.num_pred_instances, res.num_ref_instances
+            )
             if is_edgecase:
                 return result
         pred_binary = prediction_arr
@@ -353,13 +361,19 @@ def __str__(self) -> str:
         return text
 
     def to_dict(self) -> dict:
-        return {k: getattr(self, v.id) for k, v in self._evaluation_metrics.items() if (v._error == False and v._was_calculated)}
+        return {
+            k: getattr(self, v.id)
+            for k, v in self._evaluation_metrics.items()
+            if (v._error == False and v._was_calculated)
+        }
 
     def get_list_metric(self, metric: Metric, mode: MetricMode):
         if metric in self._list_metrics:
             return self._list_metrics[metric][mode]
         else:
-            raise MetricCouldNotBeComputedException(f"{metric} could not be found, have you set it in eval_metrics during evaluation?")
+            raise MetricCouldNotBeComputedException(
+                f"{metric} could not be found, have you set it in eval_metrics during evaluation?"
+            )
 
     def _calc_metric(self, metric_name: str, supress_error: bool = False):
         if metric_name in self._evaluation_metrics:
@@ -375,7 +389,9 @@ def _calc_metric(self, metric_name: str, supress_error: bool = False):
             self._evaluation_metrics[metric_name]._was_calculated = True
             return value
         else:
-            raise MetricCouldNotBeComputedException(f"could not find metric with name {metric_name}")
+            raise MetricCouldNotBeComputedException(
+                f"could not find metric with name {metric_name}"
+            )
 
     def __getattribute__(self, __name: str) -> Any:
         attr = None
@@ -390,7 +406,9 @@ def __getattribute__(self, __name: str) -> Any:
                 raise e
         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)
@@ -516,7 +534,9 @@ def function_template(res: PanopticaResult):
         if metric not in res._global_metrics:
             raise MetricCouldNotBeComputedException(f"Global Metric {metric} not set")
         if res.tp == 0:
-            is_edgecase, result = res._edge_case_handler.handle_zero_tp(metric, res.tp, res.num_pred_instances, res.num_ref_instances)
+            is_edgecase, result = res._edge_case_handler.handle_zero_tp(
+                metric, res.tp, res.num_pred_instances, res.num_ref_instances
+            )
             if is_edgecase:
                 return result
         pred_binary = res._prediction_arr.copy()