Autoformat with black

panoptica/panoptica_statistics.py

@@ -86,7 +86,9 @@ def from_file(cls, file: str):
             rows = [row for row in rd]
 
         header = rows[0]
-        assert header[0] == "subject_name", "First column is not subject_names, something wrong with the file?"
+        assert (
+            header[0] == "subject_name"
+        ), "First column is not subject_names, something wrong with the file?"
 
         keys_in_order = list([tuple(c.split("-")) for c in header[1:]])
         metric_names = []
@@ -127,13 +129,19 @@ def from_file(cls, file: str):
         return Panoptica_Statistic(subj_names=subj_names, value_dict=value_dict)
 
     def _assertgroup(self, group):
-        assert group in self.__groupnames, f"group {group} not existent, only got groups {self.__groupnames}"
+        assert (
+            group in self.__groupnames
+        ), f"group {group} not existent, only got groups {self.__groupnames}"
 
     def _assertmetric(self, metric):
-        assert metric in self.__metricnames, f"metric {metric} not existent, only got metrics {self.__metricnames}"
+        assert (
+            metric in self.__metricnames
+        ), f"metric {metric} not existent, only got metrics {self.__metricnames}"
 
     def _assertsubject(self, subjectname):
-        assert subjectname in self.__subj_names, f"subject {subjectname} not in list of subjects, got {self.__subj_names}"
+        assert (
+            subjectname in self.__subj_names
+        ), f"subject {subjectname} not in list of subjects, got {self.__subj_names}"
 
     def get(self, group, metric, remove_nones: bool = False) -> list[float]:
         """Returns the list of values for given group and metric
@@ -166,7 +174,10 @@ def get_one_subject(self, subjectname: str):
         """
         self._assertsubject(subjectname)
         sidx = self.__subj_names.index(subjectname)
-        return {g: {m: self.get(g, m)[sidx] for m in self.__metricnames} for g in self.__groupnames}
+        return {
+            g: {m: self.get(g, m)[sidx] for m in self.__metricnames}
+            for g in self.__groupnames
+        }
 
     def get_across_groups(self, metric) -> list[float]:
         """Given metric, gives list of all values (even across groups!) Treat with care!
@@ -195,8 +206,13 @@ def get_summary_across_groups(self) -> dict[str, ValueSummary]:
             summary_dict[m] = ValueSummary(value_list)
         return summary_dict
 
-    def get_summary_dict(self, include_across_group: bool = True) -> dict[str, dict[str, ValueSummary]]:
-        summary_dict = {g: {m: self.get_summary(g, m) for m in self.__metricnames} for g in self.__groupnames}
+    def get_summary_dict(
+        self, include_across_group: bool = True
+    ) -> dict[str, dict[str, ValueSummary]]:
+        summary_dict = {
+            g: {m: self.get_summary(g, m) for m in self.__metricnames}
+            for g in self.__groupnames
+        }
         if include_across_group:
             summary_dict["across_groups"] = self.get_summary_across_groups()
         return summary_dict
@@ -241,11 +257,17 @@ def get_summary_figure(
             _type_: _description_
         """
         orientation = "h" if horizontal else "v"
-        data_plot = {g: np.asarray(self.get(g, metric, remove_nones=True)) for g in self.__groupnames}
+        data_plot = {
+            g: np.asarray(self.get(g, metric, remove_nones=True))
+            for g in self.__groupnames
+        }
         if manual_metric_range is not None:
             assert manual_metric_range[0] < manual_metric_range[1], manual_metric_range
             change = (manual_metric_range[1] - manual_metric_range[0]) / 100
-            manual_metric_range = (manual_metric_range[0] - change, manual_metric_range[1] + change)
+            manual_metric_range = (
+                manual_metric_range[0] - change,
+                manual_metric_range[1] + change,
+            )
         return plot_box(
             data=data_plot,
             orientation=orientation,
@@ -281,7 +303,9 @@ def make_curve_over_setups(
         alternate_groupnames = [alternate_groupnames]
     #
     for setupname, stat in statistics_dict.items():
-        assert metric in stat.metricnames, f"metric {metric} not in statistic obj {setupname}"
+        assert (
+            metric in stat.metricnames
+        ), f"metric {metric} not in statistic obj {setupname}"
 
     setupnames = list(statistics_dict.keys())
     convert_x_to_digit = True
@@ -306,7 +330,10 @@ def make_curve_over_setups(
     plt.grid("major")
     # Y values are average metric values in that group and metric
     for idx, g in enumerate(groups):
-        Y = [ValueSummary(stat.get(g, metric, remove_nones=True)).avg for stat in statistics_dict.values()]
+        Y = [
+            ValueSummary(stat.get(g, metric, remove_nones=True)).avg
+            for stat in statistics_dict.values()
+        ]
 
         if plot_lines:
             plt.plot(
@@ -353,10 +380,18 @@ def plot_box(
     if sort:
         df_by_spec_count = df_data.groupby(name_method).mean()
         df_by_spec_count = dict(df_by_spec_count[name_metric].items())
-        df_data["mean"] = df_data[name_method].apply(lambda x: df_by_spec_count[x] * (1 if orientation == "h" else -1))
+        df_data["mean"] = df_data[name_method].apply(
+            lambda x: df_by_spec_count[x] * (1 if orientation == "h" else -1)
+        )
         df_data = df_data.sort_values(by="mean")
     if orientation == "v":
-        fig = px.strip(df_data, x=name_method, y=name_metric, stripmode="overlay", orientation=orientation)
+        fig = px.strip(
+            df_data,
+            x=name_method,
+            y=name_metric,
+            stripmode="overlay",
+            orientation=orientation,
+        )
         fig.update_traces(marker={"size": 5, "color": "#555555"})
         for e in data.keys():
             fig.add_trace(
@@ -367,7 +402,13 @@ def plot_box(
                 )
             )
     else:
-        fig = px.strip(df_data, y=name_method, x=name_metric, stripmode="overlay", orientation=orientation)
+        fig = px.strip(
+            df_data,
+            y=name_method,
+            x=name_metric,
+            stripmode="overlay",
+            orientation=orientation,
+        )
         fig.update_traces(marker={"size": 5, "color": "#555555"})
         for e in data.keys():
             fig.add_trace(

unit_tests/test_panoptic_aggregator.py

@@ -96,4 +96,3 @@ def test_simple_evaluation_then_statistic(self):
         statistic_obj.print_summary()
 
         os.remove(str(output_test_dir))
-