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Signed-off-by: Nicolas Höning <nicolas@seita.nl>
SeitaBV · Mar 1, 2024 · f0a2b2a · f0a2b2a
1 parent b89a696
commit f0a2b2a
Showing 1 changed file with 8 additions and 15 deletions.
diff --git a/timely_beliefs/beliefs/classes.py b/timely_beliefs/beliefs/classes.py
@@ -191,7 +191,7 @@ def __table_args__(cls):
                 "source_id",
                 postgresql_include=[
                     "belief_horizon",  # we use min() on this
-                ]
+                ],
             ),
         )
 
@@ -1162,16 +1162,12 @@ def _for_each_belief(
         index_names.extend(
             ["event_start"]
             if "event_start" in df.index.names
-            else ["event_end"]
-            if "event_end" in df.index.names
-            else []
+            else ["event_end"] if "event_end" in df.index.names else []
         )
         index_names.extend(
             ["belief_time"]
             if "belief_time" in df.index.names
-            else ["belief_horizon"]
-            if "belief_horizon" in df.index.names
-            else []
+            else ["belief_horizon"] if "belief_horizon" in df.index.names else []
         )
         if collective_beliefs is False:
             index_names.append("source")
@@ -1490,9 +1486,9 @@ def resample_events(
                 column_functions = {
                     "event_value": "mean",
                     "source": "first",  # keep the only source
-                    belief_timing_col: "max"
-                    if belief_timing_col == "belief_time"
-                    else "min",  # keep only most recent belief
+                    belief_timing_col: (
+                        "max" if belief_timing_col == "belief_time" else "min"
+                    ),  # keep only most recent belief
                     "cumulative_probability": "mean",  # we just have one point on each CDF
                 }
                 df = downsample_beliefs_data_frame(
@@ -1549,8 +1545,7 @@ def form_beliefs(
         belief_time: datetime,
         source: BeliefSource,
         event_start: datetime = None,
-        event_time_window: tuple[datetime, datetime]
-        | None = (
+        event_time_window: tuple[datetime, datetime] | None = (
             None,
             None,
         ),
@@ -2111,9 +2106,7 @@ def assign_sensor_and_event_resolution(df, sensor, event_resolution):
     df.event_resolution = (
         event_resolution
         if event_resolution
-        else sensor.event_resolution
-        if sensor
-        else None
+        else sensor.event_resolution if sensor else None
     )