add belief horizon field

Signed-off-by: Victor Garcia Reolid <victor@seita.nl>

flexmeasures/data/models/reporting/pandas_reporter.py

@@ -87,6 +87,7 @@ def _compute_report(self, **kwargs) -> list[dict[str, Any]]:
 
         resolution: timedelta | None = kwargs.get("resolution", None)
         belief_time: datetime | None = kwargs.get("belief_time", None)
+        belief_horizon: timedelta | None = kwargs.get("belief_horizon", None)
         output: list[dict[str, Any]] = kwargs.get("output")
 
         # by default, use the minimum resolution among the output sensors
@@ -117,10 +118,14 @@ def _compute_report(self, **kwargs) -> list[dict[str, Any]]:
                 output_data = output_data.rename(columns={column: "event_value"})[
                     ["event_value"]
                 ]
-                output_data = self._clean_belief_dataframe(output_data, belief_time)
+                output_data = self._clean_belief_dataframe(
+                    output_data, belief_time, belief_horizon
+                )
 
             elif isinstance(output_data, tb.BeliefsSeries):
-                output_data = self._clean_belief_series(output_data, belief_time)
+                output_data = self._clean_belief_series(
+                    output_data, belief_time, belief_horizon
+                )
 
             result["data"] = output_data
 
@@ -129,11 +134,16 @@ def _compute_report(self, **kwargs) -> list[dict[str, Any]]:
         return results
 
     def _clean_belief_series(
-        self, belief_series: tb.BeliefsSeries, belief_time: datetime
+        self,
+        belief_series: tb.BeliefsSeries,
+        belief_time: datetime | None = None,
+        belief_horizon: timedelta | None = None,
     ) -> tb.BeliefsDataFrame:
         """Create a BeliefDataFrame from a BeliefsSeries creating the necessary indexes."""
 
         belief_series = belief_series.to_frame("event_value")
+        if belief_horizon is not None:
+            belief_time = belief_series["event_start"] + belief_horizon
         belief_series["belief_time"] = belief_time
         belief_series["cumulative_probability"] = 0.5
         belief_series["source"] = self.data_source
@@ -144,13 +154,21 @@ def _clean_belief_series(
         return belief_series
 
     def _clean_belief_dataframe(
-        self, bdf: tb.BeliefsDataFrame, belief_time: datetime
+        self,
+        bdf: tb.BeliefsDataFrame,
+        belief_time: datetime | None = None,
+        belief_horizon: timedelta | None = None,
     ) -> tb.BeliefsDataFrame:
         """Add missing indexes to build a proper BeliefDataFrame."""
 
         # filing the missing indexes with default values:
         if "belief_time" not in bdf.index.names:
-            bdf["belief_time"] = [belief_time] * len(bdf)
+            if belief_horizon is not None:
+                belief_time = bdf["event_start"] + belief_horizon
+            else:
+                belief_time = [belief_time] * len(bdf)
+            bdf["belief_time"] = belief_time
+
             bdf = bdf.set_index("belief_time", append=True)
 
         if "cumulative_probability" not in bdf.index.names:
@@ -249,7 +267,6 @@ def _apply_transformations(self):
             kwargs = self._process_pandas_kwargs(
                 transformation.get("kwargs", {}), method
             )
-
             self.data[df_output] = getattr(self.data[df_input], method)(*args, **kwargs)
 
             previous_df = df_output