less confusing naming

Author: spjuhel

climada/trajectories/risk_trajectory.py

@@ -288,7 +288,7 @@ def _compute_period_metrics(
         df = self._generic_metrics(
             npv=npv, metric_name=metric_name, metric_meth=metric_meth, **kwargs
         )
-        return self._per_period_risk(df)
+        return self._date_to_period_agg(df)
 
     def _compute_metrics(
         self, metric_name: str, metric_meth: str, npv: bool = True, **kwargs
@@ -507,7 +507,7 @@ def _get_risk_periods(
         ]
 
     @classmethod
-    def _per_period_risk(
+    def _date_to_period_agg(
         cls,
         df: pd.DataFrame,
         time_unit: str = "year",
@@ -588,7 +588,7 @@ def per_period_risk_metrics(
     ) -> pd.DataFrame | pd.Series:
         """Returns a tidy dataframe of the risk metrics with the total for each different period."""
         df = self.per_date_risk_metrics(metrics=metrics, **kwargs)
-        return self._per_period_risk(df, **kwargs)
+        return self._date_to_period_agg(df, **kwargs)
 
     def _calc_waterfall_plot_data(
         self,

climada/trajectories/test/test_risk_trajectory.py

@@ -631,7 +631,7 @@ def test_per_period_risk_basic(self):
                 "risk": [100.0, 200.0, 300.0, 50.0],
             }
         )
-        result_df = RiskTrajectory._per_period_risk(df_input)
+        result_df = RiskTrajectory._date_to_period_agg(df_input)
 
         expected_df = pd.DataFrame(
             {
@@ -659,7 +659,7 @@ def test_per_period_risk_multiple_risk_cols(self):
                 "exposure contribution": [5.0, 8.0],
             }
         )
-        result_df = RiskTrajectory._per_period_risk(
+        result_df = RiskTrajectory._date_to_period_agg(
             df_input, colname=["base risk", "exposure contribution"]
         )
 
@@ -686,7 +686,9 @@ def test_per_period_risk_non_yearly_intervals(self):
             }
         )
         # Test with 'month' time_unit
-        result_df_month = RiskTrajectory._per_period_risk(df_input, time_unit="month")
+        result_df_month = RiskTrajectory._date_to_period_agg(
+            df_input, time_unit="month"
+        )
         expected_df_month = pd.DataFrame(
             {
                 "period": ["2023-01-01 to 2023-03-01"],
@@ -710,7 +712,7 @@ def test_per_period_risk_non_yearly_intervals(self):
                 "risk": [10.0, 20.0, 40.0],
             }
         )
-        result_df_gap = RiskTrajectory._per_period_risk(df_gap, time_unit="month")
+        result_df_gap = RiskTrajectory._date_to_period_agg(df_gap, time_unit="month")
         expected_df_gap = pd.DataFrame(
             {
                 "period": ["2023-01-01 to 2023-02-01", "2023-04-01 to 2023-04-01"],