Merge branch 'feature/static-risk-traj' into feature/interpolated-trajectories

Author: spjuhel

climada/trajectories/test/test_static_risk_trajectory.py

@@ -221,18 +221,18 @@ def test_calc_npv_cash_flows_logic(mock_disc_rates):
     )
     start_date = datetime.date(2023, 1, 1)
 
-    # NPV Factor: (1 / (1 + rate)) ^ year_delta
+    # NPV Factor: Product[ (1 / (1 + rate_i))]
+    # For a constant rate or 0.01
     # 2023: (1/1.01)^0 = 1.0 -> 100
-    # 2024: (1/1.02)^1 = 0.98039... -> 196.078...
-    # 2025: (1/1.03)^2 = 0.94259... -> 282.778...
+    # 2024: (1/1.01)^1 = 0.99099... -> 198.019...
+    # 2025: (1/1.01)^2 = 0.98029... -> 294.088...
 
     result = RiskTrajectory._calc_npv_cash_flows(
         cash_flows, start_date, mock_disc_rates
     )
-
     assert result.iloc[0] == pytest.approx(100.0)
-    assert result.iloc[1] == pytest.approx(200 / 1.02)
-    assert result.iloc[2] == pytest.approx(300 / (1.03**2))
+    assert result.iloc[1] == pytest.approx(200 / (1.02))
+    assert result.iloc[2] == pytest.approx(300 * (1 / 1.02) * (1 / 1.03))
 
 
 def test_calc_npv_cash_flows_invalid_index(mock_disc_rates):

climada/trajectories/trajectory.py

@@ -217,56 +217,44 @@ def _npv_group(group, disc):
 
     @staticmethod
     def _calc_npv_cash_flows(
-        cash_flows: pd.DataFrame | pd.Series,
+        cash_flows: pd.Series,
         start_date: datetime.date,
         disc_rates: DiscRates | None = None,
-    ):
+    ) -> pd.Series:
         """Apply discount rate to cash flows.
 
         If it is defined, applies a discount rate `disc` to a given cash flow
-        `cash_flows` assuming present year corresponds to `start_date`.
+        `cash_flows` using `start_date` as the reference year.
 
         Parameters
         ----------
         cash_flows : pd.DataFrame
             The cash flow to apply the discount rate to.
         start_date : datetime.date
             The date representing the present.
-        end_date : datetime.date, optional
         disc : DiscRates, optional
-            The discount rate to apply.
+            The discount rates to apply.
 
         Returns
         -------
 
-        A dataframe (copy) of `cash_flows` where values are discounted according to `disc`.
+        A Series (copy) of `cash_flows` where values are discounted according to `disc`.
 
         """
 
-        if not disc_rates:
+        if disc_rates is None:
             return cash_flows
 
         if not isinstance(cash_flows.index, (pd.PeriodIndex, pd.DatetimeIndex)):
             raise ValueError(
                 "cash_flows must be a pandas Series with a PeriodIndex or DatetimeIndex"
             )
 
-        metric_df = cash_flows.to_frame(name="cash_flow")  # type: ignore
-        metric_df["year"] = metric_df.index.year
-
-        # Merge with the discount rates based on the year
-        tmp = pd.Series(index=disc_rates.years, data=disc_rates.rates, name="rate")
-        tmp = tmp.loc[tmp.index >= start_date.year]
-        tmp = 1 / tmp.shift(1, fill_value=0).add(1).cumprod()
-        tmp = tmp.to_frame()
-        tmp["year"] = tmp.index
-        metric_df = metric_df.merge(
-            tmp,
-            on="year",
-            how="left",
+        growth_factors = (
+            pd.Series(disc_rates.rates, index=disc_rates.years)
+            .loc[lambda x: x.index > start_date.year]
+            .add(1)
+            .cumprod()
         )
-
-        # Apply the discount factors to the cash flows
-
-        metric_df["npv_cash_flow"] = metric_df["cash_flow"] * metric_df["rate"]
-        return metric_df["npv_cash_flow"].values
+        discount_factors = 1 / cash_flows.index.year.map(growth_factors).fillna(1.0)
+        return cash_flows.multiply(discount_factors, axis=0)