Unit test compile cost assumptions - Part 1 (#170)

* code: unit test for get_excel_sheets * pre-commit * code: move sheet_names to _helpers.py * code: modify years_list * output: cost outputs * code: merge from master * code: new unit tests * code: revert changes to _helpers.py * code:fix quick issues * code: add logger * code: add docstrings * add pre-commit changes * code: modify sheet location method * code: add docstring * code: unit test clean_up_units * code: add docstring for get_dea_martime_data * code: add unit test for set_specify_assumptions * code: add docstring for set_specify_assumptions * code: remove assert False * code: unit test for set_round_trip_efficiency * include pre-commit * code: switch to numpydoc and introduce static types * code:include pre-commit * code: numpydoc and static types for order_data * code:replace print with logger statements * code: add logger * code: modify static type * docu: update numpydoc to the requested standard * code: add unit test for add_description * modify logger msg * code: fix logger messages * code: add unit test for convert_units * code: add numpydoc for add_gas_storage * code: new changes * code: add numpydoc * code: re-factor and numpydoc' * code: add new numpydoc * code: add new numpydoc - 2 * code: add new numpydoc - 3 * doc: update release_notes.rst * code: remove unit test for dea_maritime * code: add numpydoc for carbon_flow * code: pre-commit for carbon_flow * code: numpydoc for add_egs_data * code: add unit test for annuity * code: update numpydoc and naming * code: add numpydoc to compile_cost_assumptions_usa.py * code: replace list_of_years to years * code: numpydoc for final batch of functions * code: replace NoneType with empty DataFrame * code: pre-commit run all * code: new changes
PyPSA · Feb 19, 2025 · ee37d71 · ee37d71
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diff --git a/docs/release_notes.rst b/docs/release_notes.rst
@@ -16,9 +16,12 @@ Upcoming Release
 ..   The features listed below are not released yet, but will be part of the next release! 
 ..   To use the features already you have to use the ``master`` branch.
 
+* Include unit test for compile_cost_assumptions_usa.py (https://github.com/PyPSA/technology-data/pull/170)
+
 * US specific folder for NREL/ATB data (https://github.com/PyPSA/technology-data/pull/172)
 
 * Include unit test execution and compile_cost_assumptions_usa.py in ci.yaml (https://github.com/PyPSA/technology-data/pull/174)
+
 * Align `snakemake` version and the related `mock_snakemake` to PyPSA-Eur (https://github.com/PyPSA/technology-data/pull/177)
 
 `v0.11.0 <https://github.com/PyPSA/technology-data/releases/tag/v0.11.0>`__ (24th January 2025)

diff --git a/scripts/_helpers.py b/scripts/_helpers.py
@@ -187,39 +187,66 @@ def make_accessable(*ios):
     return snakemake
 
 
-def adjust_for_inflation(inflation_rate, costs, techs, eur_year, col):
+def adjust_for_inflation(
+    inflation_rate: pd.DataFrame,
+    costs: pd.DataFrame,
+    techs: pd.Series,
+    eur_year: int,
+    col_name: str,
+    usa_costs_flag: bool = False,
+) -> pd.DataFrame:
     """
-    Adjust the investment costs for the specified techs for inflation.
+    The function adjust the investment costs for the specified techs for inflation.
 
-    techs: str or list
-        One or more techs in costs index for which the inflation adjustment is done.
-    eur_year: int
-        Reference year for which the costs are provided and based on which the inflation adjustment is done.
-    costs: Dataframe containing the costs data with multiindex on technology and one index key 'investment'.
+    Parameters
+    ----------
+    inflation_rate : pd.DataFrame
+        inflation rates for several years
+    costs : pd.DataFrame
+        existing cost dataframe
+    techs : pd.Series
+        technologies
+    eur_year : int,
+        reference year for which the costs are provided and based on which the inflation adjustment is done
+    col_name : str
+        column name to which to apply the inflation rate adjustment
+    usa_costs_flag: bool
+        flag for US specific costs
+
+    Returns
+    -------
+    Dataframe
+        inflation updated cost dataframe
     """
 
-    def get_factor(inflation_rate, ref_year, eur_year):
+    def get_factor(inflation_rate_df, ref_year, eur_year_val):
         if (pd.isna(ref_year)) or (ref_year < 1900):
             return np.nan
-        if ref_year == eur_year:
+        if ref_year == eur_year_val:
             return 1
-        mean = inflation_rate.mean()
-        if ref_year < eur_year:
-            new_index = np.arange(ref_year + 1, eur_year + 1)
-            df = 1 + inflation_rate.reindex(new_index).fillna(mean)
-            return df.cumprod().loc[eur_year]
+        mean = inflation_rate_df.mean()
+        if ref_year < eur_year_val:
+            new_index = np.arange(ref_year + 1, eur_year_val + 1)
+            df = 1 + inflation_rate_df.reindex(new_index).fillna(mean)
+            return df.cumprod().loc[eur_year_val]
         else:
-            new_index = np.arange(eur_year + 1, ref_year + 1)
-            df = 1 + inflation_rate.reindex(new_index).fillna(mean)
+            new_index = np.arange(eur_year_val + 1, ref_year + 1)
+            df = 1 + inflation_rate_df.reindex(new_index).fillna(mean)
             return 1 / df.cumprod().loc[ref_year]
 
     inflation = costs.currency_year.apply(
         lambda x: get_factor(inflation_rate, x, eur_year)
     )
 
     paras = ["investment", "VOM", "fuel"]
-    filter_i = costs.technology.isin(techs) & costs.parameter.isin(paras)
-    costs.loc[filter_i, col] = costs.loc[filter_i, col].mul(
+
+    if usa_costs_flag:
+        filter_i = costs.technology.isin(techs) & costs.parameter.isin(paras)
+    else:
+        filter_i = costs.index.get_level_values(0).isin(
+            techs
+        ) & costs.index.get_level_values(1).isin(paras)
+    costs.loc[filter_i, col_name] = costs.loc[filter_i, col_name].mul(
         inflation.loc[filter_i], axis=0
     )