More metrics

CHANGELOG.rst

@@ -4,7 +4,8 @@ Changelog
 master
 ------
 
-- (`#111 <https://github.com/GranthamImperial/silicone/pull/111>`_) Minor improvements to error messages and documentation. 
+- (`#112 <https://github.com/GranthamImperial/silicone/pull/112>`_) Enabled more general unit conversion, bug fix and improvement for infill_composite_values.
+- (`#111 <https://github.com/GranthamImperial/silicone/pull/111>`_) Minor improvements to error messages and documentation.
 - (`#110 <https://github.com/GranthamImperial/silicone/pull/110>`_) Gave an option to time_dep_ratio and decompose_collection to ignore model/scenario combinations missing values at some required times.
 - (`#108 <https://github.com/GranthamImperial/silicone/pull/108>`_) Added a multiple infiller to split up an aggregate with a remainder. Disabled test for downloading database.
 - (`#103 <https://github.com/GranthamImperial/silicone/pull/103>`_) Update github address to GranthamImperial.

src/silicone/multiple_infillers/decompose_collection_with_time_dep_ratio.py

@@ -93,7 +93,7 @@ def infill_components(
         aggregate,
         components,
         to_infill_df,
-        use_ar4_data=False,
+        metric_name="AR5GWP100",
         only_consistent_cases=True,
     ):
         """
@@ -115,9 +115,9 @@ def infill_components(
             The dataframe that already contains the ``aggregate`` variable, but needs
             the ``components`` to be infilled.
 
-        use_ar4_data : bool
-            If true, we convert all values to Mt CO2 equivalent using the IPCC AR4
-            GWP100 data, otherwise (by default) we use the GWP100 data from AR5.
+        metric_name : str
+            The name of the conversion metric to use. This will usually be
+            AR<4/5/6>GWP100.
 
         only_consistent_cases : bool
             Do we want to only use model/scenario combinations where all aggregate and
@@ -184,7 +184,7 @@ def infill_components(
                 )
         if len(self._set_of_units_without_equiv(self._filtered_db)) > 1:
             db_to_generate = convert_units_to_MtCO2_equiv(
-                self._filtered_db, use_ar4_data=use_ar4_data
+                self._filtered_db, metric_name=metric_name
             )
         else:
             db_to_generate = self._filtered_db

src/silicone/multiple_infillers/infill_composite_values.py

@@ -83,8 +83,9 @@ def infill_composite_values(df, composite_dic=None):
             composite_df = _construct_consistent_values(
                 composite, composite_variables, df
             )
+        df.append(composite_df, inplace=True)
         try:
-            to_return.append(composite_df)
+            to_return.append(composite_df, inplace=True)
         except NameError:
             to_return = composite_df
 

src/silicone/multiple_infillers/split_collection_with_remainder_emissions.py

@@ -101,7 +101,7 @@ def infill_components(
         remainder,
         to_infill_df,
         cruncher_class=QuantileRollingWindows,
-        use_ar4_data=False,
+        metric_name="AR5GWP100",
         **kwargs
     ):
         """
@@ -131,10 +131,9 @@ def infill_components(
             The cruncher used to perform the infilling. By default this will be
             QuantileRollingWindows.
 
-        use_ar4_data : bool
-            Only used if the variables have different units. If true, we convert all
-            values to Mt CO2 equivalent using the IPCC AR4
-            GWP100 data, otherwise (by default) we use the GWP100 data from AR5.
+        metric_name : str
+            The name of the conversion metric to use. This will usually be
+            AR<4/5/6>GWP100.
 
         **kwargs :
             Handed to the cruncher.
@@ -192,7 +191,7 @@ def infill_components(
         calculate_remainder_df = df_to_append.append(to_infill_df)
         if _remove_equivs(aggregate_unit) == "Mt CO2/yr":
             calculate_remainder_df = convert_units_to_MtCO2_equiv(
-                calculate_remainder_df, use_ar4_data
+                calculate_remainder_df, metric_name
             )
 
         df_to_append.append(

src/silicone/utils.py

@@ -266,7 +266,7 @@ def _get_unit_of_variable(df, variable, multiple_units="raise"):
 
 
 def return_cases_which_consistently_split(
-    df, aggregate, components, how_close=None, use_ar4_data=False
+    df, aggregate, components, how_close=None, metric_name="AR5GWP100"
 ):
     """
     Returns model-scenario tuples which correctly split up the to_split into the various
@@ -290,9 +290,8 @@ def return_cases_which_consistently_split(
         tolerance of 1% ('rtol': 1e-2). The syntax for this can be found in the numpy
         documentation.
 
-    use_ar4_data : bool
-        Determines whether the unit conversion takes place using GWP100 values from
-        the UNFCCC AR5 (if false, default) or AR4 (if true).
+    metric_name : str
+        The name of the conversion metric to use. This will usually be AR<4/5/6>GWP100.
 
     Returns
     -------
@@ -303,7 +302,7 @@ def return_cases_which_consistently_split(
         how_close = {"equal_nan": True, "rtol": 1e-02}
     valid_model_scenario = []
     df = convert_units_to_MtCO2_equiv(
-        df.filter(variable=[aggregate] + components), use_ar4_data
+        df.filter(variable=[aggregate] + components), metric_name
     )
     combinations = df.data[["model", "scenario", "region"]].drop_duplicates()
     for ind in range(len(combinations)):
@@ -331,7 +330,7 @@ def return_cases_which_consistently_split(
     return valid_model_scenario
 
 
-def convert_units_to_MtCO2_equiv(df, use_ar4_data=False):
+def convert_units_to_MtCO2_equiv(df, metric_name="AR5GWP100"):
     """
     Converts the units of gases reported in kt into Mt CO2 equivalent per year
 
@@ -342,8 +341,8 @@ def convert_units_to_MtCO2_equiv(df, use_ar4_data=False):
     df : :obj:`pyam.IamDataFrame`
         The input dataframe whose units need to be converted.
 
-    use_ar4_data : bool
-        If true, use the AR4 GWP100 conversion figures, else use the AR5.
+    metric_name : str
+        The name of the conversion metric to use. This will usually be AR<4/5/6>GWP100.
 
     Return
     ------
@@ -356,8 +355,6 @@ def convert_units_to_MtCO2_equiv(df, use_ar4_data=False):
     if df["unit"].isin([convert_to_str, convert_to_str_clean]).all():
         return df
 
-    context = "AR4GWP100" if use_ar4_data else "AR5GWP100"
-
     to_convert_df = df.copy()
     to_convert_var = to_convert_df.filter(
         unit=[convert_to_str, convert_to_str_clean], keep=False
@@ -370,7 +367,7 @@ def convert_units_to_MtCO2_equiv(df, use_ar4_data=False):
 
     conversion_factors = {}
     not_found = []
-    with _ur.context(context):
+    with _ur.context(metric_name):
         for unit in to_convert_units:
             if unit in conversion_factors:
                 continue

tests/integration/multiple_infillers/test_infill_composite_values.py

@@ -107,21 +107,31 @@ def test_infill_composite_values_works(self, larger_df, caplog):
         )
 
     def test_infill_with_factors(self, larger_df):
-        # Ensure that the multiplier works correctly
+        # Ensure that the multiplier works correctly. Also ensure that calculated values
+        # can be used in subsequent calculations.
         larger_df_copy = larger_df.copy()
         half_industry_df = infill_composite_values(
             larger_df_copy,
             composite_dic={
                 "Emissions|CO2": {
                     "Emissions|CO2|AFOLU": 1,
                     "Emissions|CO2|Industry": 0.5,
-                }
+                },
+                "Emissions|CO2|Energy": {
+                    "Emissions|CO2": 1,
+                    "Emissions|CO2|AFOLU": -1,
+                    "Emissions|CO2|Industry": -0.5,
+                },
             },
         )
         assert np.allclose(
             half_industry_df.filter(variable="Emissions|CO2")["value"].values,
             [2, 2, 2, 1, 1, 1, 2, 2, 2],
         )
+        assert np.allclose(
+            half_industry_df.filter(variable="Emissions|CO2|Energy")["value"].values,
+            [0] * 9,
+        )
 
     def test_infill_composite_values_subtraction(self, larger_df, caplog):
         # Ensure that the code performs correctly when we subtract emissions too

tests/unit/test_utils.py

@@ -403,7 +403,7 @@ def test_convert_units_to_mtco2_equiv_fails_with_bad_units(check_aggregate_df):
 
 
 @pytest.mark.parametrize(
-    "ARoption,expected", [(False, [28, 6.63]), (True, [25, 7.390])]
+    "ARoption,expected", [("AR5GWP100", [28, 6.63]), ("AR4GWP100", [25, 7.390])]
 )
 def test_convert_units_to_MtCO2_equiv_works(check_aggregate_df, ARoption, expected):
     # ARoption turns the use of AR4 on, rather than AR5 (the default)