Add more type hints to model/material

message_ix_models/model/material/data_ammonia_new.py

@@ -46,7 +46,7 @@ def gen_all_NH3_fert(
 
 
 def broadcast_years(
-    df_new: pd.DataFrame, max_lt: int, act_years, vtg_years
+    df_new: pd.DataFrame, max_lt: int, act_years: pd.Series, vtg_years: pd.Series
 ) -> pd.DataFrame:
     if "year_act" in df_new.columns:
         df_new = df_new.pipe(same_node).pipe(broadcast, year_act=act_years)

message_ix_models/model/material/data_cement.py

@@ -174,7 +174,6 @@ def gen_data_cement(
     context.datafile = "Global_steel_cement_MESSAGE.xlsx"
 
     # Techno-economic assumptions
-    # TEMP: now add cement sector as well
     data_cement = read_sector_data(scenario, "cement", "Global_cement_MESSAGE.xlsx")
     # Special treatment for time-dependent Parameters
     data_cement_ts = read_timeseries(scenario, "cement", "Global_cement_MESSAGE.xlsx")
@@ -183,15 +182,14 @@ def gen_data_cement(
     # List of data frames, to be concatenated together at end
     results = defaultdict(list)
 
-    # For each technology there are differnet input and output combinations
+    # For each technology there are different input and output combinations
     # Iterate over technologies
 
     yv_ya = s_info.yv_ya
     yv_ya = yv_ya.loc[yv_ya.year_vtg >= 1980]
     # Do not parametrize GLB region the same way
     nodes = nodes_ex_world(s_info.N)
 
-    # for t in s_info.set['technology']:
     for t in config["technology"]["add"]:
         t = t.id
         params = data_cement.loc[(data_cement["technology"] == t), "parameter"].unique()

message_ix_models/model/material/data_generic.py

@@ -1,7 +1,7 @@
 from collections import defaultdict
 
 import pandas as pd
-from message_ix import make_df
+from message_ix import Scenario, make_df
 
 import message_ix_models.util
 from message_ix_models import ScenarioInfo
@@ -40,7 +40,9 @@ def read_data_generic(scenario: Scenario) -> (pd.DataFrame, pd.DataFrame):
     return data_generic, data_generic_ts
 
 
-def gen_data_generic(scenario: Scenario, dry_run: bool = False) -> dict[str, pd.DataFrame]:
+def gen_data_generic(
+    scenario: Scenario, dry_run: bool = False
+) -> dict[str, pd.DataFrame]:
     # Load configuration
 
     config = read_config()["material"]["generic"]

message_ix_models/model/material/data_petro.py

@@ -1,5 +1,5 @@
 from collections import defaultdict
-from typing import List
+from typing import List, Set
 
 import message_ix
 import pandas as pd
@@ -166,7 +166,7 @@ def get_demand_t1_with_income_elasticity(
 
 
 def gen_data_petro_ts(
-    data_petro_ts: pd.DataFrame, results: dict[list], tec_ts, nodes
+    data_petro_ts: pd.DataFrame, results: dict[list], tec_ts: Set[str], nodes: List[str]
 ) -> None:
     for t in tec_ts:
         common = dict(
@@ -234,7 +234,7 @@ def assign_input_outpt(
     split: str,
     param_name: str,
     regions: pd.DataFrame,
-    val,
+    val: float | int,
     t: str,
     rg: str,
     global_region: str,
@@ -480,23 +480,7 @@ def gen_data_petro_chemicals(
     )
     results["demand"].append(demand_hvc)
 
-    # df_e = make_df(paramname, level='final_material', commodity="ethylene", \
-    # value=demand_e.value, unit='t',year=demand_e.year, time='year', \
-    # node=demand_e.node)#.pipe(broadcast, node=nodes)
-    # results["demand"].append(df_e)
-    #
-    # df_p = make_df(paramname, level='final_material', commodity="propylene", \
-    # value=demand_p.value, unit='t',year=demand_p.year, time='year', \
-    # node=demand_p.node)#.pipe(broadcast, node=nodes)
-    # results["demand"].append(df_p)
-    #
-    # df_BTX = make_df(paramname, level='final_material', commodity="BTX", \
-    # value=demand_BTX.value, unit='t',year=demand_BTX.year, time='year', \
-    # node=demand_BTX.node)#.pipe(broadcast, node=nodes)
-    # results["demand"].append(df_BTX)
-
     # Special treatment for time-varying params
-
     tec_ts = set(data_petro_ts.technology)  # set of tecs in timeseries sheet
 
     gen_data_petro_ts(data_petro_ts, results, tec_ts, nodes)

message_ix_models/model/material/data_util.py

@@ -2159,7 +2159,7 @@ def gen_te_projections(
 
 
 def get_ssp_soc_eco_data(
-    context: "Context", model: str, measure: str, tec
+    context: "Context", model: str, measure: str, tec: str
 ) -> pd.DataFrame:
     """
     Function to update scenario GDP and POP timeseries to SSP 3.0

message_ix_models/model/material/material_demand/material_demand_calc.py

@@ -62,7 +62,7 @@ def steel_function(x: pd.DataFrame | float, a: float, b: float, m: float):
     return a * np.exp(b / gdp_pcap) * (1 - m) ** del_t
 
 
-def cement_function(x: pd.DataFrame | float, a, b):
+def cement_function(x: pd.DataFrame | float, a: float, b: float):
     gdp_pcap = x[0]
     return a * np.exp(b / gdp_pcap)