Update type hints for Python >= 3.9

- Use standard collections, e.g. list[str] instead of typing.List[str].
- Import certain types from collections.abc, instead of deprecated aliases in
  typing.
- Omit .model.transport, handled in #225.

Author: khaeru

message_ix_models/model/build.py

@@ -1,5 +1,6 @@
 import logging
-from typing import Callable, Dict, List, Mapping, Optional, Union
+from collections.abc import Callable, Mapping
+from typing import Optional, Union
 
 import ixmp
 import pandas as pd
@@ -77,7 +78,7 @@ def apply_spec(  # noqa: C901
             pass
         maybe_check_out(scenario)
 
-    dump: Dict[str, pd.DataFrame] = {}  # Removed data
+    dump: dict[str, pd.DataFrame] = {}  # Removed data
 
     # Sort the list of sets by the number of dimensions; this places basic (non-indexed)
     # sets first. Elements for these sets must be added before elements for indexed
@@ -166,7 +167,7 @@ def apply_spec(  # noqa: C901
     )
 
 
-def ellipsize(elements: List) -> str:
+def ellipsize(elements: list) -> str:
     """Generate a short string representation of `elements`.
 
     If the list has more than 5 elements, only the first two and last two are shown,

message_ix_models/model/disutility.py

@@ -1,9 +1,9 @@
 import logging
 from collections import defaultdict
+from collections.abc import Mapping, MutableMapping, Sequence
 from copy import copy
 from functools import partial
 from itertools import product
-from typing import List, Mapping, MutableMapping, Sequence
 
 import message_ix
 import pandas as pd
@@ -166,10 +166,10 @@ def data_conversion(info, spec: Spec) -> MutableMapping[str, pd.DataFrame]:
     )
 
     # Use the spec to retrieve information
-    technology: List[Code] = spec.add.set["technology"]
+    technology: list[Code] = spec.add.set["technology"]
 
     # Data to return
-    data0: Mapping[str, List[pd.DataFrame]] = defaultdict(list)
+    data0: Mapping[str, list[pd.DataFrame]] = defaultdict(list)
 
     # Loop over conversion technologies
     for t in technology:

message_ix_models/model/emissions.py

@@ -1,6 +1,6 @@
 import logging
 import re
-from typing import Optional, Tuple
+from typing import Optional
 
 import pandas as pd
 from genno import Quantity
@@ -153,7 +153,7 @@ def add_tax_emission(
         scen.add_par(name, data)
 
 
-def split_species(unit_expr: str) -> Tuple[str, Optional[str]]:
+def split_species(unit_expr: str) -> tuple[str, Optional[str]]:
     """Split `unit_expr` to an expression without a unit mention, and maybe species."""
     if match := re.fullmatch("(.*)(CO2|C)(.*)", unit_expr):
         return f"{match.group(1)}{match.group(3)}", match.group(2)

message_ix_models/model/macro.py

@@ -5,10 +5,11 @@
 """
 
 import logging
+from collections.abc import Mapping
 from functools import lru_cache
 from itertools import product
 from pathlib import Path
-from typing import TYPE_CHECKING, List, Literal, Mapping, Optional, Union
+from typing import TYPE_CHECKING, Literal, Optional, Union
 
 import pandas as pd
 
@@ -29,7 +30,7 @@
 def generate(
     parameter: Literal["aeei", "config", "depr", "drate", "lotol"],
     context: "Context",
-    commodities: Union[List[str], List["Code"]] = COMMODITY,
+    commodities: Union[list[str], list["Code"]] = COMMODITY,
     value: Optional[float] = None,
 ) -> pd.DataFrame:
     """Generate uniform data for one :mod:`message_ix.macro` `parameter`.

message_ix_models/model/material/build.py

@@ -1,5 +1,6 @@
 import logging
-from typing import Any, Dict, Mapping
+from collections.abc import Mapping
+from typing import Any
 
 import message_ix
 import pandas as pd
@@ -206,7 +207,7 @@ def get_spec() -> Mapping[str, ScenarioInfo]:
 
 
 def make_spec(regions: str, materials: str or None = SPEC_LIST) -> Spec:
-    sets: Dict[str, Any] = dict()
+    sets: dict[str, Any] = dict()
     materials = ["common"] if not materials else materials
     # Overrides specific to regional versions
     tmp = dict()

message_ix_models/model/material/data_aluminum.py

@@ -1,5 +1,5 @@
 from collections import defaultdict
-from typing import Dict, Iterable, List
+from collections.abc import Iterable
 
 import message_ix
 import pandas as pd
@@ -69,7 +69,7 @@ def read_data_aluminum(
     return data_alu, data_alu_rel, data_aluminum_ts
 
 
-def gen_data_alu_ts(data: pd.DataFrame, nodes: list) -> Dict[str, pd.DataFrame]:
+def gen_data_alu_ts(data: pd.DataFrame, nodes: list) -> dict[str, pd.DataFrame]:
     """
     Generates time variable parameter data for aluminum sector
     Parameters
@@ -330,7 +330,7 @@ def gen_data_alu_const(
     glb_reg: str,
     years: Iterable,
     yv_ya: pd.DataFrame,
-    nodes: List[str],
+    nodes: list[str],
 ):
     results = defaultdict(list)
     for t in config["technology"]["add"]:
@@ -544,7 +544,7 @@ def gen_mock_demand_aluminum(scenario: message_ix.Scenario) -> pd.DataFrame:
     return demand2020_al
 
 
-def gen_data_alu_trade(scenario: message_ix.Scenario) -> Dict[str, pd.DataFrame]:
+def gen_data_alu_trade(scenario: message_ix.Scenario) -> dict[str, pd.DataFrame]:
     results = defaultdict(list)
 
     data_trade = pd.read_csv(

message_ix_models/model/material/data_methanol.py

@@ -1,5 +1,5 @@
 from ast import literal_eval
-from typing import TYPE_CHECKING, Dict, List
+from typing import TYPE_CHECKING
 
 import pandas as pd
 import yaml
@@ -30,7 +30,7 @@
 }
 
 
-def gen_data_methanol(scenario: "Scenario") -> Dict[str, pd.DataFrame]:
+def gen_data_methanol(scenario: "Scenario") -> dict[str, pd.DataFrame]:
     """
     Generates data for methanol industry model
 
@@ -94,8 +94,8 @@ def gen_data_methanol(scenario: "Scenario") -> Dict[str, pd.DataFrame]:
 def broadcast_nodes(
     df_bc_node: pd.DataFrame,
     df_final: pd.DataFrame,
-    node_cols: List[str],
-    node_cols_codes: Dict[str, pd.Series],
+    node_cols: list[str],
+    node_cols_codes: dict[str, pd.Series],
     i: int,
 ) -> pd.DataFrame:
     """
@@ -105,8 +105,8 @@ def broadcast_nodes(
     ----------
     df_bc_node: pd.DataFrame
     df_final: pd.DataFrame
-    node_cols: List[str]
-    node_cols_codes: Dict[str, pd.Series]
+    node_cols: list[str]
+    node_cols_codes: dict[str, pd.Series]
     i: int
     """
     if len(node_cols) == 1:
@@ -153,17 +153,17 @@ def broadcast_nodes(
 
 def broadcast_years(
     df_bc_node: pd.DataFrame,
-    yr_col_out: List[str],
-    yr_cols_codes: Dict[str, List[str]],
+    yr_col_out: list[str],
+    yr_cols_codes: dict[str, list[str]],
     col: str,
 ) -> pd.DataFrame:
     """
     Broadcast years that were stored in pivoted row
     Parameters
     ----------
     df_bc_node: pd.DataFrame
-    yr_col_out: List[str]
-    yr_cols_codes: ict[str, List[str]]
+    yr_col_out: list[str]
+    yr_cols_codes: ict[str, list[str]]
     col: str
     """
     if len(yr_col_out) == 1:

message_ix_models/model/material/data_petro.py

@@ -1,5 +1,4 @@
 from collections import defaultdict
-from typing import List, Set
 
 import message_ix
 import pandas as pd
@@ -166,7 +165,7 @@ def get_demand_t1_with_income_elasticity(
 
 
 def gen_data_petro_ts(
-    data_petro_ts: pd.DataFrame, results: dict[list], tec_ts: Set[str], nodes: List[str]
+    data_petro_ts: pd.DataFrame, results: dict[list], tec_ts: set[str], nodes: list[str]
 ) -> None:
     for t in tec_ts:
         common = dict(
@@ -239,7 +238,7 @@ def assign_input_outpt(
     rg: str,
     global_region: str,
     common: dict,
-    nodes: List[str],
+    nodes: list[str],
 ) -> pd.DataFrame:
     com = split[1]
     lev = split[2]
@@ -295,7 +294,7 @@ def assign_input_outpt(
     return df
 
 
-def broadcast_to_regions(df: pd.DataFrame, global_region: str, nodes: List[str]):
+def broadcast_to_regions(df: pd.DataFrame, global_region: str, nodes: list[str]):
     if "node_loc" in df.columns:
         if (
             len(set(df["node_loc"])) == 1

message_ix_models/model/material/data_steel.py

@@ -1,5 +1,5 @@
 from collections import defaultdict
-from typing import Dict, Iterable, List
+from collections.abc import Iterable
 
 import message_ix
 import pandas as pd
@@ -119,7 +119,7 @@ def gen_mock_demand_steel(scenario: message_ix.Scenario) -> pd.DataFrame:
 
 
 def gen_data_steel_ts(
-    data_steel_ts: pd.DataFrame, results: Dict[str, list], t: str, nodes: List[str]
+    data_steel_ts: pd.DataFrame, results: dict[str, list], t: str, nodes: list[str]
 ):
     common = dict(
         time="year",
@@ -213,11 +213,11 @@ def gen_data_steel_ts(
 
 def get_data_steel_const(
     data_steel: pd.DataFrame,
-    results: Dict[str, list],
+    results: dict[str, list],
     params: Iterable,
     t: str,
     yv_ya: pd.DataFrame,
-    nodes: List[str],
+    nodes: list[str],
     global_region: str,
 ):
     for par in params:

message_ix_models/model/material/data_util.py

@@ -1,6 +1,7 @@
 import os
+from collections.abc import Mapping
 from functools import lru_cache
-from typing import TYPE_CHECKING, Literal, Mapping
+from typing import TYPE_CHECKING, Literal
 
 import ixmp
 import message_ix

message_ix_models/model/structure.py

@@ -1,10 +1,10 @@
 import logging
 import re
 from collections import ChainMap
+from collections.abc import Mapping, MutableMapping
 from copy import copy
 from functools import lru_cache
 from itertools import product
-from typing import Dict, List, Mapping, MutableMapping, Tuple
 
 import click
 import pandas as pd
@@ -20,7 +20,7 @@
 
 
 @lru_cache()
-def codelists(kind: str) -> List[str]:
+def codelists(kind: str) -> list[str]:
     """Return a valid IDs for code lists of `kind`.
 
     Parameters
@@ -32,7 +32,7 @@ def codelists(kind: str) -> List[str]:
 
 
 @lru_cache()
-def get_codes(name: str) -> List[Code]:
+def get_codes(name: str) -> list[Code]:
     """Return codes for the dimension/set `name` in MESSAGE-GLOBIOM scenarios.
 
     The information is read from :file:`data/{name}.yaml`, e.g.
@@ -101,15 +101,15 @@ def get_codelist(name: str) -> Codelist:
 
 
 @lru_cache()
-def get_region_codes(codelist: str) -> List[Code]:
+def get_region_codes(codelist: str) -> list[Code]:
     """Return the codes that are children of "World" in the specified `codelist`."""
     nodes = get_codes(f"node/{codelist}")
     return nodes[nodes.index(Code(id="World"))].child
 
 
 def generate_product(
     data: Mapping, name: str, template: Code
-) -> Tuple[List[Code], Dict[str, xr.DataArray]]:
+) -> tuple[list[Code], dict[str, xr.DataArray]]:
     """Generates codes using a `template` by Cartesian product along ≥1 dimensions.
 
     :func:`generate_set_elements` is called for each of the `dims`, and these values

message_ix_models/model/water/build.py

@@ -1,6 +1,6 @@
 import logging
+from collections.abc import Mapping
 from functools import lru_cache, partial
-from typing import Mapping
 
 import pandas as pd
 from sdmx.model.v21 import Code

message_ix_models/model/water/data/demands.py

@@ -1,7 +1,8 @@
 """Prepare data for adding demands"""
 
 import os
-from typing import TYPE_CHECKING, Literal, Sequence, Union
+from collections.abc import Sequence
+from typing import TYPE_CHECKING, Literal, Union
 
 import numpy as np
 import pandas as pd

message_ix_models/model/water/utils.py

@@ -2,7 +2,7 @@
 from collections import defaultdict
 from functools import lru_cache
 from itertools import product
-from typing import Optional, Tuple
+from typing import Optional
 
 import numpy as np
 import pandas as pd
@@ -125,13 +125,13 @@ def func(row: pd.Series):
 
 
 def map_yv_ya_lt(
-    periods: Tuple[int, ...], lt: int, ya: Optional[int] = None
+    periods: tuple[int, ...], lt: int, ya: Optional[int] = None
 ) -> pd.DataFrame:
     """All meaningful combinations of (vintage year, active year) given `periods`.
 
     Parameters
     ----------
-    periods : Tuple[int, ...]
+    periods : tuple[int, ...]
         A sequence of years.
     lt : int, lifetime
     ya : int, active year

message_ix_models/model/workflow.py

@@ -1,7 +1,7 @@
 """Common steps for workflows."""
 
 from dataclasses import dataclass, field
-from typing import TYPE_CHECKING, Any, Dict, Optional
+from typing import TYPE_CHECKING, Any, Optional
 
 from message_ix import Scenario
 
@@ -21,7 +21,7 @@ class Config(ConfigHelper):
 
     #: Information on an optional, other scenario from which to copy demand data in
     #: :func:`solve` using :func:`transfer_demands`. Default: empty, do nothing.
-    demand_scenario: Dict = field(default_factory=dict)
+    demand_scenario: dict = field(default_factory=dict)
 
     #: :obj:`True` to call :func:`.reserve_margin.res_marg.main` in :func:`solve`.
     reserve_margin: bool = True
@@ -31,7 +31,7 @@ class Config(ConfigHelper):
     #: To replicate the behaviour of the `macro_params` argument to
     #: :meth:`.engage.ScenarioRunner.run`, which in turn sets the `convergence_issues`
     #: argument to :meth:`.engage.ScenarioRunner.solve`, set max_adjustment to 0.1.
-    solve: Dict[str, Any] = field(
+    solve: dict[str, Any] = field(
         default_factory=lambda: dict(model="MESSAGE-MACRO", max_adjustment=0.2)
     )