[pre-commit.ci] pre-commit autoupdate

.pre-commit-config.yaml

@@ -42,7 +42,7 @@ repos:
           ['--no-error-on-unmatched-pattern', '--ignore-unknown']
 
   - repo: https://github.com/igorshubovych/markdownlint-cli
-    rev: v0.42.0
+    rev: v0.43.0
     hooks:
       - id: markdownlint
         args: ["--fix"]
@@ -65,7 +65,7 @@ repos:
         args: ["-x"]  # Check external files
 
   - repo: https://github.com/Mateusz-Grzelinski/actionlint-py
-    rev: v1.7.3.17
+    rev: v1.7.6.22
     hooks:
       - id: actionlint
 
@@ -78,7 +78,7 @@ repos:
           ignore-from-file: [.gitignore],}"]
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.7.0
+    rev: v0.9.1
     hooks:
       - id: ruff
         args: [--fix, --exit-non-zero-on-fix, --config=pyproject.toml]
@@ -94,7 +94,7 @@ repos:
           then /bin/mkdir .mypy_cache; fi; exit 0'
 
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: "v1.12.1"
+    rev: "v1.14.1"
     hooks:
       - id: mypy
         verbose: true

src/ITR/data/base_providers.py

@@ -2252,7 +2252,7 @@ def project_ei_targets(
                                 last_prod_value = production_proj.loc[last_ei_year]
                                 ei_projection_scopes[scope_name] = (
                                     ICompanyEIProjections(
-                                        ei_metric=f"{(last_em_value/last_prod_value).u:~P}",
+                                        ei_metric=f"{(last_em_value / last_prod_value).u:~P}",
                                         projections=self._get_bounded_projections(
                                             model_ei_projections
                                         ),

src/ITR/data/data_warehouse.py

@@ -187,7 +187,7 @@ def update_benchmarks(
             logger.info(
                 f"new_production_bm calculating trajectories for {len(self.company_data.get_company_data())}"
                 f"companies (times {len(EScope.get_scopes())} scopes times "
-                f"{cd_pc.TARGET_YEAR-cd_pc.BASE_YEAR} years)"
+                f"{cd_pc.TARGET_YEAR - cd_pc.BASE_YEAR} years)"
             )
             self.company_data._validate_projected_trajectories(
                 self.company_data.get_company_data(), self.benchmarks_projected_ei
@@ -215,7 +215,7 @@ def update_benchmarks(
             cd_pc = self.company_data.get_projection_controls()
             logger.info(
                 f"projecting targets for {len(self.company_data.get_company_data())} companies "
-                f"(times {len(EScope.get_scopes())} scopes times {cd_pc.TARGET_YEAR-cd_pc.BASE_YEAR} years)"
+                f"(times {len(EScope.get_scopes())} scopes times {cd_pc.TARGET_YEAR - cd_pc.BASE_YEAR} years)"
             )
             self.company_data._calculate_target_projections(
                 benchmark_projected_production, benchmarks_projected_ei
@@ -418,7 +418,7 @@ def update_trajectories(self):
         logger.info(
             f"re-calculating trajectories for {len(self.company_data._companies)} companies"
             f"\n    (times {len(EScope.get_scopes())} scopes times "
-            f"{self.company_data.projection_controls.TARGET_YEAR-self.company_data.projection_controls.BASE_YEAR} years)"
+            f"{self.company_data.projection_controls.TARGET_YEAR - self.company_data.projection_controls.BASE_YEAR} years)"
         )
         for company in self.company_data._companies:
             company.projected_intensities = None

src/ITR/data/osc_units.py

@@ -593,9 +593,9 @@ def Quantity_type(units: str) -> type:
 
     def validate(value, units, info):
         quantity = to_Quantity(value)
-        assert quantity.is_compatible_with(
-            units
-        ), f"Units of {value} incompatible with {units}"
+        assert quantity.is_compatible_with(units), (
+            f"Units of {value} incompatible with {units}"
+        )
         return quantity
 
     def __get_pydantic_core_schema__(source_type: Any) -> CoreSchema:

src/ITR/data/template.py

@@ -692,7 +692,7 @@ def convert_prefix_to_scalar(x):
                         )
                         if x.currency_tuple[0] != "USD"
                         else fx_ctx.redefine(
-                            f"{x.fx_quote_tuple[0]} = {x.currency_tuple[1]/(x.fx_rate * x.fx_quote_tuple[1])} {x.currency_tuple[0]}"
+                            f"{x.fx_quote_tuple[0]} = {x.currency_tuple[1] / (x.fx_rate * x.fx_quote_tuple[1])} {x.currency_tuple[0]}"
                         )
                     ),
                     axis=1,
@@ -1830,9 +1830,9 @@ def _company_df_to_model(
                     # FIXME: Is this the best place to finalize base_year_production, ghg_s1s2, and ghg_s3 data?
                     # Something tells me these parameters should be removed in favor of querying historical data directly
                     company_data[ColumnsConfig.BASE_YEAR_PRODUCTION] = (
-                        df_historic_data.loc[
-                            company_id, "Productions", "production"
-                        ][base_year]
+                        df_historic_data.loc[company_id, "Productions", "production"][
+                            base_year
+                        ]
                     )
                     try:
                         company_data[ColumnsConfig.GHG_SCOPE12] = df_historic_data.loc[

src/ITR/data/vault_providers.py

@@ -254,7 +254,7 @@ def read_quantified_sql(
             ]
             for col_tuple in extra_unit_columns_positions:
                 logger.error(
-                    f"Missing units column '{col_tuple[2]}' after original column '{sql_df.columns[col_tuple[1]]}' (should be column #{col_tuple[0]+col_tuple[1]+1} in new query)"  # noqa: E501
+                    f"Missing units column '{col_tuple[2]}' after original column '{sql_df.columns[col_tuple[1]]}' (should be column #{col_tuple[0] + col_tuple[1] + 1} in new query)"  # noqa: E501
                 )
             raise ValueError
     return requantify_df(sql_df).convert_dtypes()
@@ -1058,7 +1058,7 @@ def __init__(
            regexp_replace(regexp_replace(concat(ET.ei_{scope}_by_year_units, ' * ', P.production_by_year_units),
                           '{re_simplify_units_both}', ''), '{re_simplify_units_one}', '')) as cumulative_target_units,
        '{scope.upper()}' as scope
-from {emissions_from_tables.replace('SCOPE', scope)}
+from {emissions_from_tables.replace("SCOPE", scope)}
 """
                 for scope in map(str.lower, EScope.get_scopes())
             ]

src/ITR/utils.py

@@ -132,9 +132,9 @@ def get_data(
     s3_data_invalid = df_company_data[ColumnsConfig.GHG_SCOPE3].isna()
     if len(s3_data_invalid[s3_data_invalid].index) > 0:
         df_company_data.loc[s3_data_invalid, ColumnsConfig.GHG_SCOPE3] = (
-            df_company_data.loc[
-                s3_data_invalid, ColumnsConfig.GHG_SCOPE3
-            ].map(lambda x: Q_(np.nan, "Mt CO2e"))
+            df_company_data.loc[s3_data_invalid, ColumnsConfig.GHG_SCOPE3].map(
+                lambda x: Q_(np.nan, "Mt CO2e")
+            )
         )
     for col in [
         ColumnsConfig.GHG_SCOPE3,

test/test_vault_providers.py

@@ -111,9 +111,9 @@ def _get_base_ei(filename: str) -> BaseProviderIntensityBenchmark:
 
 
 @pytest.fixture(scope="session")
-def base_benchmarks() -> (
-    Tuple[BaseProviderProductionBenchmark, BaseProviderIntensityBenchmark]
-):
+def base_benchmarks() -> Tuple[
+    BaseProviderProductionBenchmark, BaseProviderIntensityBenchmark
+]:
     benchmark_dict: Dict[str, Future] = {}
     with concurrent.futures.ThreadPoolExecutor() as executor:
         future_to_benchmark = {