Switch from black to ruff format

.github/bump_version.py

@@ -19,9 +19,7 @@ def get_current_version(pyproject_path: Path) -> str:
 
 def infer_bump(changelog_dir: Path) -> str:
     fragments = [
-        f
-        for f in changelog_dir.iterdir()
-        if f.is_file() and f.name != ".gitkeep"
+        f for f in changelog_dir.iterdir() if f.is_file() and f.name != ".gitkeep"
     ]
     if not fragments:
         print("No changelog fragments found", file=sys.stderr)

.github/workflows/code_changes.yaml

@@ -21,6 +21,9 @@ jobs:
         - name: Install ruff
           run: pip install ruff
 
+        - name: Run ruff format check
+          run: ruff format --check .
+
         - name: Run ruff check
           run: ruff check .
   Test:

.github/workflows/pr_code_changes.yaml

@@ -18,6 +18,9 @@ jobs:
         - name: Install ruff
           run: pip install ruff
 
+        - name: Run ruff format check
+          run: ruff format --check .
+
         - name: Run ruff check
           run: ruff check .
   Test:

changelog.d/switch-to-ruff.changed.md

@@ -0,0 +1 @@
+Switched code formatter from black to ruff format.

examples/employment_income_variation_uk.py

@@ -125,17 +125,12 @@ def create_dataset_with_varied_employment_income(
         "region": ["LONDON"] * n_households,  # Required by policyengine-uk
         "council_tax": [0.0] * n_households,  # Simplified - no council tax
         "rent": [median_annual_rent] * n_households,  # Median UK rent
-        "tenure_type": ["RENT_PRIVATELY"]
-        * n_households,  # Required for uprating
+        "tenure_type": ["RENT_PRIVATELY"] * n_households,  # Required for uprating
     }
 
     # Create MicroDataFrames
-    person_df = MicroDataFrame(
-        pd.DataFrame(person_data), weights="person_weight"
-    )
-    benunit_df = MicroDataFrame(
-        pd.DataFrame(benunit_data), weights="benunit_weight"
-    )
+    person_df = MicroDataFrame(pd.DataFrame(person_data), weights="person_weight")
+    benunit_df = MicroDataFrame(pd.DataFrame(benunit_data), weights="benunit_weight")
     household_df = MicroDataFrame(
         pd.DataFrame(household_data), weights="household_weight"
     )
@@ -273,9 +268,7 @@ def visualise_results(results: dict) -> None:
     # Calculate net employment income (employment income minus tax)
     net_employment = [
         emp - tax
-        for emp, tax in zip(
-            results["employment_income_hh"], results["household_tax"]
-        )
+        for emp, tax in zip(results["employment_income_hh"], results["household_tax"])
     ]
 
     # Stack benefits and income components using PolicyEngine colors
@@ -339,17 +332,11 @@ def main():
     simulation = run_simulation(dataset)
 
     print("Extracting results using aggregate filters...")
-    results = extract_results_by_employment_income(
-        simulation, employment_incomes
-    )
+    results = extract_results_by_employment_income(simulation, employment_incomes)
 
     print("\nSample results:")
     for emp_inc in [0, 25000, 50000, 100000]:
-        idx = (
-            employment_incomes.index(emp_inc)
-            if emp_inc in employment_incomes
-            else -1
-        )
+        idx = employment_incomes.index(emp_inc) if emp_inc in employment_incomes else -1
         if idx >= 0:
             print(
                 f"  Employment income £{emp_inc:,}: HBAI net income £{results['hbai_household_net_income'][idx]:,.0f}"

examples/employment_income_variation_us.py

@@ -127,24 +127,16 @@ def create_dataset_with_varied_employment_income(
     }
 
     # Create MicroDataFrames
-    person_df = MicroDataFrame(
-        pd.DataFrame(person_data), weights="person_weight"
-    )
+    person_df = MicroDataFrame(pd.DataFrame(person_data), weights="person_weight")
     household_df = MicroDataFrame(
         pd.DataFrame(household_data), weights="household_weight"
     )
     marital_unit_df = MicroDataFrame(
         pd.DataFrame(marital_unit_data), weights="marital_unit_weight"
     )
-    family_df = MicroDataFrame(
-        pd.DataFrame(family_data), weights="family_weight"
-    )
-    spm_unit_df = MicroDataFrame(
-        pd.DataFrame(spm_unit_data), weights="spm_unit_weight"
-    )
-    tax_unit_df = MicroDataFrame(
-        pd.DataFrame(tax_unit_data), weights="tax_unit_weight"
-    )
+    family_df = MicroDataFrame(pd.DataFrame(family_data), weights="family_weight")
+    spm_unit_df = MicroDataFrame(pd.DataFrame(spm_unit_data), weights="spm_unit_weight")
+    tax_unit_df = MicroDataFrame(pd.DataFrame(tax_unit_data), weights="tax_unit_weight")
 
     # Create temporary file
     tmpdir = tempfile.mkdtemp()
@@ -227,9 +219,7 @@ def visualise_results(results: dict) -> None:
     # Calculate net employment income (employment income minus tax)
     net_employment = [
         emp - tax
-        for emp, tax in zip(
-            results["employment_income_hh"], results["household_tax"]
-        )
+        for emp, tax in zip(results["employment_income_hh"], results["household_tax"])
     ]
 
     # Stack benefits and income components using PolicyEngine colors
@@ -287,17 +277,11 @@ def main():
     simulation = run_simulation(dataset)
 
     print("Extracting results using aggregate filters...")
-    results = extract_results_by_employment_income(
-        simulation, employment_incomes
-    )
+    results = extract_results_by_employment_income(simulation, employment_incomes)
 
     print("\nSample results:")
     for emp_inc in [0, 50000, 100000, 200000]:
-        idx = (
-            employment_incomes.index(emp_inc)
-            if emp_inc in employment_incomes
-            else -1
-        )
+        idx = employment_incomes.index(emp_inc) if emp_inc in employment_incomes else -1
         if idx >= 0:
             print(
                 f"  Employment income ${emp_inc:,}: household net income ${results['household_net_income'][idx]:,.0f}"

examples/household_impact_example.py

@@ -34,13 +34,9 @@ def uk_example():
     result = calculate_uk_impact(household)
 
     print("\nSingle adult, £50k income:")
-    print(
-        f"  Net income: £{result.household['hbai_household_net_income']:,.0f}"
-    )
+    print(f"  Net income: £{result.household['hbai_household_net_income']:,.0f}")
     print(f"  Income tax: £{result.person[0]['income_tax']:,.0f}")
-    print(
-        f"  National Insurance: £{result.person[0]['national_insurance']:,.0f}"
-    )
+    print(f"  National Insurance: £{result.person[0]['national_insurance']:,.0f}")
     print(f"  Total tax: £{result.household['household_tax']:,.0f}")
 
     # Family with two children, £30k income, renting
@@ -64,9 +60,7 @@ def uk_example():
     result = calculate_uk_impact(household)
 
     print("\nFamily (2 adults, 2 children), £30k income, renting:")
-    print(
-        f"  Net income: £{result.household['hbai_household_net_income']:,.0f}"
-    )
+    print(f"  Net income: £{result.household['hbai_household_net_income']:,.0f}")
     print(f"  Income tax: £{result.person[0]['income_tax']:,.0f}")
     print(f"  Child benefit: £{result.benunit[0]['child_benefit']:,.0f}")
     print(f"  Universal credit: £{result.benunit[0]['universal_credit']:,.0f}")
@@ -81,9 +75,7 @@ def us_example():
 
     # Single adult earning $50,000
     household = USHouseholdInput(
-        people=[
-            {"age": 35, "employment_income": 50_000, "is_tax_unit_head": True}
-        ],
+        people=[{"age": 35, "employment_income": 50_000, "is_tax_unit_head": True}],
         tax_unit={"filing_status": "SINGLE"},
         household={"state_code_str": "CA"},
         year=2024,

examples/income_distribution_us.py

@@ -26,9 +26,7 @@
 
 def load_representative_data(year: int = 2024) -> PolicyEngineUSDataset:
     """Load representative household microdata for a given year."""
-    dataset_path = (
-        Path(__file__).parent / "data" / f"enhanced_cps_2024_year_{year}.h5"
-    )
+    dataset_path = Path(__file__).parent / "data" / f"enhanced_cps_2024_year_{year}.h5"
 
     if not dataset_path.exists():
         raise FileNotFoundError(
@@ -83,15 +81,11 @@ def calculate_income_decile_statistics(simulation: Simulation) -> dict:
             quantile_eq=decile_num,
         )
         if decile_num == 1:
-            print(
-                f"    First Aggregate created ({time.time() - pre_create:.2f}s)"
-            )
+            print(f"    First Aggregate created ({time.time() - pre_create:.2f}s)")
         pre_run = time.time()
         agg.run()
         if decile_num == 1:
-            print(
-                f"    First Aggregate.run() complete ({time.time() - pre_run:.2f}s)"
-            )
+            print(f"    First Aggregate.run() complete ({time.time() - pre_run:.2f}s)")
         market_incomes.append(agg.result / 1e9)
 
         agg = Aggregate(
@@ -234,9 +228,7 @@ def calculate_income_decile_statistics(simulation: Simulation) -> dict:
         print(f"  {prog} complete ({time.time() - prog_start:.2f}s)")
 
     print(f"Tax benefits complete ({time.time() - tax_benefits_start:.2f}s)")
-    print(
-        f"\nTotal statistics calculation time: {time.time() - start_time:.2f}s"
-    )
+    print(f"\nTotal statistics calculation time: {time.time() - start_time:.2f}s")
 
     return {
         "deciles": deciles,
@@ -392,9 +384,7 @@ def main():
     print(f"Total benefits: ${total_benefits:.1f}bn")
     print(f"Total net income: ${total_net_income:.1f}bn")
     print(f"Total households: {total_households:.1f}m")
-    print(
-        f"Average effective tax rate: {total_tax / total_market_income * 100:.1f}%"
-    )
+    print(f"Average effective tax rate: {total_tax / total_market_income * 100:.1f}%")
 
     print("\nBenefit programs by decile:")
     benefit_programs = [

examples/policy_change_uk.py

@@ -82,9 +82,7 @@ def run_baseline_simulation(dataset: PolicyEngineUKDataset) -> Simulation:
     return simulation
 
 
-def run_reform_simulation(
-    dataset: PolicyEngineUKDataset, policy: Policy
-) -> Simulation:
+def run_reform_simulation(dataset: PolicyEngineUKDataset, policy: Policy) -> Simulation:
     """Run reform microsimulation with policy changes."""
     simulation = Simulation(
         dataset=dataset,
@@ -95,9 +93,7 @@ def run_reform_simulation(
     return simulation
 
 
-def analyse_overall_impact(
-    baseline_sim: Simulation, reform_sim: Simulation
-) -> dict:
+def analyse_overall_impact(baseline_sim: Simulation, reform_sim: Simulation) -> dict:
     """Analyse overall winners, losers, and financial impact."""
     winners = ChangeAggregate(
         baseline_simulation=baseline_sim,
@@ -198,9 +194,7 @@ def analyse_impact_by_income_decile(
     }
 
 
-def visualise_results(
-    overall: dict, by_decile: dict, reform_name: str
-) -> None:
+def visualise_results(overall: dict, by_decile: dict, reform_name: str) -> None:
     """Create visualisations of policy change impacts."""
     fig = make_subplots(
         rows=1,
@@ -270,9 +264,7 @@ def print_summary(overall: dict, decile: dict, reform_name: str) -> None:
     print(f"  Losers: {overall['losers']:.2f}m households")
     print(f"  No change: {overall['no_change']:.2f}m households")
     print("\nFinancial impact:")
-    print(
-        f"  Net income change: £{overall['total_change']:.2f}bn (negative = loss)"
-    )
+    print(f"  Net income change: £{overall['total_change']:.2f}bn (negative = loss)")
     print(f"  Tax revenue change: £{overall['tax_revenue_change']:.2f}bn")
     print("\nImpact by income decile:")
     for i, label in enumerate(decile["labels"]):