remove unneeded files

docs/streamlit/pages/Capital_Gains_Tax.py

@@ -36,9 +36,9 @@
     / "imputations"
     / "capital_gains_distribution_advani_summers.csv.gz"
 )
-capital_gains["maximum_total_income"] = (
-    capital_gains.minimum_total_income.shift(-1).fillna(np.inf)
-)
+capital_gains[
+    "maximum_total_income"
+] = capital_gains.minimum_total_income.shift(-1).fillna(np.inf)
 # Fit a spline to each income band's percentiles
 from scipy.interpolate import UnivariateSpline
 
@@ -119,7 +119,6 @@ def impute_capital_gains(total_income: float) -> float:
 )
 
 with st.expander("Capital gains imputation test runner"):
-
     income = st.slider("Total income", 0, 500000, 50000, 1000)
 
     with st.spinner("Imputing capital gains..."):

policyengine_uk/utils/solve_private_school_attendance_factor.py

@@ -3,25 +3,30 @@
 
 # Initialize variables to track the best private_school_factor and its result
 best_factor = None
-smallest_difference = float('inf')
+smallest_difference = float("inf")
 
 # Loop over values of private_school_factor from 0.7 to 0.8 in steps of 0.01
 for factor in [round(x * 0.01, 2) for x in range(70, 81)]:
     # Define the reform with the current private_school_factor value
-    reform = Reform.from_dict({
-        "gov.contrib.labour.private_school_vat": {
-            "2024-01-01.2100-12-31": 0.2
+    reform = Reform.from_dict(
+        {
+            "gov.contrib.labour.private_school_vat": {
+                "2024-01-01.2100-12-31": 0.2
+            },
+            "gov.simulation.private_school_vat.private_school_factor": {
+                "2024-01-01.2100-12-31": factor
+            },
         },
-        "gov.simulation.private_school_vat.private_school_factor": {
-            "2024-01-01.2100-12-31": factor
-        }
-    }, country_id="uk")
+        country_id="uk",
+    )
 
     # Run the reformed microsimulation
     reformed = Microsimulation(reform=reform)
 
     # Calculate the number of students attending private school in thousands
-    private_school_attendance = reformed.calculate("attends_private_school", period=2025).sum() / 1e3
+    private_school_attendance = (
+        reformed.calculate("attends_private_school", period=2025).sum() / 1e3
+    )
 
     # Compare the result with 550 and track the best value
     difference = abs(private_school_attendance - 550)
@@ -30,5 +35,7 @@
         best_factor = factor
 
 # Report the best private_school_factor
-print(f"The best private_school_factor is {best_factor} with a difference of {smallest_difference}")
+print(
+    f"The best private_school_factor is {best_factor} with a difference of {smallest_difference}"
+)
 # The best private_school_factor is 0.77 with a difference of 1.3427231160653719

policyengine_uk/variables/contrib/labour/private_school_vat.py

@@ -18,9 +18,7 @@ def formula(person, period, parameters):
             ps_vat_params.private_school_attendance_rate
         )
 
-        population_adjustment_factor = (
-            ps_vat_params.private_school_factor
-        )
+        population_adjustment_factor = ps_vat_params.private_school_factor
 
         person = household.members