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Repin the US local-area overlay to the buildo-acs-local release #488

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MaxGhenis merged 4 commits into from
Jul 24, 2026
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Repin the US local-area overlay to the buildo-acs-local release #488

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35dad90
Repin the US local-area overlay to the buildo-acs-local release
MaxGhenis Jul 24, 2026
9f15709
Format markdown code blocks with current ruff (CI lint drift)
MaxGhenis Jul 24, 2026
34e134d
Add the towncrier fragment for the overlay repin
MaxGhenis Jul 24, 2026
adf60bd
Rebind the TRACE TRO sidecars to the repinned bundle manifest
MaxGhenis Jul 24, 2026
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212 changes: 113 additions & 99 deletions .claude/policyengine-guide.md
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Original file line number Diff line number Diff line change
Expand Up @@ -40,42 +40,48 @@ from microdf import MicroDataFrame
from policyengine.tax_benefit_models.uk import (
PolicyEngineUKDataset,
UKYearData,
uk_latest
uk_latest,
)
from policyengine.core import Simulation

# Create synthetic person data
person_df = MicroDataFrame(
pd.DataFrame({
"person_id": [0, 1, 2],
"person_household_id": [0, 0, 1],
"person_benunit_id": [0, 0, 1],
"age": [35, 8, 40],
"employment_income": [30000, 0, 50000],
"person_weight": [1.0, 1.0, 1.0],
}),
weights="person_weight"
pd.DataFrame(
{
"person_id": [0, 1, 2],
"person_household_id": [0, 0, 1],
"person_benunit_id": [0, 0, 1],
"age": [35, 8, 40],
"employment_income": [30000, 0, 50000],
"person_weight": [1.0, 1.0, 1.0],
}
),
weights="person_weight",
)

# Create household data
household_df = MicroDataFrame(
pd.DataFrame({
"household_id": [0, 1],
"region": ["LONDON", "SOUTH_EAST"],
"rent": [15000, 12000],
"household_weight": [1.0, 1.0],
}),
weights="household_weight"
pd.DataFrame(
{
"household_id": [0, 1],
"region": ["LONDON", "SOUTH_EAST"],
"rent": [15000, 12000],
"household_weight": [1.0, 1.0],
}
),
weights="household_weight",
)

# Create benunit data (UK only)
benunit_df = MicroDataFrame(
pd.DataFrame({
"benunit_id": [0, 1],
"would_claim_uc": [True, True],
"benunit_weight": [1.0, 1.0],
}),
weights="benunit_weight"
pd.DataFrame(
{
"benunit_id": [0, 1],
"would_claim_uc": [True, True],
"benunit_weight": [1.0, 1.0],
}
),
weights="benunit_weight",
)

# Package into dataset
Expand All @@ -88,7 +94,7 @@ dataset = PolicyEngineUKDataset(
person=person_df,
household=household_df,
benunit=benunit_df,
)
),
)

# Run simulation
Expand All @@ -109,23 +115,25 @@ print(output.household[["household_id", "household_net_income"]])
from policyengine.tax_benefit_models.us import (
PolicyEngineUSDataset,
USYearData,
us_latest
us_latest,
)

# Create person data (note: US has more entity types)
person_df = MicroDataFrame(
pd.DataFrame({
"person_id": [0, 1, 2, 3],
"person_household_id": [0, 0, 0, 0],
"person_tax_unit_id": [0, 0, 0, 0],
"person_spm_unit_id": [0, 0, 0, 0],
"person_family_id": [0, 0, 0, 0],
"person_marital_unit_id": [0, 0, 1, 2],
"age": [35, 33, 8, 5],
"employment_income": [60000, 40000, 0, 0],
"person_weight": [1.0, 1.0, 1.0, 1.0],
}),
weights="person_weight"
pd.DataFrame(
{
"person_id": [0, 1, 2, 3],
"person_household_id": [0, 0, 0, 0],
"person_tax_unit_id": [0, 0, 0, 0],
"person_spm_unit_id": [0, 0, 0, 0],
"person_family_id": [0, 0, 0, 0],
"person_marital_unit_id": [0, 0, 1, 2],
"age": [35, 33, 8, 5],
"employment_income": [60000, 40000, 0, 0],
"person_weight": [1.0, 1.0, 1.0, 1.0],
}
),
weights="person_weight",
)

# Create entity dataframes (tax_unit, spm_unit, family, marital_unit, household)
Expand All @@ -142,7 +150,7 @@ dataset = PolicyEngineUSDataset(
family=family_df,
marital_unit=marital_unit_df,
household=household_df,
)
),
)
```

Expand All @@ -159,26 +167,30 @@ income_values = np.linspace(0, 100000, n_scenarios)

# Create person data with all scenarios
person_df = MicroDataFrame(
pd.DataFrame({
"person_id": range(n_scenarios),
"person_household_id": range(n_scenarios),
"person_benunit_id": range(n_scenarios),
"age": [35] * n_scenarios,
"employment_income": income_values,
"person_weight": [1.0] * n_scenarios,
}),
weights="person_weight"
pd.DataFrame(
{
"person_id": range(n_scenarios),
"person_household_id": range(n_scenarios),
"person_benunit_id": range(n_scenarios),
"age": [35] * n_scenarios,
"employment_income": income_values,
"person_weight": [1.0] * n_scenarios,
}
),
weights="person_weight",
)

# Create matching household/benunit data
household_df = MicroDataFrame(
pd.DataFrame({
"household_id": range(n_scenarios),
"region": ["LONDON"] * n_scenarios,
"rent": [15000] * n_scenarios,
"household_weight": [1.0] * n_scenarios,
}),
weights="household_weight"
pd.DataFrame(
{
"household_id": range(n_scenarios),
"region": ["LONDON"] * n_scenarios,
"rent": [15000] * n_scenarios,
"household_weight": [1.0] * n_scenarios,
}
),
weights="household_weight",
)

# ... create dataset and run simulation once for all scenarios
Expand Down Expand Up @@ -229,10 +241,7 @@ reform_sim = Simulation(
reform_sim.run()

# Analyse impact
from policyengine.outputs.change_aggregate import (
ChangeAggregate,
ChangeAggregateType
)
from policyengine.outputs.change_aggregate import ChangeAggregate, ChangeAggregateType

winners = ChangeAggregate(
baseline_simulation=baseline_sim,
Expand Down Expand Up @@ -298,33 +307,28 @@ household_income = dataset.data.map_to_entity(
source_entity="person",
target_entity="household",
columns=["employment_income"],
how="sum"
how="sum",
)

# Map household rent to person level (broadcast)
person_rent = dataset.data.map_to_entity(
source_entity="household",
target_entity="person",
columns=["rent"],
how="project"
source_entity="household", target_entity="person", columns=["rent"], how="project"
)

# Split household savings equally per person
person_savings_share = dataset.data.map_to_entity(
source_entity="household",
target_entity="person",
columns=["total_savings"],
how="divide"
how="divide",
)

# Map custom values
import numpy as np

custom_values = np.array([100, 200, 150])
household_totals = dataset.data.map_to_entity(
source_entity="person",
target_entity="household",
values=custom_values,
how="sum"
source_entity="person", target_entity="household", values=custom_values, how="sum"
)
```

Expand All @@ -335,13 +339,15 @@ from policyengine.utils.plotting import format_fig, COLORS
import plotly.graph_objects as go

fig = go.Figure()
fig.add_trace(go.Scatter(
x=income_values,
y=net_income_values,
mode='lines',
name='Net income',
line=dict(color=COLORS["primary"], width=3)
))
fig.add_trace(
go.Scatter(
x=income_values,
y=net_income_values,
mode="lines",
name="Net income",
line=dict(color=COLORS["primary"], width=3),
)
)

format_fig(
fig,
Expand Down Expand Up @@ -492,42 +498,50 @@ When user asks to:
import pandas as pd
from microdf import MicroDataFrame
from policyengine.tax_benefit_models.uk import (
PolicyEngineUKDataset, UKYearData, uk_latest
PolicyEngineUKDataset,
UKYearData,
uk_latest,
)
from policyengine.core import Simulation

# Create person data
person_df = MicroDataFrame(
pd.DataFrame({
"person_id": [0],
"person_household_id": [0],
"person_benunit_id": [0],
"age": [30],
"employment_income": [30000],
"person_weight": [1.0],
}),
weights="person_weight"
pd.DataFrame(
{
"person_id": [0],
"person_household_id": [0],
"person_benunit_id": [0],
"age": [30],
"employment_income": [30000],
"person_weight": [1.0],
}
),
weights="person_weight",
)

# Create household data
household_df = MicroDataFrame(
pd.DataFrame({
"household_id": [0],
"region": ["LONDON"],
"rent": [12000], # Typical London rent
"household_weight": [1.0],
}),
weights="household_weight"
pd.DataFrame(
{
"household_id": [0],
"region": ["LONDON"],
"rent": [12000], # Typical London rent
"household_weight": [1.0],
}
),
weights="household_weight",
)

# Create benunit data
benunit_df = MicroDataFrame(
pd.DataFrame({
"benunit_id": [0],
"would_claim_uc": [True],
"benunit_weight": [1.0],
}),
weights="benunit_weight"
pd.DataFrame(
{
"benunit_id": [0],
"would_claim_uc": [True],
"benunit_weight": [1.0],
}
),
weights="benunit_weight",
)

# Create and run simulation
Expand All @@ -539,7 +553,7 @@ dataset = PolicyEngineUKDataset(
person=person_df,
household=household_df,
benunit=benunit_df,
)
),
)

simulation = Simulation(
Expand Down
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