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Merge pull request #41 from PolicyEngine/api-parity
Add some API impacts
2 parents 8cf7ce0 + 3cafefd commit 7ef8cac

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‎.github/workflows/pr.yml

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@@ -21,7 +21,7 @@ jobs:
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pip install furo
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- name: Generate documentation
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run: make docs
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run: make documentation
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- name: Check documentation build
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run: |

‎.github/workflows/push.yml

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pip install furo
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pip install sphinx-argparse
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- name: Generate documentation
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run: make documentation
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‎docs/_toc.yml

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parts:
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- caption: Maintenance
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chapters:
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- file: maintaining
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- file: maintaining
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- caption: Schema
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chapters:
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- file: schema

‎docs/schema.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Schema\n",
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"\n",
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"This page contains examples of the schema of the returned simulation outputs for given types of simulation. Each subsection specifies the country, scope and whether a reform has been passed. The schema is given in YAML format.\n",
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"\n",
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"## UK, macro, reform-comparison"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"tags": [
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"hide-input"
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]
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},
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"outputs": [
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{
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"data": {
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"text/markdown": [
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"```yaml\n",
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"macro:\n",
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" baseline:\n",
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" gov:\n",
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" balance:\n",
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" total_gov_spending: <class 'numpy.float64'>\n",
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" total_state_tax: <class 'int'>\n",
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" total_tax_revenue: <class 'numpy.float64'>\n",
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" programs:\n",
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" child_benefit: <class 'numpy.float64'>\n",
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" council_tax: <class 'numpy.float64'>\n",
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" fuel_duty: <class 'numpy.float64'>\n",
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" income_tax: <class 'numpy.float64'>\n",
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" national_insurance: <class 'numpy.float64'>\n",
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" ni_employer: <class 'numpy.float64'>\n",
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" pension_credit: <class 'numpy.float64'>\n",
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" state_pension: <class 'numpy.float64'>\n",
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" tax_credits: <class 'numpy.float64'>\n",
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" universal_credit: <class 'numpy.float64'>\n",
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" vat: <class 'numpy.float64'>\n",
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" household:\n",
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" demographic_values:\n",
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" household_count_people: <class 'list'>\n",
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" household_weight: <class 'list'>\n",
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" person_weight: <class 'list'>\n",
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" comparison:\n",
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" revenue_impact:\n",
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" tax_revenues: <class 'numpy.float64'>\n",
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" reform:\n",
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" gov:\n",
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" balance:\n",
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" total_gov_spending: <class 'numpy.float64'>\n",
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" total_state_tax: <class 'int'>\n",
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" total_tax_revenue: <class 'numpy.float64'>\n",
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" programs:\n",
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" child_benefit: <class 'numpy.float64'>\n",
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" council_tax: <class 'numpy.float64'>\n",
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" fuel_duty: <class 'numpy.float64'>\n",
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" income_tax: <class 'numpy.float64'>\n",
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" national_insurance: <class 'numpy.float64'>\n",
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" ni_employer: <class 'numpy.float64'>\n",
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" pension_credit: <class 'numpy.float64'>\n",
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" state_pension: <class 'numpy.float64'>\n",
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" tax_credits: <class 'numpy.float64'>\n",
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" universal_credit: <class 'numpy.float64'>\n",
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" vat: <class 'numpy.float64'>\n",
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" household:\n",
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" demographic_values:\n",
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" household_count_people: <class 'list'>\n",
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" household_weight: <class 'list'>\n",
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" person_weight: <class 'list'>\n",
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"\n",
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"```"
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],
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"text/plain": [
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"<IPython.core.display.Markdown object>"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from policyengine import Simulation\n",
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"from IPython.display import Markdown\n",
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"import yaml\n",
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"\n",
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"def replace_value_with_dtype(tree):\n",
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" for key in tree:\n",
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" if isinstance(tree[key], dict):\n",
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" replace_value_with_dtype(tree[key])\n",
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" else:\n",
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" tree[key] = str(type(tree[key]))\n",
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" return tree\n",
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"\n",
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"sim = Simulation(\n",
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" country=\"uk\",\n",
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" scope=\"macro\",\n",
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" time_period=2025,\n",
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" reform={\n",
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" \"gov.hmrc.income_tax.allowances.personal_allowance.amount\": {\n",
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" \"2025\": 0,\n",
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" }\n",
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" }\n",
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")\n",
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"sim.calculate(\"macro\")\n",
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"tree = replace_value_with_dtype(sim.outputs)\n",
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"\n",
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"Markdown('```yaml\\n' + yaml.dump(tree, indent=2) + '\\n```')"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "base",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.14"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎policyengine/outputs/macro/comparison/revenue_impact.py

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Returns:
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float: The revenue impact of the simulation.
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"""
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tax_revenue_baseline = simulation.calculate("macro/baseline/tax_revenue")
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tax_revenue_reform = simulation.calculate("macro/reform/tax_revenue")
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return tax_revenue_reform - tax_revenue_baseline
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tax_revenue_baseline = simulation.calculate(
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"macro/baseline/gov/balance"
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)["total_tax_revenue"]
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tax_revenue_reform = simulation.calculate(
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"macro/reform/gov/balance"
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)["total_tax_revenue"]
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tax_revenue_impact = tax_revenue_reform - tax_revenue_baseline
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return {
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"tax_revenues": tax_revenue_impact,
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}

‎policyengine/outputs/macro/single/gov/balance.py

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from policyengine import Simulation
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def balance(simulation: Simulation) -> dict:
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sim = simulation.baseline
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if simulation.country == "uk":
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total_tax = sim.calculate("gov_tax").sum()
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total_spending = sim.calculate("gov_spending").sum()
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total_state_tax = 0
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elif simulation.country == "us":
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total_tax = sim.calculate("household_tax").sum()
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total_spending = sim.calculate("household_benefits").sum()
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total_state_tax = simulation.calculate(
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"household_state_income_tax"
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).sum()
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return {
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"total_tax_revenue": total_tax,
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"total_gov_spending": total_spending,
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"total_state_tax": total_state_tax,
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}

‎policyengine/outputs/macro/single/gov/programs.py

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from policyengine import Simulation
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from dataclasses import dataclass
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@dataclass
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class UKProgram:
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name: str
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is_positive: bool
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class UKPrograms:
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PROGRAMS = [
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UKProgram("income_tax", True),
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UKProgram("national_insurance", True),
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UKProgram("vat", True),
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UKProgram("council_tax", True),
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UKProgram("fuel_duty", True),
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UKProgram("tax_credits", False),
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UKProgram("universal_credit", False),
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UKProgram("child_benefit", False),
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UKProgram("state_pension", False),
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UKProgram("pension_credit", False),
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UKProgram("ni_employer", True),
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]
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def programs(simulation: Simulation) -> dict:
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if simulation.country == "uk":
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return {
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program.name: simulation.baseline.calculate(
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program.name, map_to="household"
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).sum()
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* (1 if program.is_positive else -1)
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for program in UKPrograms.PROGRAMS
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}

‎policyengine/outputs/macro/single/household/demographic_values.py

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from policyengine import Simulation
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def demographic_values(simulation: Simulation) -> dict:
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sim = simulation.baseline
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household_count_people = (
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sim.calculate("household_count_people").astype(int).tolist()
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)
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person_weight = sim.calculate("person_weight").astype(float).tolist()
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household_weight = sim.calculate("household_weight").astype(float).tolist()
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return {
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"household_count_people": household_count_people,
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"person_weight": person_weight,
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"household_weight": household_weight,
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}

‎policyengine/outputs/macro/single/tax_revenue.py

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‎policyengine/simulation.py

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"""
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if output.endswith("/"):
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output = output[:-1]
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if output == "":
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output = list(self.outputs.keys())[0]
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node = self.outputs
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for child_key in output.split("/")[:-1]:
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node = node[child_key]
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parent = node
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child_key = output.split("/")[-1]
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node = parent[child_key]

‎test.ipynb

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