"""
Poverty Headcount Africa Dataset Generator

Parameter Evidence Table:
| Parameter | Source | Value | Notes |
|-----------|--------|-------|-------|
| $2.15/day poverty line | World Bank | $2.15 | Extreme poverty threshold |
| $3.00/day | World Bank 2024 | 46% of SSA | Current poverty rate |
| $3.65/day | World Bank | - | Working poverty threshold |
| $6.85/day | World Bank | - | Upper middle-income threshold |

Countries: 15 African nations
Years: 2018-2025
"""

import numpy as np
import pandas as pd
from pathlib import Path


COUNTRIES = [
    "Nigeria", "Ethiopia", "Tanzania", "Kenya", "Uganda", 
    "Mozambique", "Ghana", "Cote d'Ivoire", "Cameroon", "Senegal",
    "Zambia", "Malawi", "Rwanda", "Burkina Faso", "Mali"
]

YEARS = list(range(2018, 2026))

BASE_RATES = {
    "below_2.15": 0.28,
    "below_3.00": 0.46,
    "below_3.65": 0.58,
    "below_6.85": 0.82
}

COUNTRY_ADJUSTMENTS = {
    "Nigeria": {"below_2.15": 1.3, "below_3.00": 1.2, "below_3.65": 1.15, "below_6.85": 1.1},
    "Ethiopia": {"below_2.15": 0.9, "below_3.00": 0.85, "below_3.65": 0.9, "below_6.85": 0.95},
    "Tanzania": {"below_2.15": 0.85, "below_3.00": 0.9, "below_3.65": 0.9, "below_6.85": 0.9},
    "Kenya": {"below_2.15": 0.6, "below_3.00": 0.55, "below_3.65": 0.5, "below_6.85": 0.45},
    "Uganda": {"below_2.15": 0.75, "below_3.00": 0.8, "below_3.65": 0.85, "below_6.85": 0.9},
    "Mozambique": {"below_2.15": 1.1, "below_3.00": 1.05, "below_3.65": 1.0, "below_6.85": 1.0},
    "Ghana": {"below_2.15": 0.5, "below_3.00": 0.45, "below_3.65": 0.4, "below_6.85": 0.35},
    "Cote d'Ivoire": {"below_2.15": 0.7, "below_3.00": 0.65, "below_3.65": 0.6, "below_6.85": 0.55},
    "Cameroon": {"below_2.15": 0.8, "below_3.00": 0.75, "below_3.65": 0.7, "below_6.85": 0.65},
    "Senegal": {"below_2.15": 0.65, "below_3.00": 0.6, "below_3.65": 0.55, "below_6.85": 0.5},
    "Zambia": {"below_2.15": 0.9, "below_3.00": 0.85, "below_3.65": 0.8, "below_6.85": 0.75},
    "Malawi": {"below_2.15": 1.0, "below_3.00": 0.95, "below_3.65": 0.9, "below_6.85": 0.85},
    "Rwanda": {"below_2.15": 0.7, "below_3.00": 0.65, "below_3.65": 0.6, "below_6.85": 0.55},
    "Burkina Faso": {"below_2.15": 0.85, "below_3.00": 0.8, "below_3.65": 0.75, "below_6.85": 0.7},
    "Mali": {"below_2.15": 0.95, "below_3.00": 0.9, "below_3.65": 0.85, "below_6.85": 0.8}
}


def dag_sample(probabilities, n_samples, rng):
    """DAG-based sampling with probability weighting."""
    indices = rng.choice(len(probabilities), size=n_samples, p=probabilities, replace=True)
    unique, counts = np.unique(indices, return_counts=True)
    return dict(zip(unique, counts))


def generate_dataset(scenario: str, seed: int) -> pd.DataFrame:
    """Generate poverty headcount dataset for Africa."""
    np.random.seed(seed)
    rng = np.random.default_rng(seed)
    
    scenario_config = {
        "low_burden": {"n": 4000, "variance_scale": 0.8},
        "moderate": {"n": 5000, "variance_scale": 1.0},
        "high_burden": {"n": 6000, "variance_scale": 1.2}
    }
    
    config = scenario_config[scenario]
    n = config["n"]
    scale = config["variance_scale"]
    
    records = []
    
    for year in YEARS:
        year_factor = 1.0 - (year - 2018) * 0.015
        
        for country in COUNTRIES:
            adj = COUNTRY_ADJUSTMENTS[country]
            
            for threshold in ["below_2.15", "below_3.00", "below_3.65", "below_6.85"]:
                base_rate = BASE_RATES[threshold]
                adjusted_rate = min(0.99, base_rate * adj[threshold] * year_factor * scale)
                adjusted_rate = max(0.01, adjusted_rate)
                
                n_country_year = n // (len(YEARS) * len(COUNTRIES))
                poverty_count = int(n_country_year * adjusted_rate)
                
                for _ in range(n_country_year):
                    records.append({
                        "country": country,
                        "year": year,
                        "poverty_line": threshold,
                        "headcount_ratio": adjusted_rate + rng.normal(0, 0.02 * scale),
                        "population_below": poverty_count * 1000,
                        "total_population": n_country_year * 1000
                    })
    
    df = pd.DataFrame(records)
    df["headcount_ratio"] = df["headcount_ratio"].clip(0, 1)
    
    return df


def main():
    output_dir = Path(__file__).parent
    output_dir.mkdir(parents=True, exist_ok=True)
    
    scenarios = {
        "low_burden": 42,
        "moderate": 43,
        "high_burden": 44
    }
    
    for scenario, seed in scenarios.items():
        df = generate_dataset(scenario, seed)
        df.to_csv(output_dir / f"poverty_headcount_{scenario}.csv", index=False)
        print(f"Generated {scenario}: {len(df)} rows, seed={seed}")


if __name__ == "__main__":
    main()