"""
Multidimensional Poverty Africa Dataset Generator

Parameter Evidence Table:
| Parameter | Source | Value | Notes |
|-----------|--------|-------|-------|
| MPI Threshold | UNDP/OPHI | <=0.33 | Multidimensional poverty threshold |
| Severe MPI | UNDP/OPHI | >0.5 | Severe multidimensional poverty |
| Health deprivation | MPI | 1/3 weight | Health dimension weight |
| Education deprivation | MPI | 1/3 weight | Education dimension weight |
| Living standards | MPI | 1/3 weight | Standard of living weight |

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_MPI = 0.28

COUNTRY_MPI = {
    "Nigeria": 0.35, "Ethiopia": 0.33, "Tanzania": 0.30, "Kenya": 0.18,
    "Uganda": 0.24, "Mozambique": 0.38, "Ghana": 0.14, "Cote d'Ivoire": 0.22,
    "Cameroon": 0.23, "Senegal": 0.19, "Zambia": 0.27, "Malawi": 0.31,
    "Rwanda": 0.20, "Burkina Faso": 0.28, "Mali": 0.32
}

DIMENSION_WEIGHTS = {
    "health": 0.33, "education": 0.33, "living_standards": 0.34
}

INDICATOR_WEIGHTS = {
    "nutrition": 0.17, "child_mortality": 0.16,
    "years_of_schooling": 0.17, "school_attendance": 0.16,
    "cooking_fuel": 0.085, "sanitation": 0.085, "drinking_water": 0.085,
    "electricity": 0.085, "housing": 0.085, "assets": 0.085
}


def generate_dataset(scenario: str, seed: int) -> pd.DataFrame:
    """Generate multidimensional poverty 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) * 0.012
        
        for country in COUNTRIES:
            mpi = COUNTRY_MPI[country] * year_factor * scale
            mpi = min(0.6, max(0.05, mpi))
            
            health_score = mpi * DIMENSION_WEIGHTS["health"] + rng.normal(0, 0.03)
            education_score = mpi * DIMENSION_WEIGHTS["education"] + rng.normal(0, 0.03)
            living_score = mpi * DIMENSION_WEIGHTS["living_standards"] + rng.normal(0, 0.03)
            
            n_country_year = n // (len(YEARS) * len(COUNTRIES))
            
            for _ in range(n_country_year):
                records.append({
                    "country": country,
                    "year": year,
                    "mpi": mpi + rng.normal(0, 0.015 * scale),
                    "headcount_ratio": min(0.95, max(0.05, mpi * 2.5 + rng.normal(0, 0.02))),
                    "intensity": min(0.6, max(0.1, mpi / max(0.01, mpi * 2.5))),
                    "health_deprivation": max(0, min(1, health_score + rng.normal(0, 0.04))),
                    "education_deprivation": max(0, min(1, education_score + rng.normal(0, 0.04))),
                    "living_standards_deprivation": max(0, min(1, living_score + rng.normal(0, 0.04))),
                    "is_poor": mpi < 0.33,
                    "is_severely_poor": mpi > 0.5
                })
    
    df = pd.DataFrame(records)
    df["mpi"] = df["mpi"].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"multidimensional_poverty_{scenario}.csv", index=False)
        print(f"Generated {scenario}: {len(df)} rows, seed={seed}")


if __name__ == "__main__":
    main()