"""
Climate Vulnerability and Exposure Dataset Generator for Africa

================================================================================
PARAMETER EVIDENCE TABLE
================================================================================
| Parameter                    | Value/Range           | Source                   |
|------------------------------|-----------------------|--------------------------|
| Africa warming rate          | 1.5x global average   | IPCC AR6 2023            |
| Extreme heat events increase | 4x since 1990         | WMO State of Climate     |
| Climate-related displacement | 7M annually           | IDMC 2024                |
| Agricultural GDP loss risk   | 10-15% by 2050        | World Bank 2023          |
| Adaptive capacity index      | 0.25-0.45 (Africa)    | ND-GAIN 2024             |
| Climate finance gap          | $250B annually        | AfDB 2024                |
| Coastal flood exposure       | 54M people            | WRI Aqueduct 2024        |
| Rainfall variability         | ±40% from mean        | ClimDev-Africa 2024      |
================================================================================

DAG Structure:
geographic_exposure -> climate_hazard_probability -> vulnerability_index
economic_capacity -> adaptive_capacity
infrastructure_resilience -> exposure_score
institutional_capacity -> response_capability
temperature_trends -> heat_stress_index
precipitation_patterns -> drought_flood_risk
"""

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

COUNTRIES = [
    "Kenya", "Uganda", "Nigeria", "Ghana", "Tanzania", "Ethiopia",
    "Malawi", "Zambia", "Senegal", "Rwanda", "Niger", "Mali",
    "DRC", "Mozambique", "South Africa"
]

YEARS = list(range(2018, 2026))

COUNTRY_VULNERABILITY_BASE = {
    "Niger": {"exposure": 0.85, "adaptive_capacity": 0.25},
    "Mali": {"exposure": 0.82, "adaptive_capacity": 0.28},
    "DRC": {"exposure": 0.75, "adaptive_capacity": 0.30},
    "Mozambique": {"exposure": 0.78, "adaptive_capacity": 0.32},
    "Ethiopia": {"exposure": 0.72, "adaptive_capacity": 0.35},
    "Malawi": {"exposure": 0.70, "adaptive_capacity": 0.33},
    "Tanzania": {"exposure": 0.65, "adaptive_capacity": 0.38},
    "Uganda": {"exposure": 0.62, "adaptive_capacity": 0.40},
    "Kenya": {"exposure": 0.58, "adaptive_capacity": 0.42},
    "Zambia": {"exposure": 0.60, "adaptive_capacity": 0.40},
    "Ghana": {"exposure": 0.50, "adaptive_capacity": 0.48},
    "Senegal": {"exposure": 0.55, "adaptive_capacity": 0.45},
    "Nigeria": {"exposure": 0.52, "adaptive_capacity": 0.44},
    "Rwanda": {"exposure": 0.48, "adaptive_capacity": 0.52},
    "South Africa": {"exposure": 0.42, "adaptive_capacity": 0.58},
}

CLIMATE_ZONES = ["arid", "semi_arid", "tropical_wet", "tropical_dry", "mediterranean", "highland"]

def generate_dataset(scenario: str, n_samples: int, seed: int) -> pd.DataFrame:
    rng = np.random.default_rng(seed)
    
    burden_mod = {"low_burden": 0.7, "moderate_burden": 1.0, "high_burden": 1.4}[scenario]
    
    countries = rng.choice(COUNTRIES, size=n_samples)
    years = rng.choice(YEARS, size=n_samples)
    
    data = {
        "record_id": [f"CLIM_{i:06d}" for i in range(n_samples)],
        "country": countries,
        "year": years,
        "climate_zone": rng.choice(CLIMATE_ZONES, size=n_samples, 
                                   p=[0.15, 0.25, 0.20, 0.20, 0.05, 0.15]),
    }
    
    zone = data["climate_zone"]
    base_exposure = np.array([COUNTRY_VULNERABILITY_BASE[c]["exposure"] for c in countries])
    base_adaptive = np.array([COUNTRY_VULNERABILITY_BASE[c]["adaptive_capacity"] for c in countries])
    
    year_factor = (years - 2018) * 0.02 * burden_mod
    data["geographic_exposure_index"] = np.clip(
        base_exposure * (1 + year_factor) + rng.normal(0, 0.05, n_samples), 0.1, 1.0
    ).round(3)
    
    data["coastal_proximity"] = rng.choice([0, 1], size=n_samples, p=[0.6, 0.4])
    data["elevation_category"] = rng.choice(["lowland", "mid_elevation", "highland"], 
                                            size=n_samples, p=[0.4, 0.35, 0.25])
    
    zone_temp_factor = {"arid": 1.2, "semi_arid": 1.1, "tropical_wet": 0.9, 
                        "tropical_dry": 1.0, "mediterranean": 0.8, "highland": 0.7}
    temp_factors = np.array([zone_temp_factor[z] for z in zone])
    
    data["temperature_anomaly_c"] = np.clip(
        (0.8 + year_factor) * temp_factors * rng.uniform(0.5, 1.5, n_samples), 0.2, 3.5
    ).round(2)
    
    zone_precip_factor = {"arid": 1.3, "semi_arid": 1.2, "tropical_wet": 1.1, 
                          "tropical_dry": 1.4, "mediterranean": 1.2, "highland": 1.0}
    precip_factors = np.array([zone_precip_factor[z] for z in zone])
    
    data["precipitation_variability_pct"] = np.clip(
        25 + year_factor * 10 * precip_factors + rng.normal(0, 5, n_samples), 10, 60
    ).round(1)
    
    data["extreme_heat_days_year"] = np.clip(
        (20 + data["temperature_anomaly_c"] * 15) * burden_mod * 
        rng.uniform(0.8, 1.2, n_samples), 5, 150
    ).astype(int)
    
    data["heat_stress_index"] = np.clip(
        (data["temperature_anomaly_c"] / 2 + data["extreme_heat_days_year"] / 50) * 
        burden_mod, 0, 10
    ).round(2)
    
    data["drought_probability"] = np.clip(
        data["geographic_exposure_index"] * 0.5 + 
        data["precipitation_variability_pct"] / 200 + 
        rng.uniform(-0.1, 0.1, n_samples), 0, 1
    ).round(3)
    
    data["flood_probability"] = np.clip(
        (data["coastal_proximity"] * 0.2 + 
         data["precipitation_variability_pct"] / 150) * 
        burden_mod + rng.uniform(0, 0.15, n_samples), 0, 0.8
    ).round(3)
    
    data["climate_hazard_index"] = np.clip(
        (data["drought_probability"] * 0.4 + 
         data["flood_probability"] * 0.3 + 
         data["heat_stress_index"] / 20) * burden_mod, 0, 1
    ).round(3)
    
    data["gdp_per_capita_usd"] = np.clip(
        rng.exponential(3000, n_samples) * (1 - data["geographic_exposure_index"] * 0.5), 
        300, 15000
    ).astype(int)
    
    data["agriculture_gdp_share_pct"] = np.clip(
        30 - data["gdp_per_capita_usd"] / 500 + rng.normal(0, 5, n_samples), 5, 50
    ).round(1)
    
    data["infrastructure_quality_index"] = np.clip(
        base_adaptive * 0.8 + rng.normal(0, 0.1, n_samples), 0.1, 0.9
    ).round(3)
    
    data["healthcare_access_index"] = np.clip(
        base_adaptive * 0.9 + data["gdp_per_capita_usd"] / 20000 + 
        rng.normal(0, 0.1, n_samples), 0.1, 0.95
    ).round(3)
    
    data["early_warning_coverage_pct"] = np.clip(
        base_adaptive * 60 + rng.normal(10, 15, n_samples), 5, 90
    ).round(1)
    
    data["social_protection_coverage_pct"] = np.clip(
        data["gdp_per_capita_usd"] / 150 + rng.normal(5, 10, n_samples), 2, 60
    ).round(1)
    
    data["adaptive_capacity_index"] = np.clip(
        (data["infrastructure_quality_index"] * 0.3 + 
         data["healthcare_access_index"] * 0.25 + 
         data["early_warning_coverage_pct"] / 200 + 
         data["social_protection_coverage_pct"] / 150) + 
        rng.normal(0, 0.05, n_samples), 0.1, 0.95
    ).round(3)
    
    data["climate_policy_strength"] = np.clip(
        base_adaptive * 1.2 + rng.normal(0, 0.15, n_samples), 0.1, 0.9
    ).round(3)
    
    data["ndc_ambition_score"] = np.clip(
        data["climate_policy_strength"] * 0.8 + rng.uniform(0, 0.3, n_samples), 0.1, 1.0
    ).round(3)
    
    data["climate_finance_access_million_usd"] = np.clip(
        data["climate_policy_strength"] * 500 + rng.exponential(100, n_samples), 10, 2000
    ).astype(int)
    
    data["vulnerability_index"] = np.clip(
        (data["climate_hazard_index"] * 0.5 + 
         data["geographic_exposure_index"] * 0.3 + 
         (1 - data["adaptive_capacity_index"]) * 0.2) * burden_mod, 0, 1
    ).round(3)
    
    data["exposure_score"] = np.clip(
        (data["geographic_exposure_index"] + data["climate_hazard_index"]) / 2, 0, 1
    ).round(3)
    
    data["risk_category"] = np.select(
        [data["vulnerability_index"] < 0.3, 
         data["vulnerability_index"] < 0.5, 
         data["vulnerability_index"] < 0.7, 
         data["vulnerability_index"] < 0.85, 
         data["vulnerability_index"] >= 0.85],
        ["Very_Low", "Low", "Moderate", "High", "Very_High"]
    )
    
    data["population_at_risk_millions"] = np.clip(
        data["vulnerability_index"] * 15 * burden_mod + 
        rng.exponential(2, n_samples), 0.5, 50
    ).round(2)
    
    data["potential_economic_loss_pct_gdp"] = np.clip(
        data["vulnerability_index"] * 20 + data["agriculture_gdp_share_pct"] / 5, 1, 25
    ).round(1)
    
    return pd.DataFrame(data)

def main():
    output_dir = Path(__file__).parent
    
    scenarios = [
        ("low_burden", 4000, 42),
        ("moderate_burden", 5000, 43),
        ("high_burden", 6000, 44),
    ]
    
    for scenario, n, seed in scenarios:
        df = generate_dataset(scenario, n, seed)
        output_file = output_dir / f"climate_vulnerability_exposure_africa_{scenario}.csv"
        df.to_csv(output_file, index=False)
        print(f"Generated {output_file}: {len(df)} records")

if __name__ == "__main__":
    main()