"""
Dataset Generator: Displacement in Informal Settlements Africa

Parameter Evidence Table:
| Parameter | Value | Source |
|-----------|-------|--------|
| Annual eviction rate in slums | 5-15% | UN-Habitat 2023 |
| Forced displacement drivers | Infrastructure, gentrification, events | UN-Habitat estimates |
| Resettlement success rate | 20-40% adequate | World Bank displacement data |
| Climate-related displacement | Increasing trend | OECD Africa Urbanization 2025 |
| Year range | 2018-2025 | Project scope |
| Countries | 15 African nations | Regional coverage |

DAG Structure:
country -> displacement_trigger -> eviction_event -> resettlement_outcome -> recovery_time
"""

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

COUNTRIES = [
    'Nigeria', 'Kenya', 'Ethiopia', 'Egypt', 'South Africa',
    'Tanzania', 'Morocco', 'Algeria', 'Ghana', 'Uganda',
    'Mozambique', 'Zambia', 'Malawi', 'Rwanda', 'Senegal'
]

SCENARIOS = {
    'low_burden': {'n': 4000, 'displacement_rate': 0.03, 'seed': 42},
    'moderate': {'n': 5000, 'displacement_rate': 0.08, 'seed': 43},
    'high': {'n': 6000, 'displacement_rate': 0.15, 'seed': 44}
}

CITIES = {
    'Nigeria': ['Lagos', 'Port Harcourt', 'Abuja'],
    'Kenya': ['Nairobi', 'Mombasa'],
    'Ethiopia': ['Addis Ababa'],
    'Egypt': ['Cairo', 'Giza'],
    'South Africa': ['Johannesburg', 'Durban'],
    'Tanzania': ['Dar es Salaam'],
    'Morocco': ['Casablanca', 'Marrakech'],
    'Algeria': ['Algiers'],
    'Ghana': ['Accra'],
    'Uganda': ['Kampala'],
    'Mozambique': ['Maputo'],
    'Zambia': ['Lusaka'],
    'Malawi': ['Lilongwe', 'Blantyre'],
    'Rwanda': ['Kigali'],
    'Senegal': ['Dakar']
}

SETTLEMENTS = {
    'Nigeria': ['Mushin', 'Ajegunle'],
    'Kenya': ['Kibera', 'Mathare'],
    'Ethiopia': ['Kality'],
    'Egypt': ['Ezbet El-Nakhl'],
    'South Africa': ['Soweto', 'Alexandra'],
    'Tanzania': ['Kibera'],
    'Ghana': ['Old Fadama'],
    'Uganda': ['Kisenyi']
}

DISPLACEMENT_TRIGGERS = [
    'infrastructure_development', 'gentrification', 'climate_disaster',
    'eviction_order', 'land_reclamation', 'urban_renewal', 'sporting_event',
    'political_decision', 'environmental_risk', 'commercial_development'
]

def dag_sample_displacement_event(displacement_rate, rng):
    if rng.random() < displacement_rate:
        return rng.choice(DISPLACEMENT_TRIGGERS)
    return 'no_displacement'

def dag_sample_eviction_details(trigger, rng):
    if trigger == 'no_displacement':
        return {
            'evicted': False,
            'households_affected': 0,
            'resettlement_type': 'none'
        }
    
    households = int(rng.uniform(50, 5000))
    resettlement = rng.choice(['none', 'in_situ', 'relocated', 'informal_compensation'])
    
    return {
        'evicted': True,
        'households_affected': households,
        'resettlement_type': resettlement
    }

def dag_sample_resettlement_outcome(resettlement_type, rng):
    if resettlement_type == 'none':
        return {'adequate_housing': False, 'recovery_years': 0, 'compensation': 0}
    
    adequate_chance = {'in_situ': 0.5, 'relocated': 0.25, 'informal_compensation': 0.15}.get(resettlement_type, 0.2)
    
    return {
        'adequate_housing': rng.random() < adequate_chance,
        'recovery_years': int(rng.uniform(1, 10)) if resettlement_type != 'none' else 0,
        'compensation': int(rng.uniform(0, 5000)) if resettlement_type != 'none' else 0
    }

def generate_dataset(scenario, output_dir):
    config = SCENARIOS[scenario]
    rng = np.random.default_rng(config['seed'])
    
    data = {
        'country': [],
        'city': [],
        'settlement': [],
        'year': [],
        'displacement_trigger': [],
        'evicted': [],
        'households_affected': [],
        'resettlement_type': [],
        'adequate_housing': [],
        'recovery_years': [],
        'compensation_received_usd': [],
        'legal_recourse_available': [],
        'notice_given_days': [],
        'court_order_obtained': [],
        'community_consultation': [],
        'alternative_land_provided': [],
        'follow_up_survey_available': [],
        'psychological_impact': [],
        'economic_impact_score': [],
        'housing_quality_change': []
    }
    
    for _ in range(config['n']):
        country = rng.choice(COUNTRIES)
        city = rng.choice(CITIES.get(country, ['Unknown']))
        settlement = rng.choice(SETTLEMENTS.get(country, ['Unknown']))
        year = rng.integers(2018, 2026)
        
        trigger = dag_sample_displacement_event(config['displacement_rate'], rng)
        eviction_details = dag_sample_eviction_details(trigger, rng)
        outcome = dag_sample_resettlement_outcome(eviction_details['resettlement_type'], rng)
        
        data['country'].append(country)
        data['city'].append(city)
        data['settlement'].append(settlement)
        data['year'].append(year)
        data['displacement_trigger'].append(trigger)
        data['evicted'].append(eviction_details['evicted'])
        data['households_affected'].append(eviction_details['households_affected'])
        data['resettlement_type'].append(eviction_details['resettlement_type'])
        data['adequate_housing'].append(outcome['adequate_housing'])
        data['recovery_years'].append(outcome['recovery_years'])
        data['compensation_received_usd'].append(outcome['compensation'])
        data['legal_recourse_available'].append(rng.choice([True, False], p=[0.4, 0.6]))
        data['notice_given_days'].append(int(rng.uniform(0, 90)) if eviction_details['evicted'] else 0)
        data['court_order_obtained'].append(rng.choice([True, False], p=[0.5, 0.5]) if eviction_details['evicted'] else False)
        data['community_consultation'].append(rng.choice(['none', 'minimal', 'adequate', 'robust']))
        data['alternative_land_provided'].append(rng.choice([True, False], p=[0.2, 0.8]) if eviction_details['evicted'] else False)
        data['follow_up_survey_available'].append(rng.choice([True, False], p=[0.3, 0.7]))
        data['psychological_impact'].append(rng.choice(['none', 'mild', 'moderate', 'severe']) if eviction_details['evicted'] else 'none')
        data['economic_impact_score'].append(round(rng.uniform(10, 90), 1) if eviction_details['evicted'] else 0)
        data['housing_quality_change'].append(rng.choice(['improved', 'same', 'degraded', 'homeless']) if eviction_details['evicted'] else 'same')
    
    df = pd.DataFrame(data)
    output_dir.mkdir(parents=True, exist_ok=True)
    df.to_csv(output_dir / f'{scenario}.csv', index=False)
    print(f"Generated {scenario}: {len(df)} rows")

if __name__ == '__main__':
    base_dir = Path(__file__).parent
    for scenario in SCENARIOS:
        generate_dataset(scenario, base_dir)