#!/usr/bin/env python3
"""
Literature-Informed Oral Health & Dental Disease Dataset
=========================================================

Generates realistic synthetic records of oral health patients in
sub-Saharan Africa, including dental caries, periodontal disease,
noma, oral cancer, treatment access, and outcomes.

References (web-searched):
-----------
[1]  WHO Africa 2024. Africa has largest global increase in
     oral diseases. 6 major conditions: caries, periodontal,
     oral cancer, oral HIV, noma, cleft lip/palate.
[2]  WHO 2022. Global Oral Health Status Report. Untreated
     caries is most prevalent condition globally.
[3]  PubMed 2021. DMFT in East Africa: 2.57 at age 12,
     4.04 at age 15. High caries burden.
[4]  BMC Public Health 2021. Dental caries in adults SSA.
     Limited access to dental care.
[5]  WHO Africa. Noma (cancrum oris) persists in extreme
     poverty. CFR 70-90% untreated. Disfiguring.
[6]  PubMed 2015. Oral health South Africa: DMFT trends,
     national surveys, fluoride.
[7]  Dentist ratio in SSA: <1 per 100,000 population in
     many countries. WHO target 1:7500.
"""

import numpy as np
import pandas as pd
import argparse
import os

SCENARIOS = {
    'dental_clinic': {
        'description': 'Urban dental clinic with dentist, basic '
                       'restorative/extraction capability, X-ray '
                       '(e.g., university dental clinics Nairobi, '
                       'Lagos, Addis Ababa)',
        'dentist_available': True,
        'restorative_available': True,
        'xray_available': True,
        'fluoride_programme': True,
        'treatment_mod': 1.0,
    },
    'district_hospital': {
        'description': 'District hospital with dental officer, '
                       'extraction only, no restorative '
                       '(e.g., district hospitals Tanzania, Malawi)',
        'dentist_available': True,
        'restorative_available': False,
        'xray_available': False,
        'fluoride_programme': False,
        'treatment_mod': 0.6,
    },
    'rural_health_centre': {
        'description': 'Rural health centre, no dental professional, '
                       'basic pain relief, referral '
                       '(e.g., rural CHCs DRC, Niger, Chad)',
        'dentist_available': False,
        'restorative_available': False,
        'xray_available': False,
        'fluoride_programme': False,
        'treatment_mod': 0.2,
    },
}


def generate_dataset(n=10000, seed=42, scenario='district_hospital'):
    rng = np.random.default_rng(seed)
    sc = SCENARIOS[scenario]

    records = []

    for idx in range(n):
        rec = {'id': idx + 1}

        # ── 1. Demographics ──
        rec['age_years'] = max(2, min(80, int(rng.normal(28, 18))))
        rec['sex'] = rng.choice(['M', 'F'], p=[0.48, 0.52])
        rec['child'] = 1 if rec['age_years'] < 18 else 0
        rec['education'] = rng.choice(
            ['none', 'primary', 'secondary', 'tertiary'],
            p=[0.20, 0.35, 0.35, 0.10])
        rec['urban'] = 1 if rng.random() < 0.40 else 0

        # ── 2. Risk factors ──
        rec['tobacco_use'] = 0
        if rec['age_years'] >= 15:
            rec['tobacco_use'] = 1 if rng.random() < 0.12 else 0
        rec['sugary_diet'] = 1 if rng.random() < 0.55 else 0
        rec['fluoride_toothpaste'] = 1 if rng.random() < (0.50 if rec['urban'] else 0.20) else 0
        rec['brushing_frequency'] = rng.choice(
            ['never', 'occasional', 'once_daily', 'twice_daily'],
            p=[0.15, 0.25, 0.40, 0.20])
        rec['hiv_positive'] = 1 if rng.random() < 0.06 else 0
        rec['diabetes'] = 0
        if rec['age_years'] >= 30:
            rec['diabetes'] = 1 if rng.random() < 0.08 else 0
        rec['malnutrition'] = 0
        if rec['child']:
            rec['malnutrition'] = 1 if rng.random() < 0.15 else 0

        # ── 3. Dental caries [2][3] ──
        caries_prob = 0.40
        if rec['sugary_diet']:
            caries_prob += 0.15
        if rec['brushing_frequency'] in ('never', 'occasional'):
            caries_prob += 0.10
        if not rec['fluoride_toothpaste']:
            caries_prob += 0.05
        rec['dental_caries'] = 1 if rng.random() < min(caries_prob, 0.80) else 0

        rec['dmft_score'] = 0
        if rec['dental_caries']:
            if rec['child']:
                rec['dmft_score'] = max(0, min(20, int(rng.exponential(3))))
            else:
                rec['dmft_score'] = max(0, min(32, int(rng.exponential(5))))

        rec['untreated_caries'] = 0
        if rec['dental_caries']:
            rec['untreated_caries'] = 1 if rng.random() < 0.80 else 0

        # ── 4. Periodontal disease ──
        rec['periodontal_disease'] = 0
        if rec['age_years'] >= 15:
            perio_prob = 0.20
            if rec['tobacco_use']:
                perio_prob *= 1.5
            if rec['diabetes']:
                perio_prob *= 1.5
            if rec['hiv_positive']:
                perio_prob *= 1.3
            rec['periodontal_disease'] = 1 if rng.random() < min(perio_prob, 0.60) else 0

        rec['periodontal_severity'] = 'none'
        if rec['periodontal_disease']:
            rec['periodontal_severity'] = rng.choice(
                ['mild', 'moderate', 'severe'],
                p=[0.30, 0.45, 0.25])

        rec['tooth_loss'] = 0
        if rec['periodontal_severity'] == 'severe' or rec['dmft_score'] > 8:
            rec['tooth_loss'] = max(0, min(20, int(rng.exponential(3))))

        # ── 5. Other conditions [1][5] ──
        rec['oral_cancer'] = 0
        if rec['age_years'] >= 40:
            oc_prob = 0.005
            if rec['tobacco_use']:
                oc_prob *= 3
            rec['oral_cancer'] = 1 if rng.random() < oc_prob else 0

        rec['noma'] = 0
        if rec['child'] and rec['malnutrition']:
            rec['noma'] = 1 if rng.random() < 0.005 else 0

        rec['oral_hiv_manifestation'] = 0
        if rec['hiv_positive']:
            rec['oral_hiv_manifestation'] = 1 if rng.random() < 0.30 else 0

        rec['cleft_lip_palate'] = 0
        if rec['age_years'] < 10:
            rec['cleft_lip_palate'] = 1 if rng.random() < 0.002 else 0

        rec['dental_trauma'] = 0
        if rec['age_years'] < 18:
            rec['dental_trauma'] = 1 if rng.random() < 0.05 else 0

        rec['dental_abscess'] = 0
        if rec['untreated_caries']:
            rec['dental_abscess'] = 1 if rng.random() < 0.10 else 0

        rec['dental_pain'] = 0
        if rec['dental_caries'] or rec['periodontal_disease'] or rec['dental_abscess']:
            rec['dental_pain'] = 1 if rng.random() < 0.60 else 0

        # ── 6. Treatment access [4][7] ──
        rec['sought_dental_care'] = 0
        if rec['dental_pain'] or rec['dental_abscess']:
            rec['sought_dental_care'] = 1 if rng.random() < (0.50 * sc['treatment_mod'] + 0.10) else 0

        rec['treatment_received'] = 'none'
        if rec['sought_dental_care']:
            if sc['restorative_available']:
                rec['treatment_received'] = rng.choice(
                    ['extraction', 'filling', 'scaling', 'antibiotics', 'pain_relief'],
                    p=[0.35, 0.25, 0.10, 0.15, 0.15])
            elif sc['dentist_available']:
                rec['treatment_received'] = rng.choice(
                    ['extraction', 'antibiotics', 'pain_relief'],
                    p=[0.50, 0.25, 0.25])
            else:
                rec['treatment_received'] = rng.choice(
                    ['pain_relief', 'traditional_remedy', 'referral'],
                    p=[0.40, 0.35, 0.25])

        rec['barrier_to_care'] = 'none'
        if not rec['sought_dental_care'] and rec['dental_pain']:
            rec['barrier_to_care'] = rng.choice(
                ['cost', 'distance', 'no_dentist', 'fear',
                 'not_severe_enough', 'traditional_preference'],
                p=[0.25, 0.20, 0.20, 0.15, 0.10, 0.10])

        rec['fluoride_varnish'] = 0
        if rec['child'] and sc['fluoride_programme']:
            rec['fluoride_varnish'] = 1 if rng.random() < 0.20 else 0

        rec['oral_health_education'] = 0
        if rec['sought_dental_care'] and sc['dentist_available']:
            rec['oral_health_education'] = 1 if rng.random() < 0.30 else 0

        # ── 7. Outcome ──
        rec['pain_resolved'] = 0
        if rec['treatment_received'] not in ('none', 'referral'):
            rec['pain_resolved'] = 1 if rng.random() < 0.70 else 0

        rec['complication'] = 0
        if rec['dental_abscess'] and rec['treatment_received'] == 'none':
            rec['complication'] = 1 if rng.random() < 0.15 else 0

        rec['noma_disfigurement'] = 0
        if rec['noma']:
            rec['noma_disfigurement'] = 1 if rng.random() < 0.80 else 0

        records.append(rec)

    df = pd.DataFrame(records)

    print(f"\n{'='*65}")
    print(f"Oral Health — {scenario} (n={n}, seed={seed})")
    print(f"{'='*65}")
    print(f"\n  Dental caries: {df['dental_caries'].mean()*100:.1f}%")
    print(f"  Untreated: {df['untreated_caries'].mean()*100:.1f}%")
    print(f"  Periodontal: {df['periodontal_disease'].mean()*100:.1f}%")
    print(f"  Dental pain: {df['dental_pain'].mean()*100:.1f}%")
    print(f"  Sought care: {df['sought_dental_care'].mean()*100:.1f}%")
    print(f"  Mean DMFT: {df[df['dental_caries']==1]['dmft_score'].mean():.1f}")

    return df


if __name__ == '__main__':
    parser = argparse.ArgumentParser(
        description='Generate oral health dataset')
    parser.add_argument('--scenario', type=str, default='district_hospital',
                        choices=list(SCENARIOS.keys()))
    parser.add_argument('--n', type=int, default=10000)
    parser.add_argument('--seed', type=int, default=42)
    parser.add_argument('--output', type=str, default=None)
    parser.add_argument('--all-scenarios', action='store_true')
    args = parser.parse_args()

    os.makedirs('data', exist_ok=True)

    if args.all_scenarios:
        for sc_name in SCENARIOS:
            df = generate_dataset(n=args.n, seed=args.seed, scenario=sc_name)
            out = os.path.join('data', f'oral_{sc_name}.csv')
            df.to_csv(out, index=False)
            print(f"  -> Saved to {out}\n")
    else:
        df = generate_dataset(n=args.n, seed=args.seed, scenario=args.scenario)
        out = args.output or os.path.join('data', f'oral_{args.scenario}.csv')
        df.to_csv(out, index=False)
        print(f"  -> Saved to {out}")