"""
Prostate Cancer - Sub-Saharan Africa Synthetic Dataset
Author: Electric Sheep Africa
"""

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

np.random.default_rng(42)

AGE_DIST = {'40-49': 0.04, '50-59': 0.18, '60-69': 0.35, '70-79': 0.32, '80+': 0.11}
STAGE = {'Localized': 0.38, 'Locally advanced': 0.35, 'Metastatic': 0.27}
GLEASON = {'Gleason 6': 0.12, 'Gleason 3+4': 0.22, 'Gleason 4+3': 0.20, 'Gleason 8': 0.18, 'Gleason 9-10': 0.22, 'Unknown': 0.06}
PSA_MEAN, PSA_SD = 45, 38  # Higher at diagnosis in SSA
TREATMENT = {'Radical prostatectomy': 0.18, 'EBRT': 0.25, 'ADT only': 0.28, 'Combined': 0.15, 'Palliative': 0.10, 'No treatment': 0.04}
VITAL = {'Alive': 0.55, 'Dead': 0.38, 'Lost': 0.07}
PSA_SCREENED = {'Yes': 0.12, 'No': 0.78, 'Unknown': 0.10}
COUNTRIES = {'Nigeria': 0.24, 'South Africa': 0.20, 'Kenya': 0.16, 'Ghana': 0.12, 'Uganda': 0.10, 'Ethiopia': 0.10, 'Tanzania': 0.08}
YEAR = {y: 0.067 for y in range(2015, 2030)}

def sc(p, rng): a=np.array(list(p.values())); return rng.choice(list(p.keys()), p=a/a.sum())

def gen(n=4000, seed=42):
    rng=np.random.default_rng(seed)
    recs=[]
    for i in range(n):
        psa = max(0.1, rng.lognormal(np.log(PSA_MEAN), 0.8))
        recs.append({'patient_id': f'PC-{sc(COUNTRIES,rng)[:3].upper()}-{sc(YEAR,rng)}-{i+1:05d}',
            'country': sc(COUNTRIES,rng), 'year': sc(YEAR,rng), 'age_group': sc(AGE_DIST,rng),
            'psa_diagnosis_ngml': round(psa, 1), 'stage': sc(STAGE,rng), 'gleason_group': sc(GLEASON,rng),
            'psa_screened': sc(PSA_SCREENED,rng), 'treatment_primary': sc(TREATMENT,rng),
            'vital_status_5yr': sc(VITAL,rng), 'survival_months': round(rng.exponential(36 if sc(VITAL,rng)=='Dead' else 54),1)})
    return pd.DataFrame(recs)

if __name__=="__main__":
    p=argparse.ArgumentParser(); p.add_argument('--n',type=int,default=4000); p.add_argument('--output',type=str,default='data')
    a=p.parse_args(); os.makedirs(a.output,exist_ok=True)
    for sn,m,s in [('low_burden',0.8,42),('moderate_burden',1.0,43),('high_burden',1.2,44)]:
        d=gen(int(a.n*m),s); d['scenario']=sn
        d.to_csv(os.path.join(a.output,f'prostate_cancer_ssa_{sn}.csv'),index=False)
        print(f"Saved: prostate_cancer_ssa_{sn}.csv")