#\!/usr/bin/env python3
import random, math, csv
from typing import Dict, Optional, List

class HypertensionCVDGenerator:
    def __init__(self, seed=None):
        if seed: random.seed(seed)
    
    def normal_random(self, m, s):
        u1, u2 = random.random(), random.random()
        return m + s * math.sqrt(-2*math.log(u1)) * math.cos(2*math.pi*u2)
    
    def truncated_normal(self, m, s, mn, mx):
        return max(mn, min(mx, self.normal_random(m, s)))
    
    def generate_sample(self, sid, htn_prev=0.30):
        age = self.truncated_normal(48, 15, 18, 85)
        sex = random.choice(['Male', 'Female'])
        residence = random.choices(['Urban', 'Rural'], [0.45, 0.55])[0]
        
        # BMI
        bmi = self.truncated_normal(25.5 if sex=='Female' else 23.5, 4.5, 16, 45)
        if residence == 'Urban': bmi += 1.5
        bmi = round(bmi, 1)
        
        # Risk factors
        family_hx_htn = random.random() < 0.30
        diabetes = random.random() < (0.10 if age < 45 else 0.18)
        smoking = random.random() < (0.25 if sex=='Male' else 0.05)
        alcohol_heavy = random.random() < (0.15 if sex=='Male' else 0.03)
        physically_active = random.random() < (0.35 if residence=='Urban' else 0.50)
        high_salt_diet = random.random() < 0.60
        
        # ORs
        bmi_or = 3.50 if bmi > 30 else (1.8 if bmi > 25 else 1.0)
        age_or = 4.20 if age > 60 else (2.5 if age > 45 else 1.0)
        fh_or = 2.80 if family_hx_htn else 1.0
        salt_or = 2.40 if high_salt_diet else 1.0
        dm_or = 2.30 if diabetes else 1.0
        inactive_or = 1.80 if not physically_active else 1.0
        alc_or = 2.10 if alcohol_heavy else 1.0
        urban_or = 1.95 if residence=='Urban' else 1.0
        
        combined_or = bmi_or * age_or * fh_or * salt_or * dm_or * inactive_or * alc_or * urban_or
        htn_prob = min(0.90, 0.15 * combined_or)
        has_htn = random.random() < htn_prob
        
        # BP
        if has_htn:
            systolic = self.truncated_normal(152, 18, 130, 200)
            diastolic = self.truncated_normal(95, 12, 80, 120)
            if systolic < 140 and diastolic < 90:  # Ensure diagnosed criteria
                systolic += 10
            diagnosed = random.random() < (0.30 if residence=='Urban' else 0.20)
            if diagnosed:
                on_meds = random.random() < 0.40
                adherence = random.choices(['Good','Moderate','Poor'], [0.35,0.45,0.20])[0] if on_meds else 'N/A'
                if on_meds and adherence=='Good':
                    systolic -= 15; diastolic -= 10
                    controlled = systolic < 140 and diastolic < 90
                else:
                    controlled = False
            else:
                on_meds = False
                adherence = 'Undiagnosed'
                controlled = False
        else:
            systolic = self.truncated_normal(118, 12, 90, 129)
            diastolic = self.truncated_normal(75, 8, 60, 84)
            diagnosed, on_meds, adherence, controlled = False, False, 'N/A', False
        
        # CVD events
        if has_htn:
            stroke_risk = 0.02 * (3.80 if not controlled else 1.5) * (2.0 if diabetes else 1.0)
            mi_risk = 0.015 * (2.50 if not controlled else 1.2) * (2.5 if smoking else 1.0)
            hf_risk = 0.01 * (3.20 if not controlled else 1.3)
        else:
            stroke_risk = 0.005 * (2.0 if diabetes else 1.0)
            mi_risk = 0.003 * (2.5 if smoking else 1.0)
            hf_risk = 0.002
        
        stroke = random.random() < min(0.25, stroke_risk * (age/50))
        mi = random.random() < min(0.20, mi_risk * (age/50))
        heart_failure = random.random() < min(0.15, hf_risk * (age/50))
        
        # Labs
        chol = self.truncated_normal(210 if has_htn else 180, 40, 120, 350)
        ldl = self.truncated_normal(140 if has_htn else 115, 35, 60, 250)
        hdl = self.truncated_normal(46 if has_htn else 52, 12, 25, 80)
        creat = self.truncated_normal(1.2 if has_htn else 0.9, 0.4 if has_htn else 0.2, 0.5, 4.0)
        
        return {
            'patient_id': f'HTN_{sid:06d}', 'age': round(age,1), 'sex': sex, 'residence': residence,
            'bmi': bmi, 'family_history_hypertension': family_hx_htn, 'diabetes': diabetes,
            'smoking': smoking, 'alcohol_heavy': alcohol_heavy, 'physically_active': physically_active,
            'high_salt_diet': high_salt_diet, 'hypertension_status': 'Hypertensive' if has_htn else 'Normal',
            'systolic_bp_mmhg': round(systolic,0), 'diastolic_bp_mmhg': round(diastolic,0),
            'diagnosed': diagnosed, 'on_medication': on_meds, 'medication_adherence': adherence,
            'bp_controlled': controlled, 'stroke_history': stroke, 'myocardial_infarction': mi,
            'heart_failure': heart_failure, 'total_cholesterol_mg_dl': round(chol,0),
            'ldl_mg_dl': round(ldl,0), 'hdl_mg_dl': round(hdl,0), 'creatinine_mg_dl': round(creat,2)
        }
    
    def generate_dataset(self, n, prev=0.30, output=None):
        print(f"Generating {n} HTN/CVD samples (prevalence={prev:.1%})...")
        samples = [self.generate_sample(i+1, prev) for i in range(n)]
        if output:
            with open(output, 'w', newline='') as f:
                writer = csv.DictWriter(f, fieldnames=list(samples[0].keys()))
                writer.writeheader(); writer.writerows(samples)
            print(f"✓ Saved to {output}")
        htn = sum(1 for s in samples if s['hypertension_status']=='Hypertensive')
        diagnosed = sum(1 for s in samples if s['diagnosed'])
        cvd = sum(1 for s in samples if s['stroke_history'] or s['myocardial_infarction'])
        print(f"Summary: HTN {htn} ({htn/n*100:.1f}%), Diagnosed {diagnosed} ({diagnosed/max(1,htn)*100:.1f}%), CVD events {cvd}")
        return samples

if __name__=="__main__":
    import argparse
    p = argparse.ArgumentParser()
    p.add_argument('-n', '--samples', type=int, default=1000)
    p.add_argument('-p', '--prevalence', type=float, default=0.30)
    p.add_argument('-s', '--seed', type=int, default=None)
    p.add_argument('-o', '--output', type=str, default='hypertension_cvd_africa.csv')
    args = p.parse_args()
    HypertensionCVDGenerator(args.seed).generate_dataset(args.samples, args.prevalence, args.output)