import csv
from datetime import datetime, timedelta
from collections import defaultdict

# Load CGM data
def load_cgm(filepath):
    data = {}
    with open(filepath) as f:
        reader = csv.DictReader(f)
        for row in reader:
            dt = datetime.strptime(row['Date/Time'], '%Y-%m-%d %H:%M:%S')
            bg = float(row['Blood Glucose (mg/dL)'])
            data[dt] = bg
    return data

cgm_mar6 = load_cgm('/home/ben/ava/ben/Blood-Glucose-2026-03-06-2026-03-06.csv')
cgm_mar7_14 = load_cgm('/home/ben/downloads/Blood Glucose-2026-03-07-2026-03-14.csv')

cgm = {**cgm_mar6, **cgm_mar7_14}

# Parse meal log
meals_text = open('/home/ben/ava/ben/health/meal-log.md').read()

meals = []
current_date = None
for line in meals_text.strip().split('\n'):
    line = line.strip()
    if line.startswith('# '):
        date_str = line[2:].strip()
        # Parse month and day
        try:
            current_date = datetime.strptime(date_str + ' 2026', '%B %d %Y')
        except:
            try:
                current_date = datetime.strptime(date_str + ' 2026', '%b %d %Y')
            except:
                current_date = None
    elif line and current_date:
        # Try to parse time
        parts = line.split(' ', 1)
        if ':' in parts[0]:
            try:
                time_parts = parts[0].split(':')
                h, m = int(time_parts[0]), int(time_parts[1])
                meal_dt = current_date.replace(hour=h, minute=m)
                food = parts[1] if len(parts) > 1 else ''
                meals.append((meal_dt, food))
            except:
                pass

# For each day with CGM data, compute metrics
print("=" * 80)
print("MEAL LOG + CGM OVERLAY ANALYSIS (Mar 6-14)")
print("=" * 80)

for day_offset in range(6, 15):
    day = datetime(2026, 3, day_offset)
    day_str = day.strftime('%B %d')
    
    # Get CGM readings for this day
    day_readings = [(dt, bg) for dt, bg in sorted(cgm.items()) 
                    if dt.date() == day.date()]
    
    if not day_readings:
        continue
    
    # Get meals for this day
    day_meals = [(dt, food) for dt, food in meals if dt.date() == day.date()]
    
    bgs = [bg for _, bg in day_readings]
    mean_bg = sum(bgs) / len(bgs)
    min_bg = min(bgs)
    max_bg = max(bgs)
    
    # Compute time in range (70-140)
    tir = sum(1 for bg in bgs if 70 <= bg <= 140) / len(bgs) * 100
    
    # Compute time above 140
    above_140 = sum(1 for bg in bgs if bg > 140) / len(bgs) * 100
    
    # First and last meal times
    if day_meals:
        first_meal = min(dt for dt, _ in day_meals)
        last_meal = max(dt for dt, _ in day_meals)
        eating_window = (last_meal - first_meal).total_seconds() / 3600
    else:
        eating_window = None
    
    # Overnight/fasting glucose (midnight to first meal, or 4am-7am)
    fasting_readings = [(dt, bg) for dt, bg in day_readings 
                        if 4 <= dt.hour <= 7]
    fasting_avg = sum(bg for _, bg in fasting_readings) / len(fasting_readings) if fasting_readings else None
    
    print(f"\n{'─' * 60}")
    print(f"  {day_str} (a {day.strftime('%A')})")
    print(f"{'─' * 60}")
    print(f"  CGM: mean={mean_bg:.0f}  min={min_bg:.0f}  max={max_bg:.0f}  TIR={tir:.0f}%  >140={above_140:.0f}%")
    if fasting_avg:
        print(f"  Fasting avg (4-7am): {fasting_avg:.0f}")
    
    if day_meals:
        print(f"  Eating window: {eating_window:.1f}h ({first_meal.strftime('%H:%M')} → {last_meal.strftime('%H:%M')})")
        # Estimate fasting hours since previous day's last meal
        prev_day = day - timedelta(days=1)
        prev_meals = [(dt, food) for dt, food in meals if dt.date() == prev_day.date()]
        if prev_meals:
            prev_last = max(dt for dt, _ in prev_meals)
            fast_hours = (first_meal - prev_last).total_seconds() / 3600
            print(f"  Fast before first meal: {fast_hours:.1f}h (since {prev_last.strftime('%H:%M')} prev day)")
        
        print(f"  Meals:")
        for dt, food in sorted(day_meals):
            # Find peak glucose within 2h of meal
            meal_readings = [(rdt, bg) for rdt, bg in day_readings 
                            if dt <= rdt <= dt + timedelta(hours=2)]
            if meal_readings:
                peak_bg = max(bg for _, bg in meal_readings)
                peak_time = max(meal_readings, key=lambda x: x[1])[0]
                # Pre-meal glucose (closest reading before meal)
                pre_readings = [(rdt, bg) for rdt, bg in day_readings 
                               if rdt <= dt and (dt - rdt).total_seconds() < 900]
                pre_bg = pre_readings[-1][1] if pre_readings else None
                if pre_bg:
                    delta = peak_bg - pre_bg
                    print(f"    {dt.strftime('%H:%M')} {food[:60]}")
                    print(f"           pre={pre_bg:.0f} → peak={peak_bg:.0f} (+{delta:.0f}) at {peak_time.strftime('%H:%M')}")
                else:
                    print(f"    {dt.strftime('%H:%M')} {food[:60]}")
                    print(f"           peak={peak_bg:.0f} at {peak_time.strftime('%H:%M')}")
            else:
                print(f"    {dt.strftime('%H:%M')} {food[:60]}")
    else:
        print(f"  No meals logged")

# Now compute cross-day patterns
print(f"\n{'=' * 80}")
print("CROSS-DAY PATTERNS")
print("=" * 80)

# Collect per-day summaries
day_summaries = []
for day_offset in range(6, 15):
    day = datetime(2026, 3, day_offset)
    day_readings = [(dt, bg) for dt, bg in sorted(cgm.items()) 
                    if dt.date() == day.date()]
    if not day_readings:
        continue
    
    day_meals = [(dt, food) for dt, food in meals if dt.date() == day.date()]
    bgs = [bg for _, bg in day_readings]
    
    fasting_readings = [(dt, bg) for dt, bg in day_readings if 4 <= dt.hour <= 7]
    fasting_avg = sum(bg for _, bg in fasting_readings) / len(fasting_readings) if fasting_readings else None
    
    if day_meals:
        first_meal = min(dt for dt, _ in day_meals)
        last_meal = max(dt for dt, _ in day_meals)
        eating_window = (last_meal - first_meal).total_seconds() / 3600
        num_meals = len(day_meals)
    else:
        eating_window = None
        num_meals = 0
        first_meal = None
    
    # Estimate carb-heaviness
    carb_heavy_words = ['bread', 'toast', 'pancake', 'syrup', 'rice', 'pasta', 'spaghetti', 
                         'potato', 'sweet potato', 'croissant', 'cake', 'cookie', 'ice cream',
                         'pita', 'burrito', 'tortilla', 'honey', 'chocolate', 'cornbread',
                         'macaroni', 'mamaliga', 'cobbler', 'pudding', 'fig', 'popcorn',
                         'hash brown', 'roll', 'granola']
    
    all_foods = ' '.join(food.lower() for _, food in day_meals)
    carb_items = sum(1 for word in carb_heavy_words if word in all_foods)
    
    # Previous day's last meal for fasting calc
    prev_day = day - timedelta(days=1)
    prev_meals = [(dt, food) for dt, food in meals if dt.date() == prev_day.date()]
    if prev_meals and first_meal:
        prev_last = max(dt for dt, _ in prev_meals)
        overnight_fast = (first_meal - prev_last).total_seconds() / 3600
    else:
        overnight_fast = None
    
    day_summaries.append({
        'date': day,
        'mean': sum(bgs)/len(bgs),
        'max': max(bgs),
        'min': min(bgs),
        'tir': sum(1 for bg in bgs if 70 <= bg <= 140) / len(bgs) * 100,
        'above140': sum(1 for bg in bgs if bg > 140) / len(bgs) * 100,
        'fasting_avg': fasting_avg,
        'eating_window': eating_window,
        'num_meals': num_meals,
        'overnight_fast': overnight_fast,
        'carb_items': carb_items,
    })

# Print correlation-style analysis
print("\nDay-by-day summary:")
print(f"{'Date':>8} {'Mean':>5} {'Max':>5} {'TIR%':>5} {'>140%':>5} {'Fast':>5} {'EatWin':>6} {'#Meals':>6} {'Carbs':>5} {'OvnFast':>7}")
for s in day_summaries:
    fast_str = f"{s['fasting_avg']:.0f}" if s['fasting_avg'] else "  -"
    ew_str = f"{s['eating_window']:.1f}" if s['eating_window'] else "  -"
    of_str = f"{s['overnight_fast']:.1f}" if s['overnight_fast'] else "  -"
    print(f"  Mar {s['date'].day:>2} {s['mean']:>5.0f} {s['max']:>5.0f} {s['tir']:>5.0f} {s['above140']:>5.0f} {fast_str:>5} {ew_str:>6} {s['num_meals']:>6} {s['carb_items']:>5} {of_str:>7}")

# Correlations (simple)
print("\nKey patterns:")

# Overnight fast vs fasting glucose
with_fast = [(s['overnight_fast'], s['fasting_avg']) for s in day_summaries 
             if s['overnight_fast'] and s['fasting_avg']]
if with_fast:
    print(f"\n  Overnight fast length vs fasting glucose ({len(with_fast)} days):")
    for f, g in sorted(with_fast):
        print(f"    {f:.1f}h fast → {g:.0f} mg/dL fasting glucose")

# Eating window vs daily mean
with_ew = [(s['eating_window'], s['mean']) for s in day_summaries if s['eating_window']]
if with_ew:
    print(f"\n  Eating window vs daily mean ({len(with_ew)} days):")
    for ew, m in sorted(with_ew):
        print(f"    {ew:.1f}h window → {m:.0f} mean")

# Carb count vs daily max
with_carb = [(s['carb_items'], s['max'], s['date'].day) for s in day_summaries]
if with_carb:
    print(f"\n  Carb item count vs daily max:")
    for c, mx, d in sorted(with_carb, key=lambda x: x[0]):
        print(f"    {c} carb items (Mar {d}) → max {mx:.0f}")

# Number of meals vs mean and TIR
print(f"\n  # Meals vs TIR and mean:")
for s in sorted(day_summaries, key=lambda x: x['num_meals']):
    print(f"    {s['num_meals']} meals (Mar {s['date'].day:>2}) → mean {s['mean']:.0f}, TIR {s['tir']:.0f}%, max {s['max']:.0f}")