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

# Load CGM data
cgm = []
with open('/home/ben/downloads/Blood Glucose-2026-02-19-2026-03-17.csv') as f:
    reader = csv.DictReader(f)
    for row in reader:
        dt = datetime.strptime(row['Date/Time'], '%Y-%m-%d %H:%M:%S')
        bg = int(row['Blood Glucose (mg/dL)'])
        cgm.append((dt, bg))

cgm.sort(key=lambda x: x[0])
print(f"CGM data: {cgm[0][0]} to {cgm[-1][0]}, {len(cgm)} readings")

# Group by date
daily = defaultdict(list)
for dt, bg in cgm:
    daily[dt.strftime('%Y-%m-%d')].append((dt, bg))

# Parse meal log
meals_raw = """
# March 16
12:55 White bread toast, scrambled eggs, salmon, macadamia nuts
13:40 Greek yogurt with frozen blueberries
17:10 Cheese, meat sticks, olipop
18:25 Spaghetti with shrimp and cream sauce, roasted broccoli and mushrooms

# March 15
8:48 Scrambled eggs on toast with coffee
14:25 Apple, peanut butter, cheddar cheese sticks, salmon and cream cheese toast, lapsang tea
18:10 Oat roll, bud light, mamaliga, shrimp, green beans, hash brown casserole, chocolate cake

# March 14
7:55 black coffee and colac cu nuca
8:35 sunny side up eggs
11:40 peanut butter, strawberry jam, rice cake, raw milk
14:40 smoked salmon and cream cheese toast
18:10 tacos with avocado, carrots, and cucumber
18:31 peach and strawberry cobbler

# March 13
13:10 Greek-style shrimp salad platter
16:10 Protein bar, macadamia nuts, olipop soda
18:20 Baked chicken, macaroni and cheese, green beans, cornbread, mashed potatoes

# March 12
8:00 Eggs on pita bread
11:15 Figs
18:30 Pork pizzolli, roasted veggies, pita bread, girl scout cookies

# March 11
8:25 Vegetable quiche slices and black coffee
12:55 Greek yogurt with protein powder, blueberries and coconut granola
17:55 Beef stir fry with vegetables and rice
20:10 Homemade chocolate pudding

# March 10
8:40 scrambled eggs on toast with pastry and black coffee
13:30 greek yogurt with protein powder
18:10 beef gyro salad with pita and hummus
21:35 glass of raw milk

# March 9
13:31 decaf coffee with heavy cream
17:15 coconut protein bar and carnivore bar
17:55 dried figs and popcorn chips
18:24 spaghetti with chicken and vegetables

# March 8
13:09 tea with honey, bread roll, mixed salad, a few bites of birthday cake
15:05 ham and cheese tortillas with apple and peanut butter
18:07 sirloin steak with salad, vegetables, bread, and beer

# March 7
9:48 whole wheat breakfast burritos
11:34 pancakes with sausage and syrup
15:17 Peanut butter and jelly sandwich, olipop
18:32 Ground beef, roasted sweet potato and avocado plate

# March 6
16:23 Croissant, chicken broth and poached eggs
17:55 Bacon cheeseburger with salad and porter beer
19:15 Vanilla ice cream with cherries and chocolate pieces

# March 3
11:30 meatball soup
12:30 Greek yogurt with protein powder

# March 2
14:30 Beef butter soup and poached egg
15:30 cheese slices
16:00 greek yogurt with protein powder
18:30 dried figs and watermelon

# March 1
16:07 Beef steak with green beans and pinto beans
18:18 beef patty, sauteed vegetables, tilapia, avocado slices
19:17 dark chocolate bar and milk

# Feb 28
16:09 Butter soup with poached egg and beef patty
18:15 Beef with vegetables and cream sauce
"""

meals = []
current_date = None
for line in meals_raw.strip().split('\n'):
    line = line.strip()
    if not line:
        continue
    if line.startswith('# '):
        date_str = line[2:]
        # Parse date
        date_map = {
            'March 16': '2026-03-16', 'March 15': '2026-03-15', 'March 14': '2026-03-14',
            'March 13': '2026-03-13', 'March 12': '2026-03-12', 'March 11': '2026-03-11',
            'March 10': '2026-03-10', 'March 9': '2026-03-09', 'March 8': '2026-03-08',
            'March 7': '2026-03-07', 'March 6': '2026-03-06', 'March 3': '2026-03-03',
            'March 2': '2026-03-02', 'March 1': '2026-03-01', 'Feb 28': '2026-02-28',
        }
        current_date = date_map.get(date_str)
    else:
        # Parse time and meal
        parts = line.split(' ', 1)
        if len(parts) == 2 and ':' in parts[0]:
            time_str = parts[0]
            meal_desc = parts[1]
            try:
                dt = datetime.strptime(f"{current_date} {time_str}", '%Y-%m-%d %H:%M')
                meals.append((dt, meal_desc))
            except:
                pass

meals.sort(key=lambda x: x[0])
print(f"\nMeal log: {len(meals)} meals from {meals[0][0].date()} to {meals[-1][0].date()}")

# For each meal, find the glucose at meal time and the peak in the next 2 hours
print("\n" + "="*100)
print("MEAL-BY-MEAL GLUCOSE RESPONSE")
print("="*100)

meal_responses = []
for meal_dt, meal_desc in meals:
    date_str = meal_dt.strftime('%Y-%m-%d')
    if date_str not in daily:
        print(f"\n{meal_dt.strftime('%b %d %H:%M')} — {meal_desc}")
        print(f"  ⚠️  No CGM data for this date")
        continue
    
    day_data = daily[date_str]
    # Also check next day for late meals
    next_date = (meal_dt + timedelta(days=1)).strftime('%Y-%m-%d')
    if next_date in daily:
        day_data = day_data + daily[next_date]
    
    # Find glucose at meal time (closest reading within 10 min)
    baseline_bg = None
    min_diff = timedelta(minutes=15)
    for dt, bg in day_data:
        diff = abs(dt - meal_dt)
        if diff < min_diff:
            min_diff = diff
            baseline_bg = bg
    
    # Find peak in next 2 hours
    peak_bg = None
    peak_time = None
    for dt, bg in day_data:
        if timedelta(0) <= (dt - meal_dt) <= timedelta(hours=2):
            if peak_bg is None or bg > peak_bg:
                peak_bg = bg
                peak_time = dt
    
    # Find nadir in 2-4 hours (reactive hypoglycemia check)
    nadir_bg = None
    nadir_time = None
    for dt, bg in day_data:
        if timedelta(hours=1) <= (dt - meal_dt) <= timedelta(hours=4):
            if nadir_bg is None or bg < nadir_bg:
                nadir_bg = bg
                nadir_time = dt
    
    if baseline_bg and peak_bg:
        delta = peak_bg - baseline_bg
        meal_responses.append({
            'dt': meal_dt,
            'meal': meal_desc,
            'baseline': baseline_bg,
            'peak': peak_bg,
            'delta': delta,
            'peak_time': peak_time,
            'nadir': nadir_bg,
            'nadir_time': nadir_time,
        })
        
        severity = "🔴" if delta > 40 or peak_bg > 160 else "🟡" if delta > 25 or peak_bg > 140 else "🟢"
        peak_min = int((peak_time - meal_dt).total_seconds() / 60) if peak_time else 0
        
        print(f"\n{severity} {meal_dt.strftime('%b %d %H:%M')} — {meal_desc}")
        print(f"   Baseline: {baseline_bg} → Peak: {peak_bg} (+{delta}) at {peak_min}min")
        if nadir_bg and nadir_bg < baseline_bg - 10:
            print(f"   ⚠️  Reactive drop to {nadir_bg} at {nadir_time.strftime('%H:%M')}")
    else:
        print(f"\n{meal_dt.strftime('%b %d %H:%M')} — {meal_desc}")
        print(f"   Baseline: {baseline_bg}, Peak: {peak_bg} (incomplete data)")

# Daily summary stats
print("\n\n" + "="*100)
print("DAILY SUMMARY")
print("="*100)

for date_str in sorted(daily.keys()):
    readings = [bg for dt, bg in daily[date_str]]
    if len(readings) < 10:
        continue
    
    mean = statistics.mean(readings)
    std = statistics.stdev(readings) if len(readings) > 1 else 0
    cv = (std / mean) * 100 if mean > 0 else 0
    tir = sum(1 for bg in readings if 70 <= bg <= 140) / len(readings) * 100
    
    # Overnight (midnight to 6am)
    overnight = [bg for dt, bg in daily[date_str] if dt.hour < 6]
    overnight_mean = statistics.mean(overnight) if overnight else None
    
    # First meal time
    day_meals = [(dt, desc) for dt, desc in meals if dt.strftime('%Y-%m-%d') == date_str]
    first_meal = day_meals[0][0].strftime('%H:%M') if day_meals else "no log"
    last_meal = day_meals[-1][0].strftime('%H:%M') if day_meals else "no log"
    n_meals = len(day_meals)
    eating_window = ""
    if len(day_meals) >= 2:
        window_h = (day_meals[-1][0] - day_meals[0][0]).total_seconds() / 3600
        eating_window = f" ({window_h:.1f}h window)"
    
    dn = datetime.strptime(date_str, '%Y-%m-%d')
    day_name = dn.strftime('%a')
    
    quality = "🟢" if mean < 115 and tir > 90 else "🟡" if mean < 125 and tir > 80 else "🔴"
    
    overnight_str = f", overnight avg {overnight_mean:.0f}" if overnight_mean else ""
    
    print(f"{quality} {date_str} ({day_name}): mean {mean:.0f}, max {max(readings)}, min {min(readings)}, "
          f"CV {cv:.1f}%, TIR {tir:.0f}%{overnight_str}")
    if n_meals:
        print(f"   Meals: {n_meals}x, {first_meal}-{last_meal}{eating_window}")

# Pattern analysis
print("\n\n" + "="*100)
print("PATTERN ANALYSIS: FOOD CATEGORIES")
print("="*100)

# Categorize meals
categories = {
    'Protein-heavy (no carbs)': [],
    'Eggs': [],
    'Greek yogurt / protein': [],
    'Bread/toast/pastry': [],
    'Rice': [],
    'Pasta': [],
    'Fruit/sweet': [],
    'Mixed/balanced': [],
}

for r in meal_responses:
    m = r['meal'].lower()
    if any(w in m for w in ['steak', 'gyro', 'burger', 'beef patty', 'ground beef', 'tilapia', 'chicken', 'pork']) and not any(w in m for w in ['rice', 'pasta', 'spaghetti', 'bread', 'toast', 'bun', 'tortilla', 'macaroni']):
        categories['Protein-heavy (no carbs)'].append(r)
    elif 'egg' in m and not any(w in m for w in ['toast', 'bread', 'pita', 'burrito']):
        categories['Eggs'].append(r)
    elif 'yogurt' in m or 'protein powder' in m or 'protein bar' in m:
        categories['Greek yogurt / protein'].append(r)
    elif any(w in m for w in ['toast', 'bread', 'croissant', 'colac', 'pastry', 'roll', 'cornbread']):
        categories['Bread/toast/pastry'].append(r)
    elif 'rice' in m and 'rice cake' not in m:
        categories['Rice'].append(r)
    elif any(w in m for w in ['spaghetti', 'pasta', 'macaroni']):
        categories['Pasta'].append(r)
    elif any(w in m for w in ['fig', 'fruit', 'apple', 'watermelon', 'cobbler', 'cake', 'ice cream', 'chocolate', 'cookie', 'pudding', 'pancake', 'syrup']):
        categories['Fruit/sweet'].append(r)
    else:
        categories['Mixed/balanced'].append(r)

for cat, items in categories.items():
    if not items:
        continue
    deltas = [r['delta'] for r in items]
    peaks = [r['peak'] for r in items]
    avg_delta = statistics.mean(deltas)
    avg_peak = statistics.mean(peaks)
    max_peak = max(peaks)
    print(f"\n*{cat}* (n={len(items)}):")
    print(f"  Avg delta: +{avg_delta:.0f}, Avg peak: {avg_peak:.0f}, Max peak: {max_peak}")
    for r in sorted(items, key=lambda x: x['delta'], reverse=True):
        print(f"    {r['dt'].strftime('%b %d %H:%M')} {r['meal'][:60]}: +{r['delta']} → {r['peak']}")

# Fasting analysis
print("\n\n" + "="*100)
print("FASTING WINDOW ANALYSIS")
print("="*100)

# For each day with meal data, calculate fasting glucose trend
for date_str in sorted(daily.keys()):
    day_meals = [(dt, desc) for dt, desc in meals if dt.strftime('%Y-%m-%d') == date_str]
    if not day_meals:
        continue
    
    first_meal = day_meals[0][0]
    # Look at glucose from midnight to first meal
    fasting_readings = [(dt, bg) for dt, bg in daily[date_str] if dt < first_meal]
    if len(fasting_readings) < 6:
        continue
    
    # Split into overnight (0-6am) and morning (6am-first meal)
    overnight = [bg for dt, bg in fasting_readings if dt.hour < 6]
    morning = [bg for dt, bg in fasting_readings if dt.hour >= 6]
    
    # Dawn phenomenon: glucose rise between 4-8am
    dawn = [(dt, bg) for dt, bg in fasting_readings if 4 <= dt.hour <= 8]
    
    fasting_hours = first_meal.hour + first_meal.minute/60
    # Approximate: assume last meal previous day
    prev_date = (first_meal - timedelta(days=1)).strftime('%Y-%m-%d')
    prev_meals = [(dt, desc) for dt, desc in meals if dt.strftime('%Y-%m-%d') == prev_date]
    if prev_meals:
        last_prev = prev_meals[-1][0]
        fast_duration = (first_meal - last_prev).total_seconds() / 3600
    else:
        fast_duration = None
    
    pre_meal_bg = fasting_readings[-1][1] if fasting_readings else None
    overnight_mean = statistics.mean(overnight) if overnight else None
    morning_mean = statistics.mean(morning) if morning else None
    
    fast_str = f"{fast_duration:.1f}h fast" if fast_duration else "unknown fast"
    on_str = f"overnight {overnight_mean:.0f}" if overnight_mean else ""
    mn_str = f"morning {morning_mean:.0f}" if morning_mean else ""
    
    print(f"{date_str}: {fast_str}, pre-meal BG {pre_meal_bg}, {on_str}, {mn_str}")

# Overnight trends over the full period
print("\n\n" + "="*100)
print("OVERNIGHT GLUCOSE TREND (4-6AM WINDOW)")
print("="*100)

for date_str in sorted(daily.keys()):
    window = [bg for dt, bg in daily[date_str] if 4 <= dt.hour < 6]
    if len(window) >= 3:
        avg = statistics.mean(window)
        print(f"{date_str}: {avg:.0f}")

print("\n\n" + "="*100)
print("TOP 10 SPIKES AND TOP 10 BEST-CONTROLLED MEALS")
print("="*100)

sorted_by_delta = sorted(meal_responses, key=lambda x: x['delta'], reverse=True)
print("\nWORST SPIKES:")
for i, r in enumerate(sorted_by_delta[:10]):
    print(f"  {i+1}. +{r['delta']} → {r['peak']}: {r['dt'].strftime('%b %d %H:%M')} {r['meal']}")

print("\nBEST CONTROLLED:")
for i, r in enumerate(sorted_by_delta[-10:]):
    print(f"  {i+1}. +{r['delta']} → {r['peak']}: {r['dt'].strftime('%b %d %H:%M')} {r['meal']}")

# Weekly progression
print("\n\n" + "="*100)
print("WEEKLY PROGRESSION")
print("="*100)

weeks = defaultdict(list)
for dt, bg in cgm:
    # ISO week
    week = dt.isocalendar()[1]
    weeks[week].append(bg)

for week in sorted(weeks.keys()):
    readings = weeks[week]
    mean = statistics.mean(readings)
    tir = sum(1 for bg in readings if 70 <= bg <= 140) / len(readings) * 100
    print(f"Week {week}: mean {mean:.0f}, TIR {tir:.0f}%, n={len(readings)}")