#!/usr/bin/env python3
"""
Generate realistic one-year bakery sales data for AI model training
Creates daily sales data with proper patterns, seasonality, and realistic variations
Pure Python - no external dependencies
"""

import csv
import random
from datetime import datetime, timedelta
from math import sqrt

# Set random seed for reproducibility
random.seed(42)

# Products with base quantities and prices
PRODUCTS = {
    'pan': {'base_qty': 200, 'price': 1.20, 'weekend_factor': 0.85, 'holiday_factor': 1.30},
    'croissant': {'base_qty': 110, 'price': 1.50, 'weekend_factor': 1.20, 'holiday_factor': 1.25},
    'napolitana': {'base_qty': 75, 'price': 1.80, 'weekend_factor': 1.15, 'holiday_factor': 1.20},
    'palmera': {'base_qty': 50, 'price': 1.60, 'weekend_factor': 1.25, 'holiday_factor': 1.15},
    'cafe': {'base_qty': 280, 'price': 1.40, 'weekend_factor': 0.75, 'holiday_factor': 0.90}
}

# Spanish holidays in 2025
HOLIDAYS = [
    '2025-01-01',  # Año Nuevo
    '2025-01-06',  # Reyes
    '2025-04-18',  # Viernes Santo
    '2025-05-01',  # Día del Trabajo
    '2025-08-15',  # Asunción
    '2025-10-12',  # Fiesta Nacional
    '2025-11-01',  # Todos los Santos
    '2025-12-06',  # Constitución
    '2025-12-08',  # Inmaculada
    '2025-12-25',  # Navidad
]

def random_normal(mean=0, std=1):
    """Generate random number from normal distribution using Box-Muller transform"""
    u1 = random.random()
    u2 = random.random()
    z0 = sqrt(-2.0 * 0.693147 * u1) * (2.0 * 3.14159 * u2)**0.5  # Simplified
    return mean + z0 * std

def get_temperature(date):
    """Get realistic temperature for Madrid based on month"""
    month = date.month
    base_temps = {
        1: 8, 2: 10, 3: 13, 4: 16, 5: 20, 6: 26,
        7: 30, 8: 30, 9: 25, 10: 18, 11: 12, 12: 9
    }
    base = base_temps[month]
    variation = random.uniform(-4, 4)
    return round(max(0, base + variation), 1)

def get_precipitation(date, temperature):
    """Get precipitation (mm) - more likely in cooler months"""
    month = date.month
    # Higher chance of rain in winter/spring
    rain_probability = {
        1: 0.25, 2: 0.25, 3: 0.20, 4: 0.25, 5: 0.20, 6: 0.10,
        7: 0.05, 8: 0.05, 9: 0.15, 10: 0.20, 11: 0.25, 12: 0.25
    }

    if random.random() < rain_probability[month]:
        # Rain amount in mm
        return round(random.uniform(2, 25), 1)
    return 0

def calculate_quantity(product_name, product_info, date, is_weekend, is_holiday, temperature, precipitation):
    """Calculate realistic quantity sold with various factors"""
    base = product_info['base_qty']

    # Weekend adjustment
    if is_weekend:
        base *= product_info['weekend_factor']

    # Holiday adjustment
    if is_holiday:
        base *= product_info['holiday_factor']

    # Seasonal adjustment
    month = date.month
    if month in [12, 1]:  # Christmas/New Year boost
        base *= 1.15
    elif month in [7, 8]:  # Summer vacation dip
        base *= 0.90
    elif month in [4, 5, 9, 10]:  # Spring/Fall moderate
        base *= 1.05

    # Temperature effect
    if product_name == 'cafe':
        # More coffee when cold
        if temperature < 12:
            base *= 1.15
        elif temperature > 28:
            base *= 0.85
    else:
        # Pastries sell better in moderate weather
        if 15 <= temperature <= 25:
            base *= 1.05
        elif temperature > 30:
            base *= 0.90

    # Precipitation effect (rainy days reduce sales slightly)
    if precipitation > 5:
        base *= 0.85
    elif precipitation > 15:
        base *= 0.75

    # Day of week pattern (Mon-Sun)
    day_of_week = date.weekday()
    day_factors = [0.95, 1.00, 1.05, 1.00, 1.10, 1.15, 1.05]  # Mon to Sun
    base *= day_factors[day_of_week]

    # Add random variation (±15%)
    variation = random.uniform(0.85, 1.15)
    quantity = int(base * variation)

    # Ensure minimum sales
    min_qty = {
        'pan': 80, 'croissant': 40, 'napolitana': 30,
        'palmera': 20, 'cafe': 100
    }
    quantity = max(min_qty[product_name], quantity)

    # Add occasional low-sales days (5% chance)
    if random.random() < 0.05:
        quantity = int(quantity * random.uniform(0.3, 0.6))

    return quantity

def generate_dataset():
    """Generate complete one-year bakery sales dataset"""
    start_date = datetime(2024, 9, 1)
    end_date = datetime(2025, 9, 1)

    records = []
    current_date = start_date

    print("Generating one year of bakery sales data...")
    print(f"Date range: {start_date.date()} to {end_date.date()}")
    print(f"Products: {list(PRODUCTS.keys())}")

    # Statistics tracking
    product_stats = {p: {'total': 0, 'min': float('inf'), 'max': 0, 'count': 0, 'zeros': 0}
                     for p in PRODUCTS.keys()}

    while current_date <= end_date:
        # Date properties
        is_weekend = current_date.weekday() >= 5  # Saturday=5, Sunday=6
        is_holiday = current_date.strftime('%Y-%m-%d') in HOLIDAYS

        # Environmental factors
        temperature = get_temperature(current_date)
        precipitation = get_precipitation(current_date, temperature)

        # Generate sales for each product
        for product_name, product_info in PRODUCTS.items():
            quantity = calculate_quantity(
                product_name, product_info, current_date,
                is_weekend, is_holiday, temperature, precipitation
            )

            revenue = round(quantity * product_info['price'], 2)

            records.append({
                'date': current_date.strftime('%Y-%m-%d'),
                'product_name': product_name,
                'quantity_sold': quantity,
                'revenue': revenue
            })

            # Update statistics
            stats = product_stats[product_name]
            stats['total'] += quantity
            stats['min'] = min(stats['min'], quantity)
            stats['max'] = max(stats['max'], quantity)
            stats['count'] += 1
            if quantity == 0:
                stats['zeros'] += 1

        current_date += timedelta(days=1)

    # Calculate days
    total_days = (end_date - start_date).days + 1

    # Print statistics
    print(f"\nDataset generated successfully!")
    print(f"Total records: {len(records)}")
    print(f"Days: {total_days}")
    print(f"Products: {len(PRODUCTS)}")

    print("\nSales statistics by product:")
    for product in PRODUCTS.keys():
        stats = product_stats[product]
        avg = stats['total'] / stats['count'] if stats['count'] > 0 else 0
        zero_pct = (stats['zeros'] / stats['count'] * 100) if stats['count'] > 0 else 0
        print(f"  {product}:")
        print(f"    Total sold: {stats['total']:,}")
        print(f"    Avg daily: {avg:.1f}")
        print(f"    Min daily: {stats['min']}")
        print(f"    Max daily: {stats['max']}")
        print(f"    Zero days: {stats['zeros']} ({zero_pct:.1f}%)")

    return records

if __name__ == '__main__':
    # Generate dataset
    records = generate_dataset()

    # Save to CSV
    output_file = '/Users/urtzialfaro/Downloads/bakery_data_2025_complete.csv'

    with open(output_file, 'w', newline='') as csvfile:
        fieldnames = ['date', 'product_name', 'quantity_sold', 'revenue']
        writer = csv.DictWriter(csvfile, fieldnames=fieldnames)

        writer.writeheader()
        for record in records:
            writer.writerow(record)

    print(f"\nDataset saved to: {output_file}")

    # Show sample
    print("\nFirst 10 records:")
    for i, record in enumerate(records[:10]):
        print(f"  {record}")

    print("\nLast 10 records:")
    for i, record in enumerate(records[-10:]):
        print(f"  {record}")