#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Example usage of the Weather Geo ERA5 Dataset

This script demonstrates how to use the weather_geo_utils module to load
and analyze weather data from the HuggingFace dataset.

Requirements:
    pip install pandas huggingface-hub matplotlib seaborn
"""

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from weather_geo_utils import (
    load_point_data, 
    load_cities_data, 
    convert_temperature_units,
    get_dataset_info
)

def example_single_city():
    """Example: Load data for a single city (Paris)."""
    print("=" * 60)
    print("EXAMPLE 1: Single City Analysis (Paris)")
    print("=" * 60)
    
    # Load 2023 data for Paris
    print("Loading Paris weather data for 2023...")
    df = load_point_data(
        lat=48.8566, 
        lon=2.3522, 
        start_date="2023-01-01", 
        end_date="2023-12-31"
    )
    
    # Convert temperatures to Celsius
    df = convert_temperature_units(df, from_unit='K', to_unit='C')
    
    print(f"Loaded {len(df)} records")
    print(f"Date range: {df['time'].min()} to {df['time'].max()}")
    print(f"Temperature range: {df['t2m'].min():.1f}°C to {df['t2m'].max():.1f}°C")
    print(f"Average temperature: {df['t2m'].mean():.1f}°C")
    
    # Plot temperature timeline
    plt.figure(figsize=(12, 6))
    plt.plot(df['time'], df['t2m'], linewidth=0.5, alpha=0.7)
    plt.title('Paris Temperature 2023')
    plt.xlabel('Date')
    plt.ylabel('Temperature (°C)')
    plt.grid(True, alpha=0.3)
    plt.tight_layout()
    plt.savefig('paris_temperature_2023.png', dpi=300, bbox_inches='tight')
    plt.show()
    
    return df

def example_multiple_cities():
    """Example: Compare multiple European cities."""
    print("\n" + "=" * 60)
    print("EXAMPLE 2: Multiple Cities Comparison")
    print("=" * 60)
    
    # Define cities
    cities = [
        (48.8566, 2.3522, "Paris"),
        (51.5074, -0.1278, "London"),
        (52.5200, 13.4050, "Berlin"),
        (41.9028, 12.4964, "Rome"),
        (59.3293, 18.0686, "Stockholm"),
    ]
    
    print(f"Loading data for {len(cities)} cities...")
    
    # Load 2023 data for all cities
    df = load_cities_data(cities, start_date="2023-01-01", end_date="2023-12-31")
    
    # Convert temperatures to Celsius
    df = convert_temperature_units(df, from_unit='K', to_unit='C')
    
    print(f"Total records loaded: {len(df)}")
    
    # Calculate monthly averages
    df['month'] = df['time'].dt.month
    monthly_temps = df.groupby(['city', 'month'])['t2m'].mean().reset_index()
    monthly_temps['month_name'] = pd.to_datetime(monthly_temps['month'], format='%m').dt.strftime('%b')
    
    # Plot comparison
    plt.figure(figsize=(12, 8))
    
    # Temperature comparison
    plt.subplot(2, 1, 1)
    for city in df['city'].unique():
        city_data = monthly_temps[monthly_temps['city'] == city]
        plt.plot(city_data['month'], city_data['t2m'], marker='o', label=city, linewidth=2)
    
    plt.title('Monthly Average Temperature Comparison 2023')
    plt.xlabel('Month')
    plt.ylabel('Temperature (°C)')
    plt.legend()
    plt.grid(True, alpha=0.3)
    plt.xticks(range(1, 13), ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',
                              'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])
    
    # Annual statistics
    plt.subplot(2, 1, 2)
    annual_stats = df.groupby('city')['t2m'].agg(['mean', 'min', 'max']).reset_index()
    
    x = range(len(annual_stats))
    plt.bar(x, annual_stats['mean'], alpha=0.7, label='Average')
    plt.errorbar(x, annual_stats['mean'], 
                yerr=[annual_stats['mean'] - annual_stats['min'],
                      annual_stats['max'] - annual_stats['mean']], 
                fmt='none', color='black', capsize=5, alpha=0.8)
    
    plt.title('Annual Temperature Statistics 2023')
    plt.xlabel('City')
    plt.ylabel('Temperature (°C)')
    plt.xticks(x, annual_stats['city'], rotation=45)
    plt.grid(True, alpha=0.3)
    
    plt.tight_layout()
    plt.savefig('cities_temperature_comparison.png', dpi=300, bbox_inches='tight')
    plt.show()
    
    # Print summary statistics
    print("\nAnnual Temperature Summary (°C):")
    print(annual_stats.round(1))
    
    return df

def example_climate_analysis():
    """Example: Long-term climate analysis."""
    print("\n" + "=" * 60)
    print("EXAMPLE 3: Long-term Climate Analysis (Paris)")
    print("=" * 60)
    
    print("Loading historical data for Paris (1980-2023)...")
    df = load_point_data(
        lat=48.8566, 
        lon=2.3522, 
        start_date="1980-01-01", 
        end_date="2023-12-31"
    )
    
    # Convert temperatures to Celsius
    df = convert_temperature_units(df, from_unit='K', to_unit='C')
    
    print(f"Loaded {len(df)} records over {df['time'].dt.year.nunique()} years")
    
    # Calculate annual averages
    df['year'] = df['time'].dt.year
    annual_temps = df.groupby('year')['t2m'].mean().reset_index()
    
    # Plot long-term trend
    plt.figure(figsize=(14, 10))
    
    # Annual temperature trend
    plt.subplot(2, 2, 1)
    plt.plot(annual_temps['year'], annual_temps['t2m'], linewidth=1, alpha=0.7)
    
    # Add trend line
    from numpy.polynomial import Polynomial
    p = Polynomial.fit(annual_temps['year'], annual_temps['t2m'], 1)
    plt.plot(annual_temps['year'], p(annual_temps['year']), 'r--', linewidth=2, label='Trend')
    
    plt.title('Annual Average Temperature (Paris)')
    plt.xlabel('Year')
    plt.ylabel('Temperature (°C)')
    plt.legend()
    plt.grid(True, alpha=0.3)
    
    # Monthly climatology
    plt.subplot(2, 2, 2)
    df['month'] = df['time'].dt.month
    monthly_climate = df.groupby('month')['t2m'].agg(['mean', 'std']).reset_index()
    
    plt.plot(monthly_climate['month'], monthly_climate['mean'], 'b-', linewidth=2, label='Average')
    plt.fill_between(monthly_climate['month'], 
                     monthly_climate['mean'] - monthly_climate['std'],
                     monthly_climate['mean'] + monthly_climate['std'],
                     alpha=0.3, label='±1 std dev')
    
    plt.title('Monthly Temperature Climatology')
    plt.xlabel('Month')
    plt.ylabel('Temperature (°C)')
    plt.legend()
    plt.grid(True, alpha=0.3)
    plt.xticks(range(1, 13), ['J', 'F', 'M', 'A', 'M', 'J',
                              'J', 'A', 'S', 'O', 'N', 'D'])
    
    # Temperature distribution
    plt.subplot(2, 2, 3)
    plt.hist(df['t2m'], bins=50, alpha=0.7, density=True)
    plt.axvline(df['t2m'].mean(), color='red', linestyle='--', linewidth=2, label=f'Mean: {df["t2m"].mean():.1f}°C')
    plt.title('Temperature Distribution')
    plt.xlabel('Temperature (°C)')
    plt.ylabel('Density')
    plt.legend()
    plt.grid(True, alpha=0.3)
    
    # Decade comparison
    plt.subplot(2, 2, 4)
    df['decade'] = (df['year'] // 10) * 10
    decade_temps = df.groupby('decade')['t2m'].mean().reset_index()
    
    plt.bar(decade_temps['decade'], decade_temps['t2m'], width=8, alpha=0.7)
    plt.title('Average Temperature by Decade')
    plt.xlabel('Decade')
    plt.ylabel('Temperature (°C)')
    plt.grid(True, alpha=0.3)
    
    plt.tight_layout()
    plt.savefig('paris_climate_analysis.png', dpi=300, bbox_inches='tight')
    plt.show()
    
    # Calculate warming trend
    warming_rate = p.coef[1]  # Slope of trend line
    total_warming = warming_rate * (annual_temps['year'].max() - annual_temps['year'].min())
    
    print(f"\nClimate Analysis Results:")
    print(f"Period: {annual_temps['year'].min()}-{annual_temps['year'].max()}")
    print(f"Average temperature: {df['t2m'].mean():.1f}°C")
    print(f"Warming rate: {warming_rate:.3f}°C/year")
    print(f"Total warming: {total_warming:.1f}°C over {annual_temps['year'].max() - annual_temps['year'].min()} years")
    
    return df

def main():
    """Run all examples."""
    print("Weather Geo ERA5 Dataset - Example Usage")
    print("=" * 60)
    
    # Display dataset info
    info = get_dataset_info()
    print("Dataset Information:")
    for key, value in info.items():
        print(f"  {key}: {value}")
    
    try:
        # Example 1: Single city
        df1 = example_single_city()
        
        # Example 2: Multiple cities
        df2 = example_multiple_cities()
        
        # Example 3: Climate analysis
        df3 = example_climate_analysis()
        
        print("\n" + "=" * 60)
        print("All examples completed successfully!")
        print("Generated plots:")
        print("  - paris_temperature_2023.png")
        print("  - cities_temperature_comparison.png") 
        print("  - paris_climate_analysis.png")
        print("=" * 60)
        
    except Exception as e:
        print(f"\nError running examples: {e}")
        print("Make sure you have installed the required packages:")
        print("  pip install pandas huggingface-hub matplotlib seaborn")

if __name__ == "__main__":
    main()