"""
Template for creating new analysis scripts
Copy this file and modify for your specific analysis

Usage:
1. Copy this file: cp analysis_template.py my_new_analysis.py
2. Update the ANALYSIS_NAME and DESCRIPTION
3. Implement your analysis logic in the main() function
4. Update the chart generation section
5. Run: python my_new_analysis.py
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from pathlib import Path

# Import utilities
from data_loader import load_sales_data, validate_data_structure
from validate_revenue import validate_revenue
from analysis_utils import (
    get_ltm_period_config, get_annual_data, calculate_annual_metrics,
    get_millions_formatter, setup_revenue_chart, save_chart,
    format_currency, print_annual_summary, sort_mixed_years,
    apply_exclusion_filters
)
from config import (
    DATA_FILE, OUTPUT_DIR, ANALYSIS_YEARS, MAX_DATE,
    CHART_SIZES, ensure_directories, get_data_path, COMPANY_NAME
)

# ============================================================================
# CONFIGURATION
# ============================================================================

ANALYSIS_NAME = "Template Analysis"
DESCRIPTION = "Template for new analyses - customize this for your specific analysis"

# ============================================================================
# MAIN ANALYSIS FUNCTION
# ============================================================================

def main():
    """Main analysis function"""
    
    print(f"\n{'='*60}")
    print(f"{ANALYSIS_NAME}")
    print(f"{'='*60}\n")
    
    # 1. Load data
    print("Loading data...")
    try:
        df = load_sales_data(get_data_path())
        print(f"Loaded {len(df):,} transactions")
    except Exception as e:
        print(f"ERROR loading data: {e}")
        return
    
    # 2. Validate data structure
    is_valid, msg = validate_data_structure(df)
    if not is_valid:
        print(f"ERROR: {msg}")
        return
    print("Data validation passed")
    
    # 3. Apply exclusion filters (if configured)
    df = apply_exclusion_filters(df)
    
    # 4. Filter by date range
    from config import MIN_YEAR, DATE_COLUMN
    df = df[df['Year'] >= MIN_YEAR]
    if DATE_COLUMN in df.columns:
        df = df[df[DATE_COLUMN] <= MAX_DATE]
    
    # 5. Setup LTM period (if enabled)
    ltm_start, ltm_end = get_ltm_period_config()
    if ltm_start and ltm_end:
        print(f"LTM period: {ltm_start} to {ltm_end}")
    
    # 6. Prepare data
    print("\nPreparing data...")
    # Add your data preparation logic here
    # Example: df['CustomColumn'] = df[REVENUE_COLUMN] * df[QUANTITY_COLUMN]
    
    # 7. Calculate annual metrics
    print("\nCalculating annual metrics...")
    
    def calculate_metrics(year_data):
        """Calculate metrics for a single year"""
        from config import REVENUE_COLUMN
        return {
            'Revenue': year_data[REVENUE_COLUMN].sum(),
            # Add your custom metrics here
            # 'CustomMetric': year_data['CustomColumn'].mean(),
        }
    
    annual_df = calculate_annual_metrics(df, calculate_metrics, ltm_start, ltm_end)
    
    # 8. Print summary
    print_annual_summary(annual_df, 'Revenue', 'Revenue')
    
    # 9. Create visualizations
    print("Generating charts...")
    ensure_directories()
    
    # Example chart: Annual revenue trend
    fig, ax = plt.subplots(figsize=CHART_SIZES['medium'])
    
    # Prepare data for plotting (handle mixed types)
    annual_df_sorted = sort_mixed_years(annual_df.reset_index(), 'Year')
    years = annual_df_sorted['Year'].tolist()
    revenue = annual_df_sorted['Revenue'].values / 1e6  # Convert to millions
    
    # Create chart
    ax.plot(range(len(years)), revenue, marker='o', linewidth=2, markersize=8)
    ax.set_xticks(range(len(years)))
    ax.set_xticklabels(years, rotation=45, ha='right')
    setup_revenue_chart(ax)
    
    # Add LTM notation to title if applicable
    title = f'Annual Revenue Trend - {COMPANY_NAME}'
    if ltm_start and ltm_end:
        from config import get_ltm_label
        ltm_label = get_ltm_label()
        if ltm_label:
            title += f'\n({ltm_label})'
    ax.set_title(title)
    
    plt.tight_layout()
    save_chart(fig, f'{ANALYSIS_NAME.lower().replace(" ", "_")}_trend.png')
    plt.close()
    
    # Add more charts as needed...
    
    # 10. Validate revenue
    print("\nValidating revenue...")
    validate_revenue(df, ANALYSIS_NAME)
    
    print(f"\n{ANALYSIS_NAME} complete!")
    print(f"Charts saved to: {OUTPUT_DIR}")

# ============================================================================
# RUN ANALYSIS
# ============================================================================

if __name__ == "__main__":
    main()