"""
Automated Report Generation

Author: RSK World
Website: https://rskworld.in
Email: help@rskworld.in
Phone: +91 93305 39277
"""

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime
import os
import warnings
warnings.filterwarnings('ignore')


class AutomatedReport:
    """
    Automated Statistical Report Generation
    
    Author: RSK World
    Website: https://rskworld.in
    Email: help@rskworld.in
    Phone: +91 93305 39277
    """
    
    def __init__(self, output_dir='reports'):
        """
        Initialize report generator
        
        Parameters:
        -----------
        output_dir : str
            Directory for reports
        """
        self.output_dir = output_dir
        if not os.path.exists(output_dir):
            os.makedirs(output_dir)
        self.report_sections = []
    
    def add_section(self, title, content):
        """Add section to report"""
        self.report_sections.append({
            'title': title,
            'content': content,
            'timestamp': datetime.now().strftime("%Y-%m-%d %H:%M:%S")
        })
    
    def generate_regression_report(self, model, X, y, model_name="Regression Model"):
        """
        Generate comprehensive regression report
        
        Parameters:
        -----------
        model : object
            Fitted regression model
        X : array-like
            Independent variables
        y : array-like
            Dependent variable
        model_name : str
            Name of the model
        """
        report = []
        report.append("=" * 80)
        report.append(f"REGRESSION ANALYSIS REPORT: {model_name}")
        report.append("=" * 80)
        report.append(f"Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
        report.append("")
        
        # Model Summary
        report.append("MODEL SUMMARY")
        report.append("-" * 80)
        if hasattr(model, 'results'):
            results = model.results
            report.append(f"R-squared: {results.rsquared:.4f}")
            report.append(f"Adj. R-squared: {results.rsquared_adj:.4f}")
            report.append(f"F-statistic: {results.fvalue:.4f}")
            report.append(f"F p-value: {results.f_pvalue:.6f}")
            report.append(f"AIC: {results.aic:.4f}")
            report.append(f"BIC: {results.bic:.4f}")
            report.append("")
        
        # Coefficients
        report.append("COEFFICIENTS")
        report.append("-" * 80)
        if hasattr(model, 'results'):
            coef_df = pd.DataFrame({
                'Coefficient': results.params,
                'Std Error': results.bse,
                't-value': results.tvalues,
                'p-value': results.pvalues
            })
            report.append(coef_df.to_string())
            report.append("")
        
        # Diagnostics
        report.append("MODEL DIAGNOSTICS")
        report.append("-" * 80)
        
        if hasattr(model, 'check_multicollinearity'):
            try:
                vif = model.check_multicollinearity()
                report.append("\nVIF Results:")
                report.append(vif.to_string())
            except:
                pass
        
        if hasattr(model, 'check_heteroscedasticity'):
            try:
                het_result = model.check_heteroscedasticity()
                report.append(f"\nHeteroscedasticity Test: {het_result}")
            except:
                pass
        
        report_text = "\n".join(report)
        self.add_section(f"{model_name} Report", report_text)
        
        return report_text
    
    def generate_time_series_report(self, model, data, model_name="Time Series Model"):
        """Generate time series analysis report"""
        report = []
        report.append("=" * 80)
        report.append(f"TIME SERIES ANALYSIS REPORT: {model_name}")
        report.append("=" * 80)
        report.append(f"Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
        report.append("")
        
        # Data Summary
        report.append("DATA SUMMARY")
        report.append("-" * 80)
        report.append(f"Number of observations: {len(data)}")
        report.append(f"Mean: {np.mean(data):.4f}")
        report.append(f"Std: {np.std(data):.4f}")
        report.append(f"Min: {np.min(data):.4f}")
        report.append(f"Max: {np.max(data):.4f}")
        report.append("")
        
        # Model Summary
        if hasattr(model, 'results'):
            report.append("MODEL SUMMARY")
            report.append("-" * 80)
            results = model.results
            report.append(f"AIC: {results.aic:.4f}")
            report.append(f"BIC: {results.bic:.4f}")
            report.append(f"Log Likelihood: {results.llf:.4f}")
            report.append("")
        
        report_text = "\n".join(report)
        self.add_section(f"{model_name} Report", report_text)
        
        return report_text
    
    def save_report(self, filename=None, format='txt'):
        """
        Save report to file
        
        Parameters:
        -----------
        filename : str
            Output filename
        format : str
            'txt' or 'html'
        """
        if filename is None:
            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
            filename = f"report_{timestamp}"
        
        if format == 'txt':
            filepath = os.path.join(self.output_dir, f"{filename}.txt")
            with open(filepath, 'w', encoding='utf-8') as f:
                f.write("STATISTICAL ANALYSIS REPORT\n")
                f.write("=" * 80 + "\n")
                f.write(f"Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n")
                f.write(f"Author: RSK World (https://rskworld.in)\n")
                f.write("=" * 80 + "\n\n")
                
                for section in self.report_sections:
                    f.write(section['content'])
                    f.write("\n\n")
            
            print(f"Report saved to: {filepath}")
            return filepath
        
        elif format == 'html':
            filepath = os.path.join(self.output_dir, f"{filename}.html")
            html_content = self._generate_html_report()
            with open(filepath, 'w', encoding='utf-8') as f:
                f.write(html_content)
            
            print(f"HTML report saved to: {filepath}")
            return filepath
    
    def _generate_html_report(self):
        """Generate HTML report"""
        html = f"""
<!DOCTYPE html>
<html>
<head>
    <title>Statistical Analysis Report</title>
    <style>
        body {{ font-family: Arial, sans-serif; margin: 20px; }}
        h1 {{ color: #333; }}
        h2 {{ color: #666; border-bottom: 2px solid #666; }}
        pre {{ background: #f5f5f5; padding: 10px; border-radius: 5px; }}
        .timestamp {{ color: #999; font-size: 0.9em; }}
    </style>
</head>
<body>
    <h1>Statistical Analysis Report</h1>
    <p class="timestamp">Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}</p>
    <p class="timestamp">Author: RSK World (https://rskworld.in)</p>
"""
        
        for section in self.report_sections:
            html += f"<h2>{section['title']}</h2>\n"
            html += f"<pre>{section['content']}</pre>\n"
        
        html += """
</body>
</html>
"""
        return html
    
    def clear_report(self):
        """Clear all report sections"""
        self.report_sections = []


if __name__ == "__main__":
    # Example usage
    print("Automated Reporting Example")
    print("=" * 70)
    
    from regression_analysis import LinearRegressionModel
    import numpy as np
    
    # Generate sample data
    np.random.seed(42)
    X = np.random.randn(100, 3)
    y = 2 + 1.5 * X[:, 0] + 0.8 * X[:, 1] - 0.5 * X[:, 2] + np.random.randn(100) * 0.5
    
    # Fit model
    model = LinearRegressionModel()
    model.fit(X, y)
    
    # Generate report
    reporter = AutomatedReport()
    reporter.generate_regression_report(model, X, y, "Sample Regression")
    
    # Save report
    reporter.save_report("regression_report", format='txt')
    reporter.save_report("regression_report", format='html')