betatim · July 17, 2025 08:17
diff --git a/log-test.py b/log-test.py
 import numpy as np
 import logging
 import time

 # Configure logging
 logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s',
    handlers=[
        logging.FileHandler('calculations.log'),
        logging.StreamHandler()
    ]
 )

 logger = logging.getLogger(__name__)


 def calculate_statistics(data):
    """
    Calculate basic statistics of a numpy array and log the results.
    """
    logger.info(f"Starting statistics calculation for data with shape {data.shape}")
    
    try:
        mean_val = np.mean(data)
        std_val = np.std(data)
        min_val = np.min(data)
        max_val = np.max(data)
        
        logger.info(f"Statistics calculated successfully:")
        logger.info(f"  Mean: {mean_val:.4f}")
        logger.info(f"  Standard deviation: {std_val:.4f}")
        logger.info(f"  Min: {min_val:.4f}")
        logger.info(f"  Max: {max_val:.4f}")
        
        return {
            'mean': mean_val,
            'std': std_val,
            'min': min_val,
            'max': max_val
        }
        
    except Exception as e:
        logger.error(f"Error calculating statistics: {e}")
        raise


 def matrix_operations(matrix_a, matrix_b):
    """
    Perform matrix operations and log the process.
    """
    logger.info(f"Starting matrix operations with shapes: A={matrix_a.shape}, B={matrix_b.shape}")
    
    try:
        # Matrix multiplication
        logger.debug("Performing matrix multiplication...")
        result_mult = np.dot(matrix_a, matrix_b)
        logger.info(f"Matrix multiplication completed. Result shape: {result_mult.shape}")
        
        # Matrix addition
        logger.debug("Performing matrix addition...")
        result_add = matrix_a + matrix_b
        logger.info(f"Matrix addition completed. Result shape: {result_add.shape}")
        
        # Calculate determinant
        logger.debug("Calculating determinant...")
        det_a = np.linalg.det(matrix_a)
        det_b = np.linalg.det(matrix_b)
        logger.info(f"Determinants calculated: A={det_a:.4f}, B={det_b:.4f}")
        
        return {
            'multiplication': result_mult,
            'addition': result_add,
            'det_a': det_a,
            'det_b': det_b
        }
        
    except np.linalg.LinAlgError as e:
        logger.error(f"Linear algebra error: {e}")
        raise
    except Exception as e:
        logger.error(f"Error in matrix operations: {e}")
        raise


 def polynomial_fitting(x_data, y_data, degree=2):
    """
    Fit a polynomial to data points and log the fitting process.
    """
    logger.info(f"Starting polynomial fitting with degree {degree}")
    logger.info(f"Data points: {len(x_data)}")
    
    try:
        # Fit polynomial
        logger.debug("Fitting polynomial coefficients...")
        coefficients = np.polyfit(x_data, y_data, degree)
        logger.info(f"Polynomial coefficients: {coefficients}")
        
        # Calculate R-squared
        y_pred = np.polyval(coefficients, x_data)
        ss_res = np.sum((y_data - y_pred) ** 2)
        ss_tot = np.sum((y_data - np.mean(y_data)) ** 2)
        r_squared = 1 - (ss_res / ss_tot)
        
        logger.info(f"R-squared value: {r_squared:.4f}")
        
        return {
            'coefficients': coefficients,
            'r_squared': r_squared,
            'y_predicted': y_pred
        }
        
    except Exception as e:
        logger.error(f"Error in polynomial fitting: {e}")
        raise


 def random_simulation(samples=1000, seed=42):
    """
    Perform a random simulation and log the results.
    """
    logger.info(f"Starting random simulation with {samples} samples")
    
    try:
        # Set random seed
        np.random.seed(seed)
        logger.debug(f"Random seed set to {seed}")
        
        # Generate random data
        logger.debug("Generating random normal data...")
        data = np.random.normal(0, 1, samples)
        
        # Generate random matrix
        logger.debug("Generating random matrix...")
        matrix = np.random.rand(5, 5)
        
        # Calculate some statistics
        logger.debug("Calculating simulation statistics...")
        stats = calculate_statistics(data)
        
        logger.info("Random simulation completed successfully")
        logger.info(f"Generated {samples} random samples and 5x5 matrix")
        
        return {
            'data': data,
            'matrix': matrix,
            'statistics': stats
        }
        
    except Exception as e:
        logger.error(f"Error in random simulation: {e}")
        raise


 def performance_benchmark(func, *args, iterations=100):
    """
    Benchmark a function's performance and log the results.
    """
    logger.info(f"Starting performance benchmark for {func.__name__}")
    logger.info(f"Running {iterations} iterations...")
    
    try:
        start_time = time.time()
        
        for i in range(iterations):
            if i % 20 == 0:  # Log progress every 20 iterations
                logger.debug(f"Progress: {i}/{iterations} iterations completed")
            result = func(*args)
        
        end_time = time.time()
        total_time = end_time - start_time
        avg_time = total_time / iterations
        
        logger.info(f"Benchmark completed:")
        logger.info(f"  Total time: {total_time:.4f} seconds")
        logger.info(f"  Average time per iteration: {avg_time:.6f} seconds")
        logger.info(f"  Iterations per second: {iterations/total_time:.2f}")
        
        return {
            'total_time': total_time,
            'avg_time': avg_time,
            'iterations_per_second': iterations/total_time,
            'result': result
        }
        
    except Exception as e:
        logger.error(f"Error in performance benchmark: {e}")
        raise


 if __name__ == "__main__":
    logger.info("Starting calculation test script")
    
    # Test the functions

    # Generate some test data
    x = np.linspace(0, 10, 100)
    y = 2 * x**2 + 3 * x + 1 + np.random.normal(0, 0.5, 100)
    
    # Test statistics calculation
    stats_result = calculate_statistics(y)
    
    # Test matrix operations
    matrix_a = np.random.rand(3, 3)
    matrix_b = np.random.rand(3, 3)
    matrix_result = matrix_operations(matrix_a, matrix_b)
    
    # Test polynomial fitting
    poly_result = polynomial_fitting(x, y, degree=2)
    
    # Test random simulation
    sim_result = random_simulation(samples=500)
    
    # Test performance benchmark
    benchmark_result = performance_benchmark(calculate_statistics, sim_result['data'], iterations=50)
diff --git a/log_tracer.py b/log_tracer.py
 import os
 import logging
 import sys
 from collections import defaultdict
 from contextlib import contextmanager
 import runpy
 import traceback


 class LineTracer:
    """
    A tool to trace log messages to specific lines of code execution.
    """
    
    def __init__(self, script_path, output_path):
        self.script_path = script_path
        self.output_path = output_path
        self.current_line = None
        self.line_logs = defaultdict(list)
        self.original_trace = None
        self.original_excepthook = None
        
        with open(script_path, 'r') as f:
            self.script_lines = f.readlines()
    
    def setup_tracing(self):
        """Setup line-by-line tracing using sys.settrace."""
        self.original_trace = sys.gettrace()
        sys.settrace(self.trace_function)
    
    def setup_logging_patch(self):
        """Patch Logger.handle to always call our handler for every log record."""
        class TracerHandler(logging.Handler):
            def __init__(handler_self, tracer):
                super().__init__()
                handler_self.tracer = tracer
            def emit(handler_self, record):
                msg = handler_self.format(record)
                if handler_self.tracer.current_line is not None:
                    handler_self.tracer.line_logs[handler_self.tracer.current_line].append(msg)
        self._tracer_handler = TracerHandler(self)
        fmt = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
        self._tracer_handler.setFormatter(fmt)
        
        self._original_logger_handle = logging.Logger.handle
        tracer_handler = self._tracer_handler
        def handle_patch(logger, record):
            tracer_handler.handle(record)
            return self._original_logger_handle(logger, record)
        logging.Logger.handle = handle_patch

    def restore_logging_patch(self):
        """Restore the original Logger.handle method."""
        if hasattr(self, '_original_logger_handle'):
            logging.Logger.handle = self._original_logger_handle
    
    def restore_tracing(self):
        """Restore original tracing."""
        if self.original_trace:
            sys.settrace(self.original_trace)
    
    def clean_traceback(self, exc_type, exc_value, exc_traceback):
        """Clean up traceback to remove tracer infrastructure."""
        if exc_traceback:
            # Remove traceback lines added due to us using runpy
            tb = exc_traceback
            while tb and (tb.tb_frame.f_code.co_filename.endswith('log_tracer.py') or 
                          'runpy' in tb.tb_frame.f_code.co_filename):
                tb = tb.tb_next
            
            if tb:
                # Use the internal colorised function, could be fragile :-/
                new_exc = exc_type(exc_value)
                new_exc.__traceback__ = tb
                traceback._print_exception_bltin(new_exc)
                return

        # Fallback to original
        traceback.print_exception(exc_type, exc_value, exc_traceback)
    
    def trace_function(self, frame, event, arg):
        """Trace function for sys.settrace."""
        if event == 'line':
            # Only track lines from our script at top-level
            if (frame.f_code.co_filename.endswith(self.script_path) and 
                frame.f_code.co_name == '<module>'):
                self.current_line = frame.f_lineno
        return self.trace_function
    
    @contextmanager
    def trace_execution(self):
        """Context manager to trace execution and capture logs."""
        self.setup_tracing()
        self.setup_logging_patch()
        try:
            yield
        finally:
            self.restore_logging_patch()
            self.restore_tracing()
            self.write_output()
    
    def write_output(self):
        """Write the traced output to file."""
        with open(self.output_path, 'w') as f:
            for line_num in range(1, len(self.script_lines) + 1):
                if line_num in self.line_logs:
                    line_content = self.script_lines[line_num - 1].rstrip()
                    f.write(f"{line_content}\n")
                    
                    for log_record in self.line_logs[line_num]:
                        f.write(f"LOG:{log_record}\n")
                else:
                    line_content = self.script_lines[line_num - 1].rstrip()
                    f.write(f"{line_content}\n")


 def trace_script_execution(script_path, output_path):
    """
    Trace the execution of a script and capture log messages for each line.
    
    Args:
        script_path: Path to the script to trace
        output_path: Path to write the output file
    """
    script_path = os.path.abspath(script_path)
    tracer = LineTracer(script_path, output_path)
    
    with tracer.trace_execution():
        try:
            runpy.run_path(script_path, run_name='__main__')
        except Exception as e:
            tracer.clean_traceback(type(e), e, e.__traceback__)
            sys.exit(1)


 def main():
    """Main function for command line usage."""
    import argparse
    
    parser = argparse.ArgumentParser(description='Trace script execution and capture logs per line')
    parser.add_argument('script', help='Path to the script to trace')
    parser.add_argument('-o', '--output', default='traced_output.txt', 
                       help='Path to write the output file (default: traced_output.txt)')
    
    args = parser.parse_args()
    
    trace_script_execution(args.script, args.output)


 if __name__ == "__main__":
    main() 
diff --git a/traced_output.txt b/traced_output.txt
 import numpy as np
 import logging
 import time

 # Configure logging
 logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s',
    handlers=[
        logging.FileHandler('calculations.log'),
        logging.StreamHandler()
    ]
 )

 logger = logging.getLogger(__name__)


 def calculate_statistics(data):
    """
    Calculate basic statistics of a numpy array and log the results.
    """
    logger.info(f"Starting statistics calculation for data with shape {data.shape}")

    try:
        mean_val = np.mean(data)
        std_val = np.std(data)
        min_val = np.min(data)
        max_val = np.max(data)

        logger.info(f"Statistics calculated successfully:")
        logger.info(f"  Mean: {mean_val:.4f}")
        logger.info(f"  Standard deviation: {std_val:.4f}")
        logger.info(f"  Min: {min_val:.4f}")
        logger.info(f"  Max: {max_val:.4f}")

        return {
            'mean': mean_val,
            'std': std_val,
            'min': min_val,
            'max': max_val
        }

    except Exception as e:
        logger.error(f"Error calculating statistics: {e}")
        raise


 def matrix_operations(matrix_a, matrix_b):
    """
    Perform matrix operations and log the process.
    """
    logger.info(f"Starting matrix operations with shapes: A={matrix_a.shape}, B={matrix_b.shape}")

    try:
        # Matrix multiplication
        logger.debug("Performing matrix multiplication...")
        result_mult = np.dot(matrix_a, matrix_b)
        logger.info(f"Matrix multiplication completed. Result shape: {result_mult.shape}")

        # Matrix addition
        logger.debug("Performing matrix addition...")
        result_add = matrix_a + matrix_b
        logger.info(f"Matrix addition completed. Result shape: {result_add.shape}")

        # Calculate determinant
        logger.debug("Calculating determinant...")
        det_a = np.linalg.det(matrix_a)
        det_b = np.linalg.det(matrix_b)
        logger.info(f"Determinants calculated: A={det_a:.4f}, B={det_b:.4f}")

        return {
            'multiplication': result_mult,
            'addition': result_add,
            'det_a': det_a,
            'det_b': det_b
        }

    except np.linalg.LinAlgError as e:
        logger.error(f"Linear algebra error: {e}")
        raise
    except Exception as e:
        logger.error(f"Error in matrix operations: {e}")
        raise


 def polynomial_fitting(x_data, y_data, degree=2):
    """
    Fit a polynomial to data points and log the fitting process.
    """
    logger.info(f"Starting polynomial fitting with degree {degree}")
    logger.info(f"Data points: {len(x_data)}")

    try:
        # Fit polynomial
        logger.debug("Fitting polynomial coefficients...")
        coefficients = np.polyfit(x_data, y_data, degree)
        logger.info(f"Polynomial coefficients: {coefficients}")

        # Calculate R-squared
        y_pred = np.polyval(coefficients, x_data)
        ss_res = np.sum((y_data - y_pred) ** 2)
        ss_tot = np.sum((y_data - np.mean(y_data)) ** 2)
        r_squared = 1 - (ss_res / ss_tot)

        logger.info(f"R-squared value: {r_squared:.4f}")

        return {
            'coefficients': coefficients,
            'r_squared': r_squared,
            'y_predicted': y_pred
        }

    except Exception as e:
        logger.error(f"Error in polynomial fitting: {e}")
        raise


 def random_simulation(samples=1000, seed=42):
    """
    Perform a random simulation and log the results.
    """
    logger.info(f"Starting random simulation with {samples} samples")

    try:
        # Set random seed
        np.random.seed(seed)
        logger.debug(f"Random seed set to {seed}")

        # Generate random data
        logger.debug("Generating random normal data...")
        data = np.random.normal(0, 1, samples)

        # Generate random matrix
        logger.debug("Generating random matrix...")
        matrix = np.random.rand(5, 5)

        # Calculate some statistics
        logger.debug("Calculating simulation statistics...")
        stats = calculate_statistics(data)

        logger.info("Random simulation completed successfully")
        logger.info(f"Generated {samples} random samples and 5x5 matrix")

        return {
            'data': data,
            'matrix': matrix,
            'statistics': stats
        }

    except Exception as e:
        logger.error(f"Error in random simulation: {e}")
        raise


 def performance_benchmark(func, *args, iterations=100):
    """
    Benchmark a function's performance and log the results.
    """
    logger.info(f"Starting performance benchmark for {func.__name__}")
    logger.info(f"Running {iterations} iterations...")

    try:
        start_time = time.time()

        for i in range(iterations):
            if i % 20 == 0:  # Log progress every 20 iterations
                logger.debug(f"Progress: {i}/{iterations} iterations completed")
            result = func(*args)

        end_time = time.time()
        total_time = end_time - start_time
        avg_time = total_time / iterations

        logger.info(f"Benchmark completed:")
        logger.info(f"  Total time: {total_time:.4f} seconds")
        logger.info(f"  Average time per iteration: {avg_time:.6f} seconds")
        logger.info(f"  Iterations per second: {iterations/total_time:.2f}")

        return {
            'total_time': total_time,
            'avg_time': avg_time,
            'iterations_per_second': iterations/total_time,
            'result': result
        }

    except Exception as e:
        logger.error(f"Error in performance benchmark: {e}")
        raise


 if __name__ == "__main__":
    logger.info("Starting calculation test script")
 LOG:2025-07-16 18:15:36,631 - INFO - Starting calculation test script

    # Test the functions

    # Generate some test data
    x = np.linspace(0, 10, 100)
    y = 2 * x**2 + 3 * x + 1 + np.random.normal(0, 0.5, 100)

    # Test statistics calculation
    stats_result = calculate_statistics(y)
 LOG:2025-07-16 18:15:36,661 - INFO - Starting statistics calculation for data with shape (100,)
 LOG:2025-07-16 18:15:36,661 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,661 - INFO -   Mean: 83.0020
 LOG:2025-07-16 18:15:36,661 - INFO -   Standard deviation: 68.7736
 LOG:2025-07-16 18:15:36,661 - INFO -   Min: 0.0573
 LOG:2025-07-16 18:15:36,662 - INFO -   Max: 231.1586

    # Test matrix operations
    matrix_a = np.random.rand(3, 3)
    matrix_b = np.random.rand(3, 3)
    matrix_result = matrix_operations(matrix_a, matrix_b)
 LOG:2025-07-16 18:15:36,662 - INFO - Starting matrix operations with shapes: A=(3, 3), B=(3, 3)
 LOG:2025-07-16 18:15:36,662 - INFO - Matrix multiplication completed. Result shape: (3, 3)
 LOG:2025-07-16 18:15:36,662 - INFO - Matrix addition completed. Result shape: (3, 3)
 LOG:2025-07-16 18:15:36,662 - INFO - Determinants calculated: A=0.0595, B=0.0822

    # Test polynomial fitting
    poly_result = polynomial_fitting(x, y, degree=2)
 LOG:2025-07-16 18:15:36,662 - INFO - Starting polynomial fitting with degree 2
 LOG:2025-07-16 18:15:36,662 - INFO - Data points: 100
 LOG:2025-07-16 18:15:36,663 - INFO - Polynomial coefficients: [1.99126391 3.09489698 0.81678548]
 LOG:2025-07-16 18:15:36,663 - INFO - R-squared value: 0.9999

    # Test random simulation
    sim_result = random_simulation(samples=500)
 LOG:2025-07-16 18:15:36,663 - INFO - Starting random simulation with 500 samples
 LOG:2025-07-16 18:15:36,663 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,663 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,663 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,663 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,664 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,664 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,664 - INFO - Random simulation completed successfully
 LOG:2025-07-16 18:15:36,664 - INFO - Generated 500 random samples and 5x5 matrix

    # Test performance benchmark
    benchmark_result = performance_benchmark(calculate_statistics, sim_result['data'], iterations=50)
 LOG:2025-07-16 18:15:36,664 - INFO - Starting performance benchmark for calculate_statistics
 LOG:2025-07-16 18:15:36,664 - INFO - Running 50 iterations...
 LOG:2025-07-16 18:15:36,664 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,664 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,664 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,664 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,664 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,665 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,665 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,665 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,665 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,665 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,665 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,665 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,665 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,665 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,665 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,665 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,665 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,666 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,666 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,666 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,666 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,666 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,666 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,666 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,666 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,666 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,667 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,667 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,667 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,667 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,667 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,667 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,667 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,667 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,667 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,667 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,668 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,668 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,668 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,668 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,668 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,668 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,668 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,668 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,668 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,668 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,669 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,669 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,669 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,669 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,669 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,669 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,669 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,669 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,669 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,669 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,669 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,670 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,670 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,670 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,670 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,670 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,670 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,670 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,670 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,670 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,670 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,670 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,670 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,671 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,671 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,671 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,671 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,671 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,671 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,671 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,671 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,671 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,671 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,671 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,671 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,672 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,672 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,672 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,672 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,672 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,672 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,672 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,672 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,672 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,672 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,672 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,672 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,673 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,673 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,673 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,673 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,673 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,673 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,673 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,673 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,673 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,673 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,673 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,673 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,674 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,674 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,674 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,674 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,674 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,674 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,674 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,674 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,674 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,674 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,674 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,674 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,675 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,675 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,675 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,675 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,675 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,675 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,675 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,675 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,675 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,675 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,675 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,675 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,676 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,676 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,676 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,676 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,676 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,676 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,676 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,676 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,676 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,676 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,676 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,677 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,677 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,677 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,677 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,677 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,677 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,677 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,677 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,677 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,677 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,677 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,677 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,678 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,678 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,678 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,678 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,678 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,678 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,678 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,678 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,678 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,678 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,678 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,678 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,679 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,679 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,679 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,679 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,679 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,679 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,679 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,679 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,679 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,679 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,679 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,679 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,680 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,680 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,680 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,680 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,680 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,680 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,680 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,680 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,680 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,680 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,680 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,680 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,681 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,681 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,681 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,681 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,681 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,681 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,681 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,681 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,681 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,681 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,681 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,681 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,682 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,682 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,682 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,682 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,682 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,682 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,682 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,682 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,682 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,682 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,682 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,682 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,683 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,683 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,683 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,683 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,683 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,683 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,683 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,683 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,683 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,683 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,683 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,683 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,684 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,684 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,684 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,684 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,684 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,684 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,684 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,684 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,684 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,684 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,684 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,684 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,685 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,685 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,685 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,685 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,685 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,685 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,685 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,685 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,685 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,685 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,685 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,685 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,686 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,686 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,686 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,686 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,686 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,686 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,686 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,686 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,686 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,686 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,686 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,687 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,687 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,687 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,687 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,687 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,687 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,687 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,687 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,687 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,687 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,688 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,688 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,688 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,688 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,688 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,688 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,688 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,688 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,688 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,688 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,688 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,689 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,689 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,689 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,689 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,689 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,689 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,689 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,689 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,689 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,689 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,689 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,690 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,690 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,690 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,690 - INFO - Starting statistics calculation for data with shape (500,)
 LOG:2025-07-16 18:15:36,690 - INFO - Statistics calculated successfully:
 LOG:2025-07-16 18:15:36,690 - INFO -   Mean: 0.0068
 LOG:2025-07-16 18:15:36,690 - INFO -   Standard deviation: 0.9803
 LOG:2025-07-16 18:15:36,690 - INFO -   Min: -3.2413
 LOG:2025-07-16 18:15:36,690 - INFO -   Max: 3.8527
 LOG:2025-07-16 18:15:36,690 - INFO - Benchmark completed:
 LOG:2025-07-16 18:15:36,690 - INFO -   Total time: 0.0262 seconds
 LOG:2025-07-16 18:15:36,690 - INFO -   Average time per iteration: 0.000524 seconds
 LOG:2025-07-16 18:15:36,691 - INFO -   Iterations per second: 1907.82
	import numpy as np
	import logging
	import time

	# Configure logging
	logging.basicConfig(
	level=logging.INFO,
	format='%(asctime)s - %(levelname)s - %(message)s',
	handlers=[
	logging.FileHandler('calculations.log'),
	logging.StreamHandler()
	]
	)

	logger = logging.getLogger(__name__)


	def calculate_statistics(data):
	"""
	Calculate basic statistics of a numpy array and log the results.
	"""
	logger.info(f"Starting statistics calculation for data with shape {data.shape}")

	try:
	mean_val = np.mean(data)
	std_val = np.std(data)
	min_val = np.min(data)
	max_val = np.max(data)

	logger.info(f"Statistics calculated successfully:")
	logger.info(f" Mean: {mean_val:.4f}")
	logger.info(f" Standard deviation: {std_val:.4f}")
	logger.info(f" Min: {min_val:.4f}")
	logger.info(f" Max: {max_val:.4f}")

	return {
	'mean': mean_val,
	'std': std_val,
	'min': min_val,
	'max': max_val
	}

	except Exception as e:
	logger.error(f"Error calculating statistics: {e}")
	raise


	def matrix_operations(matrix_a, matrix_b):
	"""
	Perform matrix operations and log the process.
	"""
	logger.info(f"Starting matrix operations with shapes: A={matrix_a.shape}, B={matrix_b.shape}")

	try:
	# Matrix multiplication
	logger.debug("Performing matrix multiplication...")
	result_mult = np.dot(matrix_a, matrix_b)
	logger.info(f"Matrix multiplication completed. Result shape: {result_mult.shape}")

	# Matrix addition
	logger.debug("Performing matrix addition...")
	result_add = matrix_a + matrix_b
	logger.info(f"Matrix addition completed. Result shape: {result_add.shape}")

	# Calculate determinant
	logger.debug("Calculating determinant...")
	det_a = np.linalg.det(matrix_a)
	det_b = np.linalg.det(matrix_b)
	logger.info(f"Determinants calculated: A={det_a:.4f}, B={det_b:.4f}")

	return {
	'multiplication': result_mult,
	'addition': result_add,
	'det_a': det_a,
	'det_b': det_b
	}

	except np.linalg.LinAlgError as e:
	logger.error(f"Linear algebra error: {e}")
	raise
	except Exception as e:
	logger.error(f"Error in matrix operations: {e}")
	raise


	def polynomial_fitting(x_data, y_data, degree=2):
	"""
	Fit a polynomial to data points and log the fitting process.
	"""
	logger.info(f"Starting polynomial fitting with degree {degree}")
	logger.info(f"Data points: {len(x_data)}")

	try:
	# Fit polynomial
	logger.debug("Fitting polynomial coefficients...")
	coefficients = np.polyfit(x_data, y_data, degree)
	logger.info(f"Polynomial coefficients: {coefficients}")

	# Calculate R-squared
	y_pred = np.polyval(coefficients, x_data)
	ss_res = np.sum((y_data - y_pred) ** 2)
	ss_tot = np.sum((y_data - np.mean(y_data)) ** 2)
	r_squared = 1 - (ss_res / ss_tot)

	logger.info(f"R-squared value: {r_squared:.4f}")

	return {
	'coefficients': coefficients,
	'r_squared': r_squared,
	'y_predicted': y_pred
	}

	except Exception as e:
	logger.error(f"Error in polynomial fitting: {e}")
	raise


	def random_simulation(samples=1000, seed=42):
	"""
	Perform a random simulation and log the results.
	"""
	logger.info(f"Starting random simulation with {samples} samples")

	try:
	# Set random seed
	np.random.seed(seed)
	logger.debug(f"Random seed set to {seed}")

	# Generate random data
	logger.debug("Generating random normal data...")
	data = np.random.normal(0, 1, samples)

	# Generate random matrix
	logger.debug("Generating random matrix...")
	matrix = np.random.rand(5, 5)

	# Calculate some statistics
	logger.debug("Calculating simulation statistics...")
	stats = calculate_statistics(data)

	logger.info("Random simulation completed successfully")
	logger.info(f"Generated {samples} random samples and 5x5 matrix")

	return {
	'data': data,
	'matrix': matrix,
	'statistics': stats
	}

	except Exception as e:
	logger.error(f"Error in random simulation: {e}")
	raise


	def performance_benchmark(func, *args, iterations=100):
	"""
	Benchmark a function's performance and log the results.
	"""
	logger.info(f"Starting performance benchmark for {func.__name__}")
	logger.info(f"Running {iterations} iterations...")

	try:
	start_time = time.time()

	for i in range(iterations):
	if i % 20 == 0: # Log progress every 20 iterations
	logger.debug(f"Progress: {i}/{iterations} iterations completed")
	result = func(*args)

	end_time = time.time()
	total_time = end_time - start_time
	avg_time = total_time / iterations

	logger.info(f"Benchmark completed:")
	logger.info(f" Total time: {total_time:.4f} seconds")
	logger.info(f" Average time per iteration: {avg_time:.6f} seconds")
	logger.info(f" Iterations per second: {iterations/total_time:.2f}")

	return {
	'total_time': total_time,
	'avg_time': avg_time,
	'iterations_per_second': iterations/total_time,
	'result': result
	}

	except Exception as e:
	logger.error(f"Error in performance benchmark: {e}")
	raise


	if __name__ == "__main__":
	logger.info("Starting calculation test script")

	# Test the functions

	# Generate some test data
	x = np.linspace(0, 10, 100)
	y = 2 * x*2 + 3 x + 1 + np.random.normal(0, 0.5, 100)

	# Test statistics calculation
	stats_result = calculate_statistics(y)

	# Test matrix operations
	matrix_a = np.random.rand(3, 3)
	matrix_b = np.random.rand(3, 3)
	matrix_result = matrix_operations(matrix_a, matrix_b)

	# Test polynomial fitting
	poly_result = polynomial_fitting(x, y, degree=2)

	# Test random simulation
	sim_result = random_simulation(samples=500)

	# Test performance benchmark
	benchmark_result = performance_benchmark(calculate_statistics, sim_result['data'], iterations=50)
	import os
	import logging
	import sys
	from collections import defaultdict
	from contextlib import contextmanager
	import runpy
	import traceback


	class LineTracer:
	"""
	A tool to trace log messages to specific lines of code execution.
	"""

	def __init__(self, script_path, output_path):
	self.script_path = script_path
	self.output_path = output_path
	self.current_line = None
	self.line_logs = defaultdict(list)
	self.original_trace = None
	self.original_excepthook = None

	with open(script_path, 'r') as f:
	self.script_lines = f.readlines()

	def setup_tracing(self):
	"""Setup line-by-line tracing using sys.settrace."""
	self.original_trace = sys.gettrace()
	sys.settrace(self.trace_function)

	def setup_logging_patch(self):
	"""Patch Logger.handle to always call our handler for every log record."""
	class TracerHandler(logging.Handler):
	def __init__(handler_self, tracer):
	super().__init__()
	handler_self.tracer = tracer
	def emit(handler_self, record):
	msg = handler_self.format(record)
	if handler_self.tracer.current_line is not None:
	handler_self.tracer.line_logs[handler_self.tracer.current_line].append(msg)
	self._tracer_handler = TracerHandler(self)
	fmt = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
	self._tracer_handler.setFormatter(fmt)

	self._original_logger_handle = logging.Logger.handle
	tracer_handler = self._tracer_handler
	def handle_patch(logger, record):
	tracer_handler.handle(record)
	return self._original_logger_handle(logger, record)
	logging.Logger.handle = handle_patch

	def restore_logging_patch(self):
	"""Restore the original Logger.handle method."""
	if hasattr(self, '_original_logger_handle'):
	logging.Logger.handle = self._original_logger_handle

	def restore_tracing(self):
	"""Restore original tracing."""
	if self.original_trace:
	sys.settrace(self.original_trace)

	def clean_traceback(self, exc_type, exc_value, exc_traceback):
	"""Clean up traceback to remove tracer infrastructure."""
	if exc_traceback:
	# Remove traceback lines added due to us using runpy
	tb = exc_traceback
	while tb and (tb.tb_frame.f_code.co_filename.endswith('log_tracer.py') or
	'runpy' in tb.tb_frame.f_code.co_filename):
	tb = tb.tb_next

	if tb:
	# Use the internal colorised function, could be fragile :-/
	new_exc = exc_type(exc_value)
	new_exc.__traceback__ = tb
	traceback._print_exception_bltin(new_exc)
	return

	# Fallback to original
	traceback.print_exception(exc_type, exc_value, exc_traceback)

	def trace_function(self, frame, event, arg):
	"""Trace function for sys.settrace."""
	if event == 'line':
	# Only track lines from our script at top-level
	if (frame.f_code.co_filename.endswith(self.script_path) and
	frame.f_code.co_name == '<module>'):
	self.current_line = frame.f_lineno
	return self.trace_function

	@contextmanager
	def trace_execution(self):
	"""Context manager to trace execution and capture logs."""
	self.setup_tracing()
	self.setup_logging_patch()
	try:
	yield
	finally:
	self.restore_logging_patch()
	self.restore_tracing()
	self.write_output()

	def write_output(self):
	"""Write the traced output to file."""
	with open(self.output_path, 'w') as f:
	for line_num in range(1, len(self.script_lines) + 1):
	if line_num in self.line_logs:
	line_content = self.script_lines[line_num - 1].rstrip()
	f.write(f"{line_content}\n")

	for log_record in self.line_logs[line_num]:
	f.write(f"LOG:{log_record}\n")
	else:
	line_content = self.script_lines[line_num - 1].rstrip()
	f.write(f"{line_content}\n")


	def trace_script_execution(script_path, output_path):
	"""
	Trace the execution of a script and capture log messages for each line.

	Args:
	script_path: Path to the script to trace
	output_path: Path to write the output file
	"""
	script_path = os.path.abspath(script_path)
	tracer = LineTracer(script_path, output_path)

	with tracer.trace_execution():
	try:
	runpy.run_path(script_path, run_name='__main__')
	except Exception as e:
	tracer.clean_traceback(type(e), e, e.__traceback__)
	sys.exit(1)


	def main():
	"""Main function for command line usage."""
	import argparse

	parser = argparse.ArgumentParser(description='Trace script execution and capture logs per line')
	parser.add_argument('script', help='Path to the script to trace')
	parser.add_argument('-o', '--output', default='traced_output.txt',
	help='Path to write the output file (default: traced_output.txt)')

	args = parser.parse_args()

	trace_script_execution(args.script, args.output)


	if __name__ == "__main__":
	main()