WORKAN WOOOO

chrony_statistics.py

@@ -1,103 +0,0 @@
-# stolen from https://austinsnerdythings.com/2025/02/14/revisiting-microsecond-accurate-ntp-for-raspberry-pi-with-gps-pps-in-2025/
-import pandas as pd
-import matplotlib.pyplot as plt
-from datetime import datetime
-
-def parse_chrony_stats(file_path):
-    """
-    Parse chrony statistics log file and return a pandas DataFrame
-    """
-    # read file contents first
-    with open(file_path, 'r') as f:
-        file_contents = f.readlines()
-
-    # for each line, if it starts with '=' or ' ', skip it
-    file_contents = [line for line in file_contents if not line.startswith('=') and not line.startswith(' ')]
-
-    # exclude lines that include 'PPS'
-    file_contents = [line for line in file_contents if 'PPS' not in line]
-
-    # Use StringIO to create a file-like object from the filtered contents
-    from io import StringIO
-    csv_data = StringIO(''.join(file_contents))
-
-    # Read the filtered data using pandas
-    df = pd.read_csv(csv_data,
-                     delim_whitespace=True,
-                     names=['Date', 'Time', 'IP_Address', 'Std_dev', 'Est_offset', 'Offset_sd', 
-                           'Diff_freq', 'Est_skew', 'Stress', 'Ns', 'Bs', 'Nr', 'Asym'])
-    
-
-    # Combine Date and Time columns into a datetime column
-    df['timestamp'] = pd.to_datetime(df['Date'] + ' ' + df['Time'])
-    
-    return df
-
-def plot_est_offset(df):
-    """
-    Create a plot of Est_offset vs time for each IP address
-    """
-    plt.figure(figsize=(12, 6))
-    
-    # Plot each IP address as a separate series
-    for ip in df['IP_Address'].unique():
-        ip_data = df[df['IP_Address'] == ip]
-        plt.plot(ip_data['timestamp'], ip_data['Est_offset'], 
-                marker='o', label=ip, linestyle='-', markersize=4)
-    
-    plt.xlabel('Time')
-    plt.ylabel('Estimated Offset (seconds)')
-    plt.title('Chrony Estimated Offset Over Time by IP Address')
-    plt.legend()
-    plt.grid(True)
-    
-    # Rotate x-axis labels for better readability
-    plt.xticks(rotation=45)
-    
-    # Adjust layout to prevent label cutoff
-    plt.tight_layout()
-    
-    return plt
-
-def analyze_chrony_stats(file_path):
-    """
-    Main function to analyze chrony statistics
-    """
-    # Parse the data
-    df = parse_chrony_stats(file_path)
-    
-    # Create summary statistics
-    summary = {
-        'IP Addresses': df['IP_Address'].nunique(),
-        'Time Range': f"{df['timestamp'].min()} to {df['timestamp'].max()}",
-        'Average Est Offset by IP': df.groupby('IP_Address')['Est_offset'].mean().to_dict(),
-        'Max Est Offset by IP': df.groupby('IP_Address')['Est_offset'].max().to_dict(),
-        'Min Est Offset by IP': df.groupby('IP_Address')['Est_offset'].min().to_dict(),
-        'Median Est Offset by IP': df.groupby('IP_Address')['Est_offset'].median().to_dict()
-    }
-    
-    # Create the plot
-    plot = plot_est_offset(df)
-    
-    return df, summary, plot
-
-# Example usage
-if __name__ == "__main__":
-    file_path = "chrony_statistics.log"  # Replace with your file path
-    df, summary, plot = analyze_chrony_stats(file_path)
-    
-    # Print summary statistics
-    print("\nChrony Statistics Summary:")
-    print("-" * 30)
-    print(f"Number of IP Addresses: {summary['IP Addresses']}")
-    print(f"Time Range: {summary['Time Range']}")
-    print("\nAverage Estimated Offset by IP:")
-    for ip, avg in summary['Average Est Offset by IP'].items():
-        print(f"{ip}: {avg:.2e}")
-
-    print("\nMedian Estimated Offset by IP:")
-    for ip, median in summary['Median Est Offset by IP'].items():
-        print(f"{ip}: {median:.2e}")
-    
-    # Show the plot
-    plt.show()