Add python plotting script for raw data and anomalies

Author: patr1ck-m

plot_data.py

@@ -0,0 +1,172 @@
+"""
+Plot anomalies and raw data from CSV files.
+"""
+
+import pandas as pd
+import matplotlib.pyplot as plt
+from datetime import datetime
+import argparse
+
+
+def convert_timestamp(timestamp_ms):
+    """Converts Unix timestamp in milliseconds to a readable datetime format"""
+    timestamp_sec = timestamp_ms // 1000
+    dt = datetime.fromtimestamp(timestamp_sec)
+    return dt
+
+
+def plot_raw_data(csv_file, value_name='Value'):
+    """Plots raw data values"""
+    
+    print(f"Loading raw data from {csv_file}...")
+    df = pd.read_csv(csv_file)
+    
+    # Convert timestamps
+    df['datetime'] = df['timestamp'].apply(convert_timestamp)
+    
+    # Create plot
+    plt.figure(figsize=(14, 6))
+    plt.plot(df['datetime'], df['value'], marker='o', linestyle='-', 
+             markersize=3, linewidth=1, color='steelblue', label=value_name)
+    
+    # Formatting
+    plt.title(f'{value_name} - Raw Data Plot', fontsize=14, fontweight='bold')
+    plt.xlabel('Time', fontsize=12)
+    plt.ylabel(value_name, fontsize=12)
+    plt.grid(True, alpha=0.3)
+    plt.legend()
+    
+    # Rotate x-axis labels by 45 degrees for better readability
+    plt.xticks(rotation=45, ha='right')
+    plt.tight_layout()
+    
+    plt.show()
+
+
+def plot_anomalies(csv_file, value_name='Value', show_anomaly_values=False):
+    """Plots data with marked anomalies and background shading"""
+    
+    print(f"Loading anomaly data from {csv_file}...")
+    df = pd.read_csv(csv_file)
+    
+    # Convert timestamps
+    df['datetime'] = df['timestamp'].apply(convert_timestamp)
+    
+    # Convert boolean values (if needed)
+    df['isAnomaly'] = df['isAnomaly'].astype(bool)
+    
+    # Create plot
+    fig, ax = plt.subplots(figsize=(14, 6))
+    
+    # Find continuous anomaly regions
+    anomaly_regions = []
+    in_anomaly = False
+    start_idx = None
+    
+    for idx, row in enumerate(df.itertuples(index=False)):
+        if row.isAnomaly and not in_anomaly:
+            # Start of an anomaly region
+            in_anomaly = True
+            start_idx = idx
+        elif not row.isAnomaly and in_anomaly:
+            # End of an anomaly region
+            in_anomaly = False
+            anomaly_regions.append((start_idx, idx - 1))
+    
+    # Handle case where data ends with an anomaly
+    if in_anomaly:
+        anomaly_regions.append((start_idx, len(df) - 1))
+    
+    # Add red background for anomaly regions
+    for start_idx, end_idx in anomaly_regions:
+        start_time = df.iloc[start_idx]['datetime']
+        end_time = df.iloc[end_idx]['datetime']
+        ax.axvspan(start_time, end_time, alpha=0.2, color='red', zorder=0)
+    
+    # Plot all values as continuous line
+    ax.plot(df['datetime'], df['value'], marker='o', linestyle='-', 
+            markersize=3, linewidth=1, color='steelblue', label=value_name, zorder=2)
+    
+    # Color anomaly points differently
+    anomalies = df[df['isAnomaly']]
+    ax.scatter(anomalies['datetime'], anomalies['value'], 
+              color='red', s=80, marker='o', label='Anomaly', zorder=3)
+    
+    # Optionally plot anomaly scores
+    if show_anomaly_values:
+        # Create secondary y-axis for anomaly scores
+        ax2 = ax.twinx()
+        
+        # Plot anomaly score as line with markers
+        ax2.plot(df['datetime'], df['anomalyScore'], marker='s', linestyle='--', 
+                markersize=3, linewidth=1, color='orange', label='Anomaly Score', zorder=2, alpha=0.7)
+        
+        # Format secondary axis
+        ax2.set_ylabel('Anomaly Score', fontsize=12, color='orange')
+        ax2.tick_params(axis='y', labelcolor='orange')
+        
+        # Combine legends from both axes
+        lines1, labels1 = ax.get_legend_handles_labels()
+        lines2, labels2 = ax2.get_legend_handles_labels()
+        ax.legend(lines1 + lines2, labels1 + labels2, loc='upper left')
+    
+    # Formatting
+    ax.set_title(f'{value_name} with Anomaly Detection', fontsize=14, fontweight='bold')
+    ax.set_xlabel('Time', fontsize=12)
+    ax.set_ylabel(value_name, fontsize=12)
+    ax.grid(True, alpha=0.3)
+    
+    if not show_anomaly_values:
+        ax.legend()
+    
+    # Rotate x-axis by 45 degrees
+    plt.xticks(rotation=45, ha='right')
+    plt.tight_layout()
+    
+    plt.show()
+
+
+def main():
+    """Main function with argument parser"""
+    
+    parser = argparse.ArgumentParser(
+        description='Plots data from CSV files',
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+Examples:
+  python plot_data.py --raw cpu_usage_raw.csv --value-name "CPU Usage"
+  python plot_data.py --anomalies cpu_anomalies.csv --value-name "CPU Usage"
+  python plot_data.py --anomalies cpu_anomalies.csv --value-name "CPU Usage" --show-anomaly-values
+  python plot_data.py --raw data.csv --value-name "Temperature"
+        """
+    )
+    
+    # Define arguments
+    parser.add_argument('--raw', type=str, metavar='FILE',
+                       help='Plots raw data')
+    parser.add_argument('--anomalies', type=str, metavar='FILE',
+                       help='Plots data with anomaly detection')
+    parser.add_argument('--value-name', type=str, metavar='NAME', default='Value',
+                       help='Name of the values being plotted (e.g., CPU Usage, Memory, Temperature)')
+    parser.add_argument('--show-anomaly-values', action='store_true',
+                       help='Show anomaly scores on a secondary y-axis')
+    
+    args = parser.parse_args()
+    
+    # At least one option must be chosen
+    if not args.raw and not args.anomalies:
+        print("Error: At least one of --raw or --anomalies must be specified")
+        print()
+        parser.print_help()
+        return
+    
+    # Plot data
+    if args.raw:
+        plot_raw_data(args.raw, args.value_name)
+    
+    if args.anomalies:
+        plot_anomalies(args.anomalies, args.value_name, args.show_anomaly_values)
+
+
+if __name__ == "__main__":
+    main()