andy
f08a1a9bf5
Python project for analyzing alarm data from building monitoring systems. Includes alarm analyzer, plotting, tests, and source data files.
81 lines
3.4 KiB
Python
81 lines
3.4 KiB
Python
#!/usr/bin/env python
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# Script to verify the enhanced group statistics
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import pandas as pd
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import os
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def check_enhanced_group_stats():
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print("=== ENHANCED GROUP STATISTICS VERIFICATION ===")
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print()
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# Check if output directory exists
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if not os.path.exists('output'):
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print("Output directory not found!")
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return
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# Check if group_statistics.csv exists
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group_stats_path = os.path.join('output', 'group_statistics.csv')
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if not os.path.exists(group_stats_path):
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print(f"Group statistics file not found at {group_stats_path}")
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return
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# Load the enhanced group statistics
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group_stats_df = pd.read_csv(group_stats_path)
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print("Enhanced Group Statistics Columns:")
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print(list(group_stats_df.columns))
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print()
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# Verify the new columns exist
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required_columns = [
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'Total_Sensors_In_Group',
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'Percentage_Monitoring_Points_Alarmed',
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'Alarm_Time_Percentage'
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]
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missing_columns = [col for col in required_columns if col not in group_stats_df.columns]
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if missing_columns:
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print(f"ERROR: Missing columns: {missing_columns}")
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return
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else:
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print("All required enhanced columns are present")
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print()
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# Display sample of the enhanced data
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print("Sample of Enhanced Group Statistics (Top 10 by Alarm Count):")
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print(group_stats_df[['Sensor_Group', 'Total_Alarm_Count', 'Unique_Sensors',
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'Total_Sensors_In_Group', 'Percentage_Monitoring_Points_Alarmed',
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'Alarm_Time_Percentage']].head(10))
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print()
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# Show some key statistics
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print("=== ENHANCED ANALYSIS SUMMARY ===")
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# Groups with highest percentage of monitoring points alarmed
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print("Top 5 groups with highest percentage of monitoring points that experienced alarms:")
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top_alarm_percent = group_stats_df.nlargest(5, 'Percentage_Monitoring_Points_Alarmed')[['Sensor_Group', 'Percentage_Monitoring_Points_Alarmed', 'Unique_Sensors', 'Total_Sensors_In_Group']]
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print(top_alarm_percent)
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print()
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# Groups with highest alarm time percentage
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print("Top 5 groups with highest percentage of time spent in alarm condition:")
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top_time_percent = group_stats_df.nlargest(5, 'Alarm_Time_Percentage')[['Sensor_Group', 'Alarm_Time_Percentage', 'Total_Duration', 'Total_Sensors_In_Group']]
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print(top_time_percent)
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print()
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# Groups with the most difference between total sensors and unique sensors that alarmed
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print("Groups with the highest number of total sensors but lower alarm activity:")
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group_stats_df['Sensors_Not_Alarming'] = group_stats_df['Total_Sensors_In_Group'] - group_stats_df['Unique_Sensors']
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top_inactive = group_stats_df.nlargest(5, 'Sensors_Not_Alarming')[['Sensor_Group', 'Sensors_Not_Alarming', 'Total_Sensors_In_Group', 'Unique_Sensors', 'Percentage_Monitoring_Points_Alarmed']]
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print(top_inactive)
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print()
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print("Enhanced group statistics analysis completed successfully!")
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print()
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print("New metrics added:")
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print("- Total_Sensors_In_Group: Total number of sensors in the group according to sensor report")
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print("- Percentage_Monitoring_Points_Alarmed: Percentage of sensors in the group that experienced alarms")
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print("- Alarm_Time_Percentage: Percentage of total possible sensor-time that was spent in alarm condition")
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if __name__ == "__main__":
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check_enhanced_group_stats() |