andy
f08a1a9bf5
Python project for analyzing alarm data from building monitoring systems. Includes alarm analyzer, plotting, tests, and source data files.
74 lines
3.0 KiB
Python
74 lines
3.0 KiB
Python
#!/usr/bin/env python
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# Script to check the ID mapping between alarm data and sensor report
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import pandas as pd
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import numpy as np
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def check_mapping():
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print("Loading alarm data...")
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alarm_df = pd.read_csv('CardinalAlarmsDec25.csv')
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print("Loading sensor report...")
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# Try to read with header=0 first (new format) then with header=4 (old format)
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try:
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temp_df = pd.read_excel('SensorReport Cardinal 2025-12-23_processed.xlsx', header=0, nrows=5)
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expected_cols = ['ID', 'Remote', 'Group', 'Type', 'Serial No', 'Name']
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has_expected_cols = any(col in temp_df.columns for col in expected_cols)
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if has_expected_cols:
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sensor_df = pd.read_excel('SensorReport Cardinal 2025-12-23_processed.xlsx', header=0)
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print("Using new sensor report format (header=0)")
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else:
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sensor_df = pd.read_excel('SensorReport Cardinal 2025-12-23_processed.xlsx', header=4)
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print("Using old sensor report format (header=4)")
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except FileNotFoundError:
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print("Sensor report file not found. Please ensure 'SensorReport Cardinal 2025-12-23_processed.xlsx' is in the current directory.")
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return
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print(f"Alarm data shape: {alarm_df.shape}")
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print(f"Sensor report shape: {sensor_df.shape}")
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print("\nAlarm data Sensor_Id sample (first 10):")
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print(alarm_df['Sensor_Id'].head(10).tolist())
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print("\nSensor report columns:")
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print(sensor_df.columns.tolist())
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print("\nSensor report 'Remote SN' column info:")
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print(f"Data type: {sensor_df['Remote SN'].dtype}")
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print(f"Sample values (first 10): {sensor_df['Remote SN'].head(10).tolist()}")
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print(f"Non-null count: {sensor_df['Remote SN'].notna().sum()}")
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# Check for potential matches
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alarm_sensors = set(alarm_df['Sensor_Id'].unique())
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# Clean the Remote SN column to find valid numeric values
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valid_remote_sns = []
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for sn in sensor_df['Remote SN'].dropna():
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try:
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# Try to convert to int
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valid_remote_sns.append(int(sn))
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except (ValueError, TypeError):
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print(f"Could not convert to int: {sn}")
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continue
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sensor_sns = set(valid_remote_sns)
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print(f"\nNumber of unique alarm sensors: {len(alarm_sensors)}")
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print(f"Number of valid sensor report IDs: {len(sensor_sns)}")
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print(f"Common IDs between datasets: {len(alarm_sensors.intersection(sensor_sns))}")
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if len(alarm_sensors.intersection(sensor_sns)) > 0:
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print(f"Sample common IDs: {list(alarm_sensors.intersection(sensor_sns))[:10]}")
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else:
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print("No direct matches found. Let's check other potential ID columns in sensor report...")
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# Check other columns that might contain IDs
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for col in sensor_df.columns:
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if col != 'Remote SN':
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print(f"\nChecking column: {col}")
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non_null_values = sensor_df[col].dropna().head(10).tolist()
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print(f"Sample values: {non_null_values}")
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if __name__ == "__main__":
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check_mapping() |