andy
f08a1a9bf5
Python project for analyzing alarm data from building monitoring systems. Includes alarm analyzer, plotting, tests, and source data files.
54 lines
2.2 KiB
Python
54 lines
2.2 KiB
Python
import pandas as pd
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# Read the 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('C:\\Users\\AndrewConlon\\Documents\\AlarmAnalysis\\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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df = pd.read_excel('C:\\Users\\AndrewConlon\\Documents\\AlarmAnalysis\\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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df = pd.read_excel('C:\\Users\\AndrewConlon\\Documents\\AlarmAnalysis\\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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exit(1)
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print('Shape:', df.shape)
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print('Before forward-fill:')
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print('First 10 rows:')
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print(df[['ID', 'Group']].head(10))
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# Apply the same hierarchical processing as in the code
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df_processed = df.copy()
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hierarchical_cols = ['Group', 'Remote', 'Name', 'Type', 'Serial No']
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for col in hierarchical_cols:
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if col in df_processed.columns:
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# Forward fill: propagate non-null values down until the next non-null value
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df_processed[col] = df_processed[col].ffill()
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print()
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print('After forward-fill:')
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print('First 10 rows:')
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print(df_processed[['ID', 'Group']].head(10))
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# Check if sensor 7335 now has a group
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sensor_7335 = df_processed[pd.to_numeric(df_processed['ID'], errors='coerce') == 7335]
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if not sensor_7335.empty:
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print()
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print('Sensor 7335 after forward-fill:')
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print(sensor_7335[['ID', 'Group', 'Name']])
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else:
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print()
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print('Sensor 7335 not found in processed data')
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# Let's also check for all sensors that have ID 7335 in the original data
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original_sensor_7335 = df[pd.to_numeric(df['ID'], errors='coerce') == 7335]
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if not original_sensor_7335.empty:
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print()
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print('Sensor 7335 in original data:')
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print(original_sensor_7335[['ID', 'Group', 'Name']]) |