feat: implement comprehensive alarm logic system with Nelson Rules and advanced detection methods

[xzxjesse]

Summary

This PR implements a comprehensive alarm logic system for BibMon, including all 8 classical Nelson Rules for statistical process control and advanced anomaly detection methods.

Implemented Features

Nelson Rules (1-8)

• Rule 1: Detection of one point above 3 standard deviations from the mean
• Rule 2: Detection of nine consecutive points on the same side of the mean
• Rule 3: Detection of six consecutive points in trend (increasing or decreasing)
• Rule 4: Detection of fourteen points alternating above and below the mean
• Rule 5: Detection of two out of three consecutive points above 2 sigmas
• Rule 6: Detection of four out of five consecutive points above 1 sigma
• Rule 7: Detection of fifteen consecutive points within 1 sigma
• Rule 8: Detection of eight consecutive points outside 1 sigma

Advanced Detection Methods

• Drift/Bias Detection: Detection of trends and shifts in time series data
• Variance Change Detection: Detection of sudden changes in data variance
• Outlier Frequency Change: Detection of changes in outlier frequency

Technical Implementation

• All functions follow the established pattern in the _alarms.py module
• Robust implementation with special case handling
• Complete documentation with NumPy-style docstrings
• Comprehensive unit tests for each functionality
• Compatibility with existing BibMon architecture

Benefits

• Enhanced monitoring capabilities for BibMon
• More precise detection of different types of anomalies
• Flexibility for different industrial scenarios
• Reliability through comprehensive unit tests

Testing

• 11 new unit tests implemented
• Complete functionality coverage
• Robust and realistic test data
• Validation of extreme cases and edge cases

Impact

This implementation resolves issue #39, significantly expanding BibMon's anomaly detection capabilities, making it more suitable for monitoring complex industrial processes.

Modified Files

• bibmon/_alarms.py: Implementation of detection functions
• test/test_tools.py: Unit tests for all functionalities

Development Methodology

Development following TDD (Test-Driven Development), ensuring code quality and reliability of the implemented features.

Code Quality

• All functions are production-ready with proper error handling
• Consistent coding style following project standards
• Comprehensive documentation for maintainability
• Efficient algorithms optimized for performance

Compatibility

• Maintains backward compatibility with existing BibMon functionality
• No breaking changes to existing APIs
• Seamless integration with current alarm system architecture