Glows time flags

[laspsandoval]

This pull request introduces significant improvements to the GLOWS L1B data processing pipeline, focusing on more accurate computation and testing of histogram bad-time flags. The main changes include implementing a new compute_flags method for HistogramL1B, updating test data and test cases to align with the new flag logic, and adding utility functions for threshold extraction.

Enhancements to flag computation and logic:

• Added the compute_flags method to the HistogramL1B class to compute the 17 bad-time flags based on onboard and ground processing logic, using thresholds from PipelineSettings. This replaces the previous approach of setting all flags to 1 and introduces more granular checks for temperature, voltage, spin period, and pulse length standard deviations.
• Updated the __post_init__ method of HistogramL1B to use the new compute_flags method for initializing the flags attribute, ensuring flags are computed correctly at object creation.

Utility and helper functions:

• Added a standalone get_threshold function to extract threshold values from nested dictionaries based on suffix matching, used in flag computation.

Testing improvements:

• Updated the mock_pipeline_settings fixture to provide realistic flag values, reflecting the new flag computation logic.
• Enhanced the test_process_histogram and added a new test_compute_flags test to validate the new flag computation, including cases where threshold values are exceeded and flags are set to bad. [1] [2]
• Added a unit test for the get_threshold function to ensure correct extraction of threshold values from the settings dictionary.

These changes collectively improve the accuracy, maintainability, and test coverage of the GLOWS L1B data processing pipeline.

[laspsandoval]

There are two flags that we not well-defined in the Algorithm Document that I will need to come back to:

flag based on comparison of the total numbers of counts in a given block-accumulated histogram with the
daily average (deviations by n-sigma and more are interpreted as beyond daily statistical error), see the end of Sec. 10.2

external information on exceeding a background threshold (TBC)