lisa-gap Documentation
lisa-gap is a Python package for simulating planned and unplanned data gaps in LISA time series data.
The package provides tools for generating realistic gap masks that can be applied to LISA time series data. It supports both planned gaps (e.g., scheduled maintenance) and unplanned gaps (e.g., hardware failures) with configurable statistical distributions.
Built on top of lisaglitch, lisa-gap adds advanced windowing, proportional tapering, and data segmentation capabilities for frequency domain analysis.
Note
Original code developed by Eleonora Castelli (NASA Goddard) and adapted, packaged and enhanced by Ollie Burke (University of Glasgow).
Features
Generate realistic gap patterns for LISA time series
Support for both planned and unplanned gaps
Configurable gap rates and durations
Proportional tapering with automatic gap categorization
Extended lobe tapering for ultra-smooth transitions
Data segmentation with edge tapering for spectral analysis
Advanced windowing with boundary artifact prevention
Built on proven lisaglitch foundation
Save/load gap configurations to/from HDF5 files
Core Classes
GapMaskGenerator - Generate gap masks from statistical definitions
GapWindowGenerator - Apply proportional and traditional tapering
DataSegmentGenerator - Segment data and apply edge tapering for frequency analysis
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