pygwb 0.2

Lighweight python stochastic GWB analysis pipeline

pygwb

Documentation

Installation instructions

• Essentials to support pygwb are present in live igwn conda environments https://computing.docs.ligo.org/conda/

• More precisely, current dependencies are

  • numpy
  • scipy==1.8.0
  • matplotlib
  • corner
  • gwpy==3.0.1
  • bilby
  • astropy>=4.3.0
  • lalsuite==7.3
  • loguru
  • json5
  • jinja2==3.0.3
  • seaborn

  Modules

  The code is structured into many small modules.

  • pre-processing.py applies initial data-conditioning steps (high-pass filter and downsampling) on data from individual detector. Also supports importing simualted data.
  • spectral.py calculated CSDs and PSDs for each segment in a job (a coincident time segment of a pair of detectors)
  • postprocessing.py combines the cross-correlation spectrograms into a final spectrum.
  • pe.py defines classes to perform pe with bilby for various models.
  • constants.py contains numerical values of constants used throughout the code. Constants should never be redefined elsewhere.
  • util.py contains miscellaneous useful classes and functionality.

  Scripts

  A set of scripts are supported to run every-day stochastic tasks.

  • pygwb_pipe runs the cross-correlation stochastic analysis over data from selected detector pair, within the timeframes requested.
  • pygwb_combine combines over multiple pygwb_pipe output files. Useful when running long analyses in parallel.
  • pygwb_pe runs parameter estimation on desired parameters.
  • pygwb_stats produces regular statistical checks output.