bilby_pipe
1.6.1.dev2+g5386373

Contents:

  • Installation
  • Using bilby_pipe
  • Examples
  • The ini file
  • Injections
  • Customising data analysis
  • Code overview
  • Singularity
  • API Reference
bilby_pipe
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bilby_pipe

bilby_pipe is a python3 tool for automating the process of running bilby for gravitational-wave parameter estimation on computing clusters.

Contents:

  • Installation
    • Installing bilby_pipe from release
    • Install bilby_pipe for development
      • Python installation
      • Installing bilby_pipe
    • Dependencies
  • Using bilby_pipe
    • Summary webpage
    • bilby_pipe help
      • Positional Arguments
      • Named Arguments
      • Calibration arguments
      • Data generation arguments
      • Detector arguments
      • Injection arguments
      • Job submission arguments
      • Likelihood arguments
      • Output arguments
      • Prior arguments
      • Post processing arguments
      • Sampler arguments
      • Waveform arguments
      • Global settings
  • Examples
    • Trigger time
    • Running on GPS times
    • Running on injections in Gaussian noise
    • Running on GraceDB events
      • Using GraceDB ID vs GraceDB event file
      • Selecting GraceDB-url
      • Output methods
  • The ini file
  • Injections
    • Injection files
    • Generating injection files
    • General tips
      • No injection file
      • Interaction with n-simulation
      • Interaction with gps-times or gps-tuple
      • Specifying a subset of injections
      • Specifying the injection waveform
      • XML files
  • Customising data analysis
  • Code overview
    • User interface
    • Python Modules
    • Workflow
    • Code Format
    • Submodules API
      • Main
      • Data Generation
      • Data Analysis
      • Creating an injection set
      • GraceDB
      • Utils
  • Singularity
    • Obtaining a singularity image
    • Using a singularity image
    • Building a custom container
  • API Reference
    • bilby_pipe
      • Submodules
      • Attributes
      • Package Contents
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© Copyright 2020, Greg Ashton.

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