Plugins

3ML is based on a plugin system. This means that For each instrument/datum, there is a plugin that holds the data, reads a model, and returns a likelihood. This is how we achieve the multi-messenger paradigm. A plugin handles its likelihood call internally and the likelhoods are combined within 3ML during a fit.

Contents:

• Building Custom Plugins
  • The PluginPrototype class
    • Basic Properties
      • name
      • nuisance parameters
    • Unique Properties
      • set_model
      • get_log_like
      • inner_fit
  • Making a custom plugin
• Spectrum Plugins
  • SpectrumLike
    • The count spectrum
      • Selection
    • Rebinning
    • Fitting
  • DispersionSpectrumLike
  • OGIPLike
• Constructing plugins from TimeSeries
  • Constructing time series objects from different data types
    • GBM Data
    • LAT LLE data
  • Viewing Lightcurves and selecting source intervals
  • Fitting a polynomial background
    • What occurs during a fit?
  • Saving the background fit
  • Creating a plugin
  • Time-resolved binning and plugin creation
    • Constant Cadence
    • Custom
    • Significance
    • Bayesian Blocks
    • Working with bins
• Background Modeling
  • Using a profile likelihood
  • Modeling the background
• Photometric Plugin
  • Setup
    • Simple example of building a filter
  • 3ML filter library
  • Build your own filters
  • GROND Example
    • Model specification
• HAL (HAWC Accelerated Likelihood) plugin
  • Download
  • Exploratory analysis
  • Simple model fit
  • Assessing the Fit Quality
  • Visualizing the Fit Results