2.1. OverviewΒΆ

In order to use pypfilt you will need to define the following things:

  • A simulation model that describes the behaviour of the system;
  • Prior distribution(s) and parameter bounds;
    • Specify independent priors for each parameter, using any method provided by the numpy.random.Generator class; or
    • Read correlated priors for multiple parameters from an external lookup table (see the How-to Guides for details).
  • Some observations of the system (either real or simulated from the simulation model);
  • An observation model that describes how the simulation model and the observations are related;
  • Simulation and particle filter settings, such as:
    • Choose a time scale (either Scalar or Datetime);
    • Particle filter settings, such as the resampling threshold and whether to use post-regularisation;
    • Select appropriate summary tables to record things of interest;
    • The directory where input files are located;
    • The directory where output files should be written; and
    • A seed for the pseudo-random number generator, so that the results are reproducible.

All of this information is collected into a single TOML file, which pypfilt can then use to generate forecasts.

Note

TOML is a simple, easy to read configuration file format, similar to JSON and YAML.