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.