5. API documentation¶

The pypfilt module provides top-level functions for running forecasts and simulating observations from simulation models:

forecasts_iter(): Iterate over forecast settings for each scenario.
forecast(): Generate forecasts at specific times for a single scenario.
fit(): Fit the simulation model to all of the available observations.
simulate_from_model(): Simulate observations from the simulation model, according to each observation model.

It also contains a number of sub-modules. Some are intended for public use (see the key modules table), while others are likely of no use outside of pypfilt (see the secondary modules table).

Key pypfilt modules.¶
Module	Description
`pypfilt`	Provides model-fitting and forecasting functions
`pypfilt.config`	Reads forecast scenarios from TOML files
`pypfilt.sweep`	Iterates over forecast scenarios
`pypfilt.model`	Defines the simulation model base class `Model`
`pypfilt.obs`	Defines the observation model base class `Obs`
`pypfilt.params`	Provides default parameter values for the particle filter
`pypfilt.time`	Provides scalar and date-time simulation time scales
`pypfilt.summary`	Provides common summary statistics and records outputs
`pypfilt.plot`	Provides functions for plotting summary statistics
`pypfilt.io`	Reads data tables from text files
`pypfilt.examples`	Provides example models

Secondary pypfilt modules, which you will rarely (if ever) use directly.¶
Module	Description
`pypfilt.cache`	Implements the particle filter state cache
`pypfilt.check`	Provides invariant checks for the state history matrix
`pypfilt.context`	Manages simulation components, parameters, event handlers, etc.
`pypfilt.pfilter`	The particle filter core: time-steps and adjusting particle weights
`pypfilt.resample`	Implements particle resampling and post-regularisation
`pypfilt.state`	Creates the state history matrix
`pypfilt.stats`	Calculates weighted quantiles, credible intervals, etc

Secondary modules