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).
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 |
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 |