API documentation¶
The pypfilt module provides top-level functions for running forecasts
and simulating observations from simulation models:
load_instances()Iterate over scenario instances for each scenario.
forecast()Generate forecasts at specific times for a single scenario.
fit()Fit the simulation model to all of the available observations.
adaptive_fit()Fit the simulation model to all of the available observations in a series of passes, where in each pass the observation models are refined.
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 |
|---|---|
Provides model-fitting and forecasting functions |
|
Reads forecast scenarios from TOML files |
|
Defines the simulation model base class
|
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Defines the observation model base class
|
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Provides scalar and date-time simulation time scales |
|
Provides common summary statistics and records outputs |
|
Provides functions for plotting summary statistics |
|
Reads data tables from text files |
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Calculates CRPS scores for simulated observations |
|
Provides example models |
Module |
Description |
|---|---|
Implements adaptive fitting methods |
|
Builds simulation contexts |
|
Implements the particle filter state cache |
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The particle filter core: time-steps and adjusting particle weights |
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Implements particle resampling and post-regularisation |
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Construct sampling functions for each model parameter |
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Creates the state history matrix |
|
Calculates weighted quantiles, credible intervals, etc |
Key modules