5.1. pypfilt

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

pypfilt.forecasts_iter(*config_files)

Iterate over the forecast settings for each scenario defined in the provided configuration file(s).

Parameters:config_files (str) – The forecast configuration file(s).
Return type:Iterator[pypfilt.sweep.Forecasts]
Examples: 
>>> import pypfilt
>>> import pypfilt.examples.predation
>>> pypfilt.examples.predation.write_example_files()
>>> forecast_times = [1.0, 3.0, 5.0, 7.0, 9.0]
>>> config_file = 'predation.toml'
>>> data_file = 'output.hdf5'
>>> for forecast in pypfilt.forecasts_iter(config_file):
...     state = pypfilt.forecast(forecast.params,
...                              forecast.observation_streams,
...                              forecast_times, filename=data_file)
pypfilt.forecast(params, streams, dates, filename)

Generate forecasts from various dates during a simulation.

Parameters:
  • params (Union[dict, pypfilt.Context]) – The simulation parameters, or a simulation context.
  • streams – A list of observation streams.
  • dates – The dates at which forecasts should be generated.
  • filename – The output file to generate (can be None).
Returns:

The simulation state for each forecast date.
pypfilt.fit(params, streams, filename)

Run a single estimation pass over the entire simulation period.

Parameters:
  • params (Union[dict, pypfilt.Context]) – The simulation parameters, or a simulation context.
  • streams – A list of observation streams.
  • filename – The output file to generate (can be None).
Returns:

The simulation state for the estimation pass.
Examples: 
>>> import pypfilt
>>> import pypfilt.examples.predation
>>> pypfilt.examples.predation.write_example_files()
>>> config_file = 'predation.toml'
>>> data_file = 'output.hdf5'
>>> for forecast in pypfilt.forecasts_iter(config_file):
...     state = pypfilt.fit(forecast.params, forecast.observation_streams,
...                         filename=data_file)
pypfilt.simulate_from_model(params, px_count=None)

Simulate observations from a model.

Parameters:params – The simulation parameters.
Returns:A table of simulated observations.
Return type:numpy.ndarray
Examples: 
>>> import pypfilt
>>> import pypfilt.examples.predation
>>> pypfilt.examples.predation.write_example_files()
>>> config_file = 'predation.toml'
>>> for forecast in pypfilt.forecasts_iter(config_file):
...     sim_obs = pypfilt.simulate_from_model(forecast.params, px_count=1)
...     print(sim_obs[['date', 'unit', 'value']][:4])
[(0., 'x', 1.33489946) (0., 'y', 0.07977887) (1., 'x', 1.93397389)
 (1., 'y', 0.58160109)]

The pypfilt module also re-exports a number of items from sub-modules: