2.8. Conclusion

Congratulations! You’ve used pypfilt to combine a simulation model with observations, generate forecasts, and plot the results.

You can now try modifying the observation files, adjusting the prior distributions, etc, and see how this affects the forecast predictions. Either modify the provided forecast scenario, or add new scenarios to the example TOML file. The following script, which reproduces all of the steps in this Getting Started guide, can be used as a starting point:

import pypfilt
import pypfilt.examples.predation

pypfilt.examples.predation.write_example_files()

config_file = 'predation.toml'
forecast_times = [1.0, 3.0, 5.0, 7.0, 9.0]
data_file = 'output.hdf5'

for forecast in pypfilt.forecasts_iter(config_file)
    pypfilt.forecast(forecast.params, forecast.observation_streams,
                     forecast_times, filename=data_file)

pypfilt.examples.predation.plot(data_file, png=True, pdf=False)

To learn how to use pypfilt with your own models and data, see Key Concepts and the How-to Guides.