Munge functionality for the particle filter. #286
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Includes ForecastSimulator, ParticleFilterSimulator, and EnsembleKalmanFilterSimulator as top-level interfaces.
…naries of unknown parameters to have consistent usage of keys when iterating over them.
…hood with an `EnsembleKalmanFilterSimulator`.
… likelihood in terms of standard deviation for consistency. Added and updated documentation and annotations.
…naries of unknown parameters to have consistent usage of keys when iterating over them.
…hood with an `EnsembleKalmanFilterSimulator`.
… likelihood in terms of standard deviation for consistency. Added and updated documentation and annotations.
…/Epymorph into Forecasting-Code-Cleanup
modified: epymorph/forecasting/parameter_strategy.py
…valued hyperparameters.
…e "realizations" as the first dimension.
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I added a munge function to operate on both The average runtime of |
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This PR adds munge functionality for
PipelineOutput.PipelineOutputRealizationStrategyParameterStrategymunge_pipeline_outputRealizationSelectorandParameterSelectorare properties ofPipelineOutputwhereasthe other selectors are still properties of the rume.
Example usage
``` realization` = particle_filter_output.select.all()
time = rume.time_frame.select.all().group("day").agg()
geo = rume.scope.select.all()
quantity = particle_filter_output.param_select.by_name("log_beta")
df = munge_pipeline_output(particle_filter_output,realization,geo,time,quantity) ```