from .base import Property
from ..models.transition import TransitionModel
from .kalman import KalmanPredictor
from ..types.prediction import Prediction
[docs]class EnsemblePredictor(KalmanPredictor):
r"""Ensemble Kalman Filter Predictor class
The EnKF predicts the state by treating each column of the ensemble matrix
as a state vector. The state is propagated through time by applying the
transition function to each member (vector) of the ensemble.
.. math::
\hat{X}_k = [f(x_1), f(x_2), ..., f(x_M)]
"""
transition_model: TransitionModel = Property(doc="The transition model to be used.")
[docs] def predict(self, prior, timestamp=None, **kwargs):
"""Ensemble Kalman Filter prediction step
Parameters
----------
prior : :class:`~.EnsembleState`
A prior state object
control_input : :class:`~.State`, optional
The control input. It will only have an effect if
:attr:`control_model` is not `None` (the default is `None`)
timestamp: :class:`datetime.datetime`, optional
A timestamp signifying when the prediction is performed
(the default is `None`)
Returns
-------
: :class:`~.EnsembleStatePrediction`
The predicted state
"""
# Compute time_interval
time_interval = self._predict_over_interval(prior, timestamp)
# This block of code propagates each column through the transition model.
pred_ensemble = self.transition_model.function(
prior, noise=True, time_interval=time_interval)
return Prediction.from_state(prior, pred_ensemble, timestamp=timestamp,
transition_model=self.transition_model)