How to run pykalman Kalman Filter on a single observation? (python)

Question

I can run the simple pykalman Kalman Filter example given in the pykalman documentation:

import pykalman
import numpy as np
kf = pykalman.KalmanFilter(transition_matrices = [[1, 1], [0, 1]], observation_matrices = [[0.1, 0.5], [-0.3, 0.0]])
measurements = np.asarray([[1,0], [0,0], [0,1]])  # 3 observations
(filtered_state_means, filtered_state_covariances) = kf.filter(measurements)
print filtered_state_means

This correctly returns the state estimates (one for each observation):

[[ 0.07285974  0.39708561]
 [ 0.30309693  0.2328318 ]
 [-0.5533711  -0.0415223 ]]

However, if I provide only a single observation, the code fails:

import pykalman
import numpy as np
kf = pykalman.KalmanFilter(transition_matrices = [[1, 1], [0, 1]], observation_matrices = [[0.1, 0.5], [-0.3, 0.0]])
measurements = np.asarray([[1,0]])  # 1 observation
(filtered_state_means, filtered_state_covariances) = kf.filter(measurements)
print filtered_state_means

with the following error:

ValueError: could not broadcast input array from shape (2,2) into shape (2,1)

How can I use pykalman to update an initial state and initial covariance using just a single observation?

Answer 1

From the documentation at: http://pykalman.github.io/#kalmanfilter

filter_update(filtered_state_mean, filtered_state_covariance, observation=None, transition_matrix=None, transition_offset=None, transition_covariance=None, observation_matrix=None, observation_offset=None, observation_covariance=None)

This takes in the filtered_state_mean and filtered_state_covariance at time t, and an observation at t+1, and returns the state mean and state covariance at t+1 (to be used for the next update)