# Path: imusim/maths/kalman.py
# class KalmanFilter(object):
# class UnscentedKalmanFilter(KalmanFilter):
#     def __init__(self, stateTransitionMatrix, controlMatrix, measurementMatrix,
#             state, stateCovariance, processCovariance, measurementCovariance):
#     def state(self):
#     def state(self, state):
#     def stateTransitionMatrix(self):
#     def stateTransitionMatrix(self, stateTransitionMatrix):
#     def controlMatrix(self):
#     def controlMatrix(self, controlMatrix):
#     def measurementMatrix(self):
#     def measurementMatrix(self, measurementMatrix):
#     def stateCovariance(self):
#     def stateCovariance(self, stateCovariance):
#     def processCovariance(self):
#     def processCovariance(self, processCovariance):
#     def measurementCovariance(self):
#     def measurementCovariance(self, measurementCovariance):
#     def gain(self):
#     def predict(self, control=None):
#     def innovation(self, measurement):
#     def correct(self, measurement):
#     def update(self, measurement, control=None):
#     def normalisedInnovation(self, measurement):
#     def __init__(self, stateUpdateFunction, measurementFunction, state,
#             stateCovariance, processCovariance, measurementCovariance):
#     def stateUpdateFunction(self):
#     def stateUpdateFunction(self, function):
#     def measurementFunction(self):
#     def measurementFunction(self, function):
#     def predict(self, control=None):
#     def innovation(self, measurement):
#     def correct(self, measurement):
#             B = 0


import numpy as np
    import pylab
from imusim.maths import kalman
from numpy import testing
        ]
initialStates = [
        np.zeros((1,1)),
        np.array([[1]]),
        np.array([[0],[0]])
        ]
initialCovariances = [
        np.array([[0.1]]),
        np.array([[0.5]]),
        0.2**2 * np.array([[dt**4/4,dt**3/2],[dt**3/2, dt**2]])
        ]
processCovariances = [
        np.array([[0.01]]),
        np.array([[0.05]]),
        0.2**2 * np.array([[dt**4/4,dt**3/2],[dt**3/2, dt**2]])
        ]
measurementCovariances = [
        np.array([[0.3]]),
        np.array([[0.2]]),
        np.array([[10]])
        ]

def checkKalmanFilter(stateTransition, measurement, control, initialState,
        initialCovariance, processCovariance, measurementCovariance,
        controlFunction):

    states = initialState.shape[0]
    measurements = measurement.shape[0]
    controls = control.shape[1]


# Path: imusim/maths/kalman.py
# class KalmanFilter(object):
# class UnscentedKalmanFilter(KalmanFilter):
#     def __init__(self, stateTransitionMatrix, controlMatrix, measurementMatrix,
#             state, stateCovariance, processCovariance, measurementCovariance):
#     def state(self):
#     def state(self, state):
#     def stateTransitionMatrix(self):
#     def stateTransitionMatrix(self, stateTransitionMatrix):
#     def controlMatrix(self):
#     def controlMatrix(self, controlMatrix):
#     def measurementMatrix(self):
#     def measurementMatrix(self, measurementMatrix):
#     def stateCovariance(self):
#     def stateCovariance(self, stateCovariance):
#     def processCovariance(self):
#     def processCovariance(self, processCovariance):
#     def measurementCovariance(self):
#     def measurementCovariance(self, measurementCovariance):
#     def gain(self):
#     def predict(self, control=None):
#     def innovation(self, measurement):
#     def correct(self, measurement):
#     def update(self, measurement, control=None):
#     def normalisedInnovation(self, measurement):
#     def __init__(self, stateUpdateFunction, measurementFunction, state,
#             stateCovariance, processCovariance, measurementCovariance):
#     def stateUpdateFunction(self):
#     def stateUpdateFunction(self, function):
#     def measurementFunction(self):
#     def measurementFunction(self, function):
#     def predict(self, control=None):
#     def innovation(self, measurement):
#     def correct(self, measurement):
#             B = 0


# Path: imusim/tests/maths/kalman_test.py
import numpy as np
    import pylab
from imusim.maths import kalman
from numpy import testing

        ]
initialStates = [
        np.zeros((1,1)),
        np.array([[1]]),
        np.array([[0],[0]])
        ]
initialCovariances = [
        np.array([[0.1]]),
        np.array([[0.5]]),
        0.2**2 * np.array([[dt**4/4,dt**3/2],[dt**3/2, dt**2]])
        ]
processCovariances = [
        np.array([[0.01]]),
        np.array([[0.05]]),
        0.2**2 * np.array([[dt**4/4,dt**3/2],[dt**3/2, dt**2]])
        ]
measurementCovariances = [
        np.array([[0.3]]),
        np.array([[0.2]]),
        np.array([[10]])
        ]

def checkKalmanFilter(stateTransition, measurement, control, initialState,
        initialCovariance, processCovariance, measurementCovariance,
        controlFunction):

    states = initialState.shape[0]
    measurements = measurement.shape[0]
    controls = control.shape[1]


    filter = kalman.KalmanFilter(stateTransition, control,