def update(x, P, z, R, H=None, return_all=False):
    """
    Add a new measurement (z) to the Kalman filter. If z is None, nothing
    is changed.

    This can handle either the multidimensional or unidimensional case. If
    all parameters are floats instead of arrays the filter will still work,
    and return floats for x, P as the result.

    update(1, 2, 1, 1, 1)  # univariate
    update(x, P, 1



    Parameters
    ----------

    x : numpy.array(dim_x, 1), or float
        State estimate vector

    P : numpy.array(dim_x, dim_x), or float
        Covariance matrix

    z : (dim_z, 1): array_like
        measurement for this update. z can be a scalar if dim_z is 1,
        otherwise it must be convertible to a column vector.

    R : numpy.array(dim_z, dim_z), or float
        Measurement noise matrix

    H : numpy.array(dim_x, dim_x), or float, optional
        Measurement function. If not provided, a value of 1 is assumed.

    return_all : bool, default False
        If true, y, K, S, and log_likelihood are returned, otherwise
        only x and P are returned.

    Returns
    -------

    x : numpy.array
        Posterior state estimate vector

    P : numpy.array
        Posterior covariance matrix

    y : numpy.array or scalar
        Residua. Difference between measurement and state in measurement space

    K : numpy.array
        Kalman gain

    S : numpy.array
        System uncertainty in measurement space

    log_likelihood : float
        log likelihood of the measurement
    """

    #pylint: disable=bare-except

    if z is None:
        if return_all:
            return x, P, None, None, None, None
        return x, P

    if H is None:
        H = np.array([1])

    if np.isscalar(H):
        H = np.array([H])

    Hx = np.atleast_1d(dot(H, x))
    z = reshape_z(z, Hx.shape[0], x.ndim)

    # error (residual) between measurement and prediction
    y = z - Hx

    # project system uncertainty into measurement space
    S = dot(dot(H, P), H.T) + R


    # map system uncertainty into kalman gain
    try:
        K = dot(dot(P, H.T), linalg.inv(S))
    except:
        # can't invert a 1D array, annoyingly
        K = dot(dot(P, H.T), 1./S)


    # predict new x with residual scaled by the kalman gain
    x = x + dot(K, y)

    # P = (I-KH)P(I-KH)' + KRK'
    KH = dot(K, H)

    try:
        I_KH = np.eye(KH.shape[0]) - KH
    except:
        I_KH = np.array([1 - KH])
    P = dot(dot(I_KH, P), I_KH.T) + dot(dot(K, R), K.T)


    if return_all:
        # compute log likelihood
        log_likelihood = logpdf(z, dot(H, x), S)
        return x, P, y, K, S, log_likelihood
    return x, P
def update ( x , P , z , R , H = None , return_all = False ) : if z is None : if return_all : return x , P , None , None , None , None return x , P if H is None : H = np . array ( [ 1 ] ) if np . isscalar ( H ) : H = np . array ( [ H ] ) Hx = np . atleast_1d ( dot ( H , x ) ) z = reshape_z ( z , Hx . shape [ 0 ] , x . ndim ) y = z - Hx S = dot ( dot ( H , P ) , H . T ) + R try : K = dot ( dot ( P , H . T ) , linalg . inv ( S ) ) except : K = dot ( dot ( P , H . T ) , 1. / S ) x = x + dot ( K , y ) KH = dot ( K , H ) try : I_KH = np . eye ( KH . shape [ 0 ] ) - KH except : I_KH = np . array ( [ 1 - KH ] ) P = dot ( dot ( I_KH , P ) , I_KH . T ) + dot ( dot ( K , R ) , K . T ) if return_all : log_likelihood = logpdf ( z , dot ( H , x ) , S ) return x , P , y , K , S , log_likelihood return x , P
Add a new measurement (z) to the Kalman filter. If z is None, nothing
    is changed.

    This can handle either the multidimensional or unidimensional case. If
    all parameters are floats instead of arrays the filter will still work,
    and return floats for x, P as the result.

    update(1, 2, 1, 1, 1)  # univariate
    update(x, P, 1



    Parameters
    ----------

    x : numpy.array(dim_x, 1), or float
        State estimate vector

    P : numpy.array(dim_x, dim_x), or float
        Covariance matrix

    z : (dim_z, 1): array_like
        measurement for this update. z can be a scalar if dim_z is 1,
        otherwise it must be convertible to a column vector.

    R : numpy.array(dim_z, dim_z), or float
        Measurement noise matrix

    H : numpy.array(dim_x, dim_x), or float, optional
        Measurement function. If not provided, a value of 1 is assumed.

    return_all : bool, default False
        If true, y, K, S, and log_likelihood are returned, otherwise
        only x and P are returned.

    Returns
    -------

    x : numpy.array
        Posterior state estimate vector

    P : numpy.array
        Posterior covariance matrix

    y : numpy.array or scalar
        Residua. Difference between measurement and state in measurement space

    K : numpy.array
        Kalman gain

    S : numpy.array
        System uncertainty in measurement space

    log_likelihood : float
        log likelihood of the measurement