calculate the sensitivity matrix for timescale k given transition matrix T.
    Parameters
    ----------
    T : numpy.ndarray shape = (n, n)
        Transition matrix
    k : int
        timescale index for timescales of descending order (k = 0 for the infinite one)

    Returns
    -------
    x : ndarray, shape=(n, n)
        Sensitivity matrix for entry index around transition matrix T. Reversibility is not assumed.

def timescale_sensitivity(T, k):
    """
    calculate the sensitivity matrix for timescale k given transition matrix T.
    Parameters
    ----------
    T : numpy.ndarray shape = (n, n)
        Transition matrix
    k : int
        timescale index for timescales of descending order (k = 0 for the infinite one)

    Returns
    -------
    x : ndarray, shape=(n, n)
        Sensitivity matrix for entry index around transition matrix T. Reversibility is not assumed.
    """

    eValues, rightEigenvectors = numpy.linalg.eig(T)
    leftEigenvectors = numpy.linalg.inv(rightEigenvectors)

    perm = numpy.argsort(eValues)[::-1]

    eValues = eValues[perm]
    rightEigenvectors = rightEigenvectors[:, perm]
    leftEigenvectors = leftEigenvectors[perm]

    eVal = eValues[k]

    sensitivity = numpy.outer(leftEigenvectors[k], rightEigenvectors[:, k])

    if eVal < 1.0:
        factor = 1.0 / (numpy.log(eVal) ** 2) / eVal
    else:
        factor = 0.0

    sensitivity *= factor

    return sensitivity