Generates model inputs using a fractional factorial sample

    Returns a NumPy matrix containing the model inputs required for a
    fractional factorial analysis.
    The resulting matrix has D columns, where D is smallest power of 2 that is
    greater than the number of parameters.
    These model inputs are intended to be used with
    :func:`SALib.analyze.ff.analyze`.

    The problem file is padded with a number of dummy variables called
    ``dummy_0`` required for this procedure. These dummy variables can be used
    as a check for errors in the analyze procedure.

    This algorithm is an implementation of that contained in
    [`Saltelli et al. 2008 <http://www.wiley.com/WileyCDA/WileyTitle/productCd-0470059974.html>`_]

    Arguments
    =========
    problem : dict
        The problem definition

    Returns
    =======
    sample : :class:`numpy.array`

def sample(problem, seed=None):
    """Generates model inputs using a fractional factorial sample

    Returns a NumPy matrix containing the model inputs required for a
    fractional factorial analysis.
    The resulting matrix has D columns, where D is smallest power of 2 that is
    greater than the number of parameters.
    These model inputs are intended to be used with
    :func:`SALib.analyze.ff.analyze`.

    The problem file is padded with a number of dummy variables called
    ``dummy_0`` required for this procedure. These dummy variables can be used
    as a check for errors in the analyze procedure.

    This algorithm is an implementation of that contained in
    [`Saltelli et al. 2008 <http://www.wiley.com/WileyCDA/WileyTitle/productCd-0470059974.html>`_]

    Arguments
    =========
    problem : dict
        The problem definition

    Returns
    =======
    sample : :class:`numpy.array`

    """
    if seed:
        np.random.seed(seed)
    contrast = generate_contrast(problem)
    sample = np.array((contrast + 1.) / 2, dtype=np.float)
    problem = extend_bounds(problem)
    scale_samples(sample, problem['bounds'])
    return sample