def normal(target, seeds, scale, loc):
    r"""
    Produces values from a Weibull distribution given a set of random numbers.

    Parameters
    ----------
    target : OpenPNM Object
        The object with which this function as associated.  This argument
        is required to (1) set number of values to generate (geom.Np or
        geom.Nt) and (2) provide access to other necessary values
        (i.e. geom['pore.seed']).

    seeds : string, optional
        The dictionary key on the Geometry object containing random seed values
        (between 0 and 1) to use in the statistical distribution.

    scale : float
        The standard deviation of the Normal distribution

    loc : float
        The mean of the Normal distribution

    Examples
    --------
    The following code illustrates the inner workings of this function,
    which uses the 'norm' method of the scipy.stats module.  This can
    be used to find suitable values of 'scale' and 'loc'.

    >>> import scipy
    >>> func = scipy.stats.norm(scale=.0001, loc=0.001)
    >>> import matplotlib.pyplot as plt
    >>> fig = plt.hist(func.ppf(q=scipy.rand(10000)), bins=50)

    """
    seeds = target[seeds]
    value = spts.norm.ppf(q=seeds, scale=scale, loc=loc)
    return value
r"""
    Produces values from a Weibull distribution given a set of random numbers.

    Parameters
    ----------
    target : OpenPNM Object
        The object with which this function as associated.  This argument
        is required to (1) set number of values to generate (geom.Np or
        geom.Nt) and (2) provide access to other necessary values
        (i.e. geom['pore.seed']).

    seeds : string, optional
        The dictionary key on the Geometry object containing random seed values
        (between 0 and 1) to use in the statistical distribution.

    scale : float
        The standard deviation of the Normal distribution

    loc : float
        The mean of the Normal distribution

    Examples
    --------
    The following code illustrates the inner workings of this function,
    which uses the 'norm' method of the scipy.stats module.  This can
    be used to find suitable values of 'scale' and 'loc'.

    >>> import scipy
    >>> func = scipy.stats.norm(scale=.0001, loc=0.001)
    >>> import matplotlib.pyplot as plt
    >>> fig = plt.hist(func.ppf(q=scipy.rand(10000)), bins=50)