def METHOD_NAME(filename, voxel_size=1):
    r"""
    Load data from a 3DMA-Rock extracted network.
    Parameters
    ----------
    filename : str
        The location of the 'np2th' and 'th2np' files. This can be an
        absolute path or relative to the current working directory.
    voxel_size : scalar
        The resolution of the image on which 3DMA-Rock was run, in terms of
        the linear length of eac voxel. The default is 1.  This is used to
        scale the voxel counts to actual dimension. It is recommended that
        this value be in SI units [m] to work well with OpenPNM.
    Returns
    -------
    network : dict
        An OpenPNM Network dictionary
    Notes
    -----
    This format consists of two files: 'rockname.np2th' and 'rockname.th2pn'.
    They should be stored together in a folder which is referred to by the
    path argument. These files are binary and therefore not human readable.
    References
    ----------
    3DMA-Rock is a network extraction algorithm developed by Brent Lindquist
    and his group
    It uses Medial Axis thinning to find the skeleton of the pore space, then
    extracts geometrical features such as pore volume and throat
    cross-sectional area.
    [1] Lindquist, W. Brent, S. M. Lee, W. Oh, A. B. Venkatarangan, H. Shin,
        and M. Prodanovic. "3DMA-Rock: A software package for automated analysis
        of rock pore structure in 3-D computed microtomography images." SUNY Stony
        Brook (2005).
    """
    net = {}
    path = Path(filename)
    path = path.resolve()
    for file in os.listdir(path):
        if file.endswith(".np2th"):
            np2th_file = os.path.join(path, file)
        elif file.endswith(".th2np"):
            th2np_file = os.path.join(path, file)
    with open(np2th_file, mode='rb') as f:
        [Np, Nt] = np.fromfile(file=f, count=2, dtype='u4')
        net['pore.boundary_type'] = np.ndarray([Np, ], int)
        net['throat.conns'] = np.ones([Nt, 2], int)*(-1)
        net['pore.coordination'] = np.ndarray([Np, ], int)
        net['pore.ID_number'] = np.ndarray([Np, ], int)
        for i in range(0, Np):
            ID = np.fromfile(file=f, count=1, dtype='u4')
            net['pore.ID_number'][i] = ID
            net['pore.boundary_type'][i] = np.fromfile(file=f, count=1,
                                                       dtype='u1')
            z = np.fromfile(file=f, count=1, dtype='u4')[0]
            net['pore.coordination'][i] = z
            att_pores = np.fromfile(file=f, count=z, dtype='u4')
            att_throats = np.fromfile(file=f, count=z, dtype='u4')
            for j in range(0, len(att_throats)):
                t = att_throats[j] - 1
                p = att_pores[j] - 1
                net['throat.conns'][t] = [i, p]
        net['throat.conns'] = np.sort(net['throat.conns'], axis=1)
        net['pore.volume'] = np.fromfile(file=f, count=Np, dtype='u4')
        nx = np.fromfile(file=f, count=1, dtype='u4')
        nxy = np.fromfile(file=f, count=1, dtype='u4')
        pos = np.fromfile(file=f, count=Np, dtype='u4')
        ny = nxy/nx
        ni = np.mod(pos, nx)
        nj = np.mod(np.floor(pos/nx), ny)
        nk = np.floor(np.floor(pos/nx)/ny)
        net['pore.coords'] = np.array([ni, nj, nk]).T
    with open(th2np_file, mode='rb') as f:
        Nt = np.fromfile(file=f, count=1, dtype='u4')[0]
        net['throat.cross_sectional_area'] = \
            np.ones([Nt, ], dtype=int)*(-1)
        for i in range(0, Nt):
            ID = np.fromfile(file=f, count=1, dtype='u4')
            net['throat.cross_sectional_area'][i] = \
                np.fromfile(file=f, count=1, dtype='f4')
            # numvox = np.fromfile(file=f, count=1, dtype='u4')
            att_pores = np.fromfile(file=f, count=2, dtype='u4')
        nx = np.fromfile(file=f, count=1, dtype='u4')
        nxy = np.fromfile(file=f, count=1, dtype='u4')
        pos = np.fromfile(file=f, count=Nt, dtype='u4')
        ny = nxy/nx
        ni = np.mod(pos, nx)
        nj = np.mod(np.floor(pos/nx), ny)
        nk = np.floor(np.floor(pos/nx)/ny)
        net['throat.coords'] = np.array([ni, nj, nk]).T
        net['pore.internal'] = net['pore.boundary_type'] == 0
    # Convert voxel area and volume to actual dimensions
    net['throat.cross_sectional_area'] = \
        (voxel_size**2)*net['throat.cross_sectional_area']
    net['pore.volume'] = (voxel_size**3)*net['pore.volume']
    network = Network()
    network.update(net)
    # Trim headless throats before returning
    ind = np.where(network['throat.conns'][:, 0] == -1)[0]
    trim(network=network, throats=ind)
    return network