How to use the pynets.core.thresholding.standardize function in pynets

def test_standardize(x):
        w = thresholding.standardize(x)
        assert w is not None

from pynets.stats.netmotifs import adaptivethresh
    from pynets.core.thresholding import standardize
    from scipy import spatial
    import pandas as pd
    from py3plex.core import multinet

    # Structural graph threshold window
    struct_mat = standardize(struct_mat)
    dims_struct = struct_mat.shape[0]
    struct_mat[range(dims_struct), range(dims_struct)] = 0
    tmin_struct = struct_mat.min()
    tmax_struct = struct_mat.max()
    threshes_struct = np.linspace(tmin_struct, tmax_struct, bins)

    # Functional graph threshold window
    func_mat = standardize(func_mat)
    dims_func = func_mat.shape[0]
    func_mat[range(dims_func), range(dims_func)] = 0
    tmin_func = func_mat.min()
    tmax_func = func_mat.max()
    threshes_func = np.linspace(tmin_func, tmax_func, bins)

    assert np.all(struct_mat == struct_mat.T), "Structural Matrix must be symmetric"
    assert np.all(func_mat == func_mat.T), "Functional Matrix must be symmetric"

    # list of
    mlib = ['1113', '1122', '1223', '2222', '2233', '3333']

    # Count motifs
    print("%s%s%s%s" % ('Mining ', N, '-node motifs: ', mlib))
    motif_dict = {}
    for thr_struct, thr_func in list(itertools.product(threshes_struct, threshes_func)):

def compare_motifs(struct_mat, func_mat, name, bins=50, N=4):
    from pynets.stats.netmotifs import adaptivethresh
    from pynets.core.thresholding import standardize
    from scipy import spatial
    import pandas as pd
    from py3plex.core import multinet

    # Structural graph threshold window
    struct_mat = standardize(struct_mat)
    dims_struct = struct_mat.shape[0]
    struct_mat[range(dims_struct), range(dims_struct)] = 0
    tmin_struct = struct_mat.min()
    tmax_struct = struct_mat.max()
    threshes_struct = np.linspace(tmin_struct, tmax_struct, bins)

    # Functional graph threshold window
    func_mat = standardize(func_mat)
    dims_func = func_mat.shape[0]
    func_mat[range(dims_func), range(dims_func)] = 0
    tmin_func = func_mat.min()
    tmax_func = func_mat.max()
    threshes_func = np.linspace(tmin_func, tmax_func, bins)

    assert np.all(struct_mat == struct_mat.T), "Structural Matrix must be symmetric"
    assert np.all(func_mat == func_mat.T), "Functional Matrix must be symmetric"

self.in_mat = thresholding.normalize(self.in_mat)
        # Apply log10
        elif self.norm == 2:
            self.in_mat = np.log10(self.in_mat)
        # Apply PTR simple-nonzero
        elif self.norm == 3:
            self.in_mat = pass_to_ranks(self.in_mat, method="simple-nonzero")
        # Apply PTR simple-all
        elif self.norm == 4:
            self.in_mat = pass_to_ranks(self.in_mat, method="simple-all")
        # Apply PTR zero-boost
        elif self.norm == 5:
            self.in_mat = pass_to_ranks(self.in_mat, method="zero-boost")
        # Apply standardization [0, 1]
        elif self.norm == 6:
            self.in_mat = thresholding.standardize(self.in_mat)
        else:
            pass

        self.G = nx.from_numpy_matrix(self.in_mat)

        return self.G

self.in_mat = thresholding.normalize(self.in_mat)
        # Apply log10
        elif self.norm == 2:
            self.in_mat = np.log10(self.in_mat)
        # Apply PTR simple-nonzero
        elif self.norm == 3:
            self.in_mat = pass_to_ranks(self.in_mat, method="simple-nonzero")
        # Apply PTR simple-all
        elif self.norm == 4:
            self.in_mat = pass_to_ranks(self.in_mat, method="simple-all")
        # Apply PTR zero-boost
        elif self.norm == 5:
            self.in_mat = pass_to_ranks(self.in_mat, method="zero-boost")
        # Apply standardization [0, 1]
        elif self.norm == 6:
            self.in_mat = thresholding.standardize(self.in_mat)
        else:
            pass

        self.G = nx.from_numpy_matrix(self.in_mat)

        return self.G