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

def test_autofix(x, thr, cp):
        x[1][1] = np.inf
        x[2][1] = np.nan
        s = thresholding.autofix(x)
        assert (np.nan not in s) and (np.inf not in s)

self.conn_model = conn_model
        self.est_path = est_path
        self.prune = prune
        self.norm = norm
        self.out_fmt = out_fmt
        self.in_mat = None
        self._est_path_fmt = "%s%s" % ('.', self.est_path.split('.')[-1])

        # Load and threshold matrix
        if self._est_path_fmt == '.txt':
            self.in_mat_raw = np.array(np.genfromtxt(self.est_path))
        else:
            self.in_mat_raw = np.array(np.load(self.est_path))

        # De-diagnal and remove nan's and inf's, ensure edge weights are positive
        self.in_mat = np.array(np.abs(np.array(thresholding.autofix(self.in_mat_raw))))

        # Load numpy matrix as networkx graph
        self.G = nx.from_numpy_matrix(self.in_mat)

self.conn_model = conn_model
        self.est_path = est_path
        self.prune = prune
        self.norm = norm
        self.out_fmt = out_fmt
        self.in_mat = None
        self._est_path_fmt = "%s%s" % ('.', self.est_path.split('.')[-1])

        # Load and threshold matrix
        if self._est_path_fmt == '.txt':
            self.in_mat_raw = np.array(np.genfromtxt(self.est_path))
        else:
            self.in_mat_raw = np.array(np.load(self.est_path))

        # De-diagnal and remove nan's and inf's, ensure edge weights are positive
        self.in_mat = np.array(np.abs(np.array(thresholding.autofix(self.in_mat_raw))))

        # Load numpy matrix as networkx graph
        self.G = nx.from_numpy_matrix(self.in_mat)

else:
            # Save coords to pickle
            coord_path = "%s%s" % (namer_dir, '/coords_plotting.pkl')
            with open(coord_path, 'wb') as f:
                pickle.dump(coords, f, protocol=2)

            # Save labels to pickle
            labels_path = "%s%s" % (namer_dir, '/labelnames_plotting.pkl')
            with open(labels_path, 'wb') as f:
                pickle.dump(labels, f, protocol=2)

        connectome = niplot.plot_connectome(np.zeros(shape=(1, 1)), [(0, 0, 0)], node_size=0.0001, black_bg=True)
        connectome.add_overlay(ch2better_loc, alpha=0.45, cmap=plt.cm.gray)
        #connectome.add_overlay(ch2better_loc, alpha=0.35, cmap=plt.cm.gray)
        conn_matrix = np.array(np.array(thresholding.autofix(conn_matrix)))
        [z_min, z_max] = -np.abs(conn_matrix).max(), np.abs(conn_matrix).max()
        if node_size == 'parc':
            node_size_plot = int(6)
        else:
            node_size_plot = int(node_size)
        if len(coords) != conn_matrix.shape[0]:
            raise RuntimeWarning('\nWARNING: Number of coordinates does not match conn_matrix dimensions. If you are '
                                 'using disparity filtering, try relaxing the α threshold.')
        else:
            color_theme = 'Blues'
            #color_theme = 'Greens'
            #color_theme = 'Reds'
            node_color = 'auto'
            connectome.add_graph(conn_matrix, coords, edge_threshold=edge_threshold, edge_cmap=color_theme,
                                 edge_vmax=float(z_max), edge_vmin=float(z_min), node_size=node_size_plot,
                                 node_color='auto')

pickle.dump(labels, f, protocol=2)
        else:
            # Save coords to pickle
            coord_path = "%s%s" % (namer_dir, '/coords_plotting.pkl')
            with open(coord_path, 'wb') as f:
                pickle.dump(coords, f, protocol=2)

            # Save labels to pickle
            labels_path = "%s%s" % (namer_dir, '/labelnames_plotting.pkl')
            with open(labels_path, 'wb') as f:
                pickle.dump(labels, f, protocol=2)

        connectome = niplot.plot_connectome(np.zeros(shape=(1, 1)), [(0, 0, 0)], node_size=0.0001, black_bg=True)
        connectome.add_overlay(ch2better_loc, alpha=0.45, cmap=plt.cm.gray)
        #connectome.add_overlay(ch2better_loc, alpha=0.35, cmap=plt.cm.gray)
        conn_matrix = np.array(np.array(thresholding.autofix(conn_matrix)))
        [z_min, z_max] = -np.abs(conn_matrix).max(), np.abs(conn_matrix).max()
        if node_size == 'parc':
            node_size_plot = int(6)
        else:
            node_size_plot = int(node_size)
        if len(coords) != conn_matrix.shape[0]:
            raise RuntimeWarning('\nWARNING: Number of coordinates does not match conn_matrix dimensions.')
        else:
            norm = colors.Normalize(vmin=-1, vmax=1)
            clust_pal = sns.color_palette("Blues_r", conn_matrix.shape[0])
            clust_colors = colors.to_rgba_array(clust_pal)
            fa_path = dir_path + '/../reg_dmri/dmri_tmp/DSN/Warped.nii.gz'
            if os.path.isfile(fa_path):
                connectome.add_overlay(img=fa_path,
                                       threshold=0.01, alpha=0.25, cmap=plt.cm.copper)