Removed edge operations from network.py

        mst = nx.minimum_spanning_tree(self)

        self.graph_masks['mst'][mst.edges()] = True

    def symmetry_checks(self):

        """

        Perform a symmetry check on all edges in the graph

        """

        for s, d, edge in self.edges_iter(data=True):

            edge.symmetry_check()

    def ratio_checks(self, clean_keys=[], **kwargs):

        """

        Perform a ratio check on all edges in the graph

        """

        for s, d, edge in self.edges_iter(data=True):

            edge.ratio_check(clean_keys=clean_keys, **kwargs)

    def compute_homographies(self, clean_keys=[], **kwargs):

        """

        Compute homographies for all edges using identical parameters

        Parameters

        ----------

        clean_keys : list

                     Of keys in the mask dict

        """

        for s, d, edge in self.edges_iter(data=True):

            edge.compute_homography(clean_keys=clean_keys, **kwargs)

    def compute_fundamental_matrices(self, clean_keys=[], **kwargs):

        """

        Compute fundamental matrices for all edges using identical parameters

        Parameters

        ----------

        clean_keys : list

                     Of keys in the mask dict

        """

        for s, d, edge in self.edges_iter(data=True):

            edge.compute_fundamental_matrix(clean_keys=clean_keys, **kwargs)

    def subpixel_register(self, clean_keys=[], threshold=0.8, upsampling=10,

                                 template_size=9, search_size=27, tiled=False, **kwargs):

         """

         Compute subpixel offsets for all edges using identical parameters

         """

         for s, d, edge in self.edges_iter(data=True):

             edge.subpixel_register(clean_keys=clean_keys, threshold=threshold,

                                    upsampling=upsampling, template_size=template_size,

                                    search_size=search_size, tiled=tiled, **kwargs)

    def suppress(self, clean_keys=[], func=spf.correlation, **kwargs):

        for s, d, e in self.edges_iter(data=True):

            e.suppress(clean_keys=clean_keys, func=func, **kwargs)

    def to_filelist(self):

        """

        Generate a file list for the entire graph.