How to use the mayavi.mlab.axes function in mayavi

if props.db_type == 'sqlite':
            query = "PRAGMA table_info(%s)"%(props.image_table)
            w_col = [_[1] for _ in db.execute(query) if _[1].find('Image_Width') >= 0][0]
            h_col = [_[1] for _ in db.execute(query) if _[1].find('Image_Height') >= 0][0]  
        else:
            query = "SELECT * FROM information_schema.COLUMNS WHERE TABLE_SCHEMA = '%s' AND TABLE_NAME = '%s' AND COLUMN_NAME REGEXP 'Image_Width' LIMIT 1"%(props.db_name, props.image_table)
            w_col = db.execute(query)[0][0]
            query = "SELECT * FROM information_schema.COLUMNS WHERE TABLE_SCHEMA = '%s' AND TABLE_NAME = '%s' AND COLUMN_NAME REGEXP 'Image_Height' LIMIT 1"%(props.db_name, props.image_table)
            h_col = db.execute(query)[0][0]          

        query = "SELECT %s FROM %s LIMIT 1"%(w_col, props.image_table)
        self.parent.image_x_dims = db.execute(query)[0][0]
        query = "SELECT %s FROM %s LIMIT 1"%(h_col, props.image_table)
        self.parent.image_y_dims = db.execute(query)[0][0]        
        
        ax = mlab.axes(self.trajectory_line_source, 
                      xlabel='X', ylabel='Y',zlabel='T',
                      #extent = (1,self.parent.image_x_dims,1,self.parent.image_y_dims,self.parent.start_frame,self.parent.end_frame),
                      opacity = self.axes_opacity,
                      x_axis_visibility=True, y_axis_visibility=True, z_axis_visibility=True)
        
        # Set axes to MATLAB's default 3d view
        mlab.view(azimuth = 322.5,elevation = 30.0,
                  figure = self.trajectory_scene.mayavi_scene)     
        
        # Add object label text at end of trajectory
        text_scale_factor = self.trajectory_node_scale_factor*5 
        end_nodes = {}
        for (key,subgraph) in self.connected_nodes.items():
            end_nodes[key] = [_[0] for _ in nx.get_node_attributes(subgraph,END_NODES).items() if _[1]][0]
        self.trajectory_label_collection = dict(zip(self.connected_nodes.keys(),
                                                    [mlab.text3d(subgraph.node[end_nodes[key]]["x"],

mlab.plot3d(cam_axis_line[:,0], cam_axis_line[:,1], cam_axis_line[:,2], color=(1,1,1), tube_radius=0.0025)

                #cam_axis_line = np.array([target_x0_closest, target_x0_closest + t_stp_stp_p])
                #mlab.plot3d(cam_axis_line[:,0], cam_axis_line[:,1], cam_axis_line[:,2], color=(0,0,0), tube_radius=0.0025)

                """
                t = 1e-2
                pair = np.zeros([2,3])
                for k in subsample_inds2.tolist():
                    pair[0,:] = source_object_points[k,:]
                    pair[1,:] = source_object_points[k,:] + t * source_object_normals[k,:]
                    mlab.plot3d(pair[:,0], pair[:,1], pair[:,2], color=(0,0,1), line_width=0.1, tube_radius=None)        
                """

                T_obj_world = mv.MayaviVisualizer.plot_stable_pose(object_mesh, neighbor_image.stable_pose, T_table_world, d=0.15)
                mlab.axes()
                mlab.show()

            # point to plane ICP solver
            ppis = reg.PointToPlaneICPSolver(sample_size=icp_sample_size, gamma=icp_relative_point_plane_cost, mu=icp_regularization_lambda)
            ppfm = fm.PointToPlaneFeatureMatcher(dist_thresh=feature_matcher_dist_thresh, norm_thresh=feature_matcher_norm_thresh) 
            registration = ppis.register_2d(source_object_points, target_object_points, source_object_normals, target_object_normals, ppfm, num_iterations=num_registration_iters,
                                            compute_total_cost=compute_total_cost, vis=debug)
            registration_results.append(registration)

            logging.info('Neighbor %d registration cost %f' %(i, registration.cost))
            if registration.cost < min_cost:
                min_cost = registration.cost
                best_reg = registration
                best_T_stp_camera = T_stp_stp_p.dot(T_stp_camera)
                best_T_stp_obj = T_stp_obj
                best_T_stp_stp_p = stf.SimilarityTransform3D(pose=tfx.pose(best_reg.R, best_reg.t), from_frame='stp', to_frame='stp')

X,Y,Z = mgrid[g.x_min:g.x_max:1j*g.nxsteps,\
            g.y_min:g.y_max:1j*g.nysteps,\
            g.z_min:g.z_max:1j*g.nzsteps]
data = mlab.pipeline.scalar_field(X,Y,Z,L1)
mlab.pipeline.iso_surface(data,contours=arange(mx-10,mx,1).tolist(),vmin=mx-10,vmax=mx)

# uncomment one of the following lines to see a different visualization of 
# the data
#mlab.contour3d(X,Y,Z,L1,vmin=mx-5,vmax=mx,transparent=True)
#mlab.points3d(X,Y,Z,L1,vmin=mx-5,vmax=mx,transparent=True)

#===============================================================================
# adds some axes and enters the GUI main loop
#===============================================================================

mlab.axes()
mlab.show()

def write_mesh_stable_poses(self, mesh, filename, min_prob=0, vis=False):
        prob_mapping, cv_hull = st.compute_stable_poses(mesh), mesh.convex_hull()
        R_list = []
        for face, p in prob_mapping.items():
            if p >= min_prob:
                R_list.append([p, st.compute_basis([cv_hull.vertices()[i] for i in face])])

        if vis:
            print 'P', R_list[0][0]
            mv.figure()
            mesh.visualize()
            mv.axes()

            mv.figure()
            cv_hull_tf = cv_hull.transform(stf.SimilarityTransform3D(tfx.transform(R_list[0][1], np.zeros(3))))
            cv_hull_tf.visualize()
            mv.axes()
            mv.show()

        f = open(filename[:-4] + ".stp", "w")
        f.write("#############################################################\n")
        f.write("# STP file generated by UC Berkeley Automation Sciences Lab #\n")
        f.write("#                                                           #\n")
        f.write("# Num Poses: %d" %len(R_list))
        for _ in range(46 - len(str(len(R_list)))):
            f.write(" ")
        f.write(" #\n")
        f.write("# Min Probability: %s" %str(min_prob))

def show(self):
    '''Show the 3D diagram in the screen.''' 
    from mayavi import mlab
    self.triangleMesh= mlab.triangular_mesh(self.x, self.y, self.z, self.triangles, scalars= self.scalars)
    mlab.colorbar(self.triangleMesh, orientation='vertical')
    mlab.outline(self.triangleMesh)
    mlab.axes(self.triangleMesh, xlabel= self.axialForceLabel, ylabel= self.bendingMomentYLabel, zlabel= self.bendingMomentZLabel)
    #mlab.title(self.title)
    mlab.show()

# Upper right corner
        ra0 = self.extent[1]; dec0 = self.extent[3]
        c = SkyCoord(ra=ra0*u.degree, dec=dec0*u.degree, frame='icrs')
        RA_ll = str(int(c.ra.hms.h))+'h'+str(int(c.ra.hms.m))+'m'+str(round(c.ra.hms.s,1))+'s'
        mlab.text3d(self.xrang,-10,self.zrang+5,RA_ll,scale=fontsize,orient_to_camera=True,color=tcolor)
        DEC_ll = str(int(c.dec.dms.d))+'d'+str(int(abs(c.dec.dms.m)))+'m'+str(round(abs(c.dec.dms.s),1))+'s'
        mlab.text3d(-40,self.yrang,self.zrang+5,DEC_ll,scale=fontsize,orient_to_camera=True,color=tcolor)
        # V axis
        if self.extent[5] > self.extent[4]:
            v0 = self.extent[4]; v1 = self.extent[5]
        else:
            v0 = self.extent[5]; v1 = self.extent[4]
        mlab.text3d(-10,-10,self.zrang,str(round(v0,1)),scale=fontsize,orient_to_camera=True,color=tcolor)
        mlab.text3d(-10,-10,0,str(round(v1,1)),scale=fontsize,orient_to_camera=True,color=tcolor)
        
        mlab.axes(self.field, ranges=self.extent, x_axis_visibility=False, y_axis_visibility=False, z_axis_visibility=False)
        mlab.outline()

T_grasp_gripper = stf.SimilarityTransform3D(pose=tfx.pose(R_grasp_gripper), from_frame='gripper', to_frame='grasp')

    T_mesh_gripper.save('/home/jmahler/jeff_working/GPIS/data/grippers/baxter/T_mesh_gripper.stf')
    T_grasp_gripper.save('/home/jmahler/jeff_working/GPIS/data/grippers/baxter/T_grasp_gripper.stf')

    gripper_params = {}
    gripper_params['min_width'] = 0.026
    gripper_params['max_width'] = 0.060
    f = open('/home/jmahler/jeff_working/GPIS/data/grippers/baxter/params.json', 'w')
    json.dump(gripper_params, f)

    MayaviVisualizer.plot_pose(T_mesh_world, alpha=0.05, tube_radius=0.0025, center_scale=0.005)
    MayaviVisualizer.plot_pose(T_gripper_world, alpha=0.05, tube_radius=0.0025, center_scale=0.005)
    MayaviVisualizer.plot_mesh(gripper_mesh, T_mesh_world, style='surface', color=(1,1,1))
    mv.axes()
    mv.show()    

if __name__ == '__main__':

def draw_3d(grid_x, grid_y, fval, title='pi'):
        mlab.figure()
        mlab.surf(grid_x, grid_y, fval)#, warp_scale="auto")
        mlab.axes(xlabel='x', ylabel='z', zlabel=title)
        mlab.orientation_axes(xlabel='x', ylabel='z', zlabel=title)
        mlab.title(title)