GraphCanvas.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Aug  5 15:43:59 2019
@author: pavel
    The GraphCanvas class that extends the scene class in vispy in order to draw
    the graph object. This class is wrapped in a QTcanvas.
"""
from vispy import gloo, scene
from vispy.gloo import set_viewport, set_state, clear, set_blend_color, context
from vispy.util.transforms import perspective, translate, rotate, scale
import vispy.gloo.gl as glcore
from vispy import app
#from shapely.geometry import Polygon
#from shapely.geometry import Point
#from shapely.geometry import MultiPoint
from scipy.spatial import ConvexHull
from scipy.spatial import Delaunay
#import scipy.spatial.ConvexHull
from vispy.scene.visuals import Text
from vispy.scene.visuals import ColorBar
import copy
import numpy as np
import math
import network_dep as nwt
#import the graph shaders.
from graph_shaders import vert, frag, vs, fs
from subgraph_shaders import vert_s, frag_s, vs_s, fs_s
DEBUG = False
#class storing the path. a vertex in the path is defined as a vertex idx
#and a list of all vertices required to reach the next point in the path
class path_point:
    #Init an empty vertex with no path
    def __init__(self, idx):
        self.idx = idx
        self.v_path = []
        self.e_path = []
    
    #Remove all future path vertices attached to this vertex
    def clear_path(self):
        self.v_path = []
        self.e_path = []
        
    # == comparison operator    
    def __eq__(self, other):
        if self.idx == other.idx:
            return True
        else:
            return False
    # != comparison operator
    def __ne__(self, other):
        if self.idx != other.idx:
            return True
        else:
            return False
        
    def __str__(self):
        print("PathPoint[", self.idx, "], Chain = [", self.v_path, "]")
        
#The graph canvas class that 
class GraphCanvas(scene.SceneCanvas):
    
    """
        Initialization method.
        Generates the 512x512 canvas, makes it available for drawing the fills all
        the GLSL shaders with dummy data.
    """
    def __init__(self, **kwargs):
        # Initialize the canvas for real
        scene.SceneCanvas.__init__(self, size=(512, 512), **kwargs)
        #Unfreeze the canvas to make dynamic interaction possible
        self.unfreeze()
        
        #initialize all the boolean and dictionary variables.
        ps = self.pixel_scale
        self.subgraphs = False
        self.position = 50, 50
        self.down=False;
        
        #Dictionaries to store the unique color ID, cluster ID, and edge-to-ID
        #dictionaries.
        self.color_dict = {}
        self.cluster_dict = {}
        self.edge_dict = {}
        #Booleans the storage for the current "Path", i.e. edges the user selected.
        self.pathing = False
        self.path = []
        self.full_path = []
        #utility variables used for storing the cluster being moved and all the
        #nodes and edges that belong to that cluster and move along with it.
        self.moving = False
        self.moving_cluster = False
        self.selection = False
        n = 10
        ne = 10
        #Init dummy structures
        #self.uniforms = [('u_graph_size', np.float32, 3)]
        self.data = np.zeros(n, dtype=[('a_position', np.float32, 3),
                                  ('a_fg_color', np.float32, 4),
                                  ('a_bg_color', np.float32, 4),
                                  ('a_size', np.float32, 1),
                                  ('a_linewidth', np.float32, 1),
                                  ('a_unique_id', np.float32, 4),
                                  ])
    
        self.line_data = np.zeros(ne, dtype=[('a_position', np.float32, 3),
                                  ('a_normal', np.float32, 2),
                                  ('a_fg_color', np.float32, 4),
                                  ('a_linewidth', np.float32, 1),
                                  ])
    
            
        self.clusters = np.zeros(n*4, dtype=[('a_position', np.float32, 3),
                      ('a_value', np.float32, 2),
                      ('a_bg_color', np.float32, 4),
                      ('a_cluster_color', np.float32, 4),
                      ('a_arc_length', np.float32, 1),
                      ('a_outer_arc_length', np.float32, 4),
                      ('a_unique_id', np.float32, 4),
                      ])
        self.cluster_line_data = np.zeros(ne, dtype=[('a_position', np.float32, 3),
                          ('a_normal', np.float32, 2),
                          ('a_fg_color', np.float32, 4),
                          ('a_linewidth', np.float32, 1),
                          ])
    
        self.edges = np.random.randint(size=(ne, 2), low=0,
                                  high=n-1).astype(np.uint32)
        self.edges_s = np.random.randint(size=(ne, 4), low=0,
                                  high=n-1).astype(np.uint32)
        self.data['a_position'] = np.hstack((0.25 * np.random.randn(n, 2),
                                       np.zeros((n, 1))))
        self.data['a_fg_color'] = 0, 0, 0, 1.0
        color = np.random.uniform(0.5, 1., (n, 3))
        self.data['a_bg_color'] = np.hstack((color, np.zeros((n, 1))))
        self.data['a_size'] = np.random.randint(size=n, low=8*ps, high=20*ps)
        self.data['a_linewidth'] = 8.*ps
        self.data['a_unique_id'] = np.hstack((color, np.ones((n, 1))))
        #self.uniforms['u_graph_size'] = [1.0, 1.0, 1.0]
        self.translate = [0,0,0]
        self.scale = [1,1,1]
        #color = np.random.uniform(0.5, 1., (ne, 3))
        #self.linecolor = np.hstack((color, np.ones((ne, 1))))
        #color = np.random.uniform(0.5, 1., (ne, 3))
        #self.linecolor = np.hstack((color, np.ones((ne, 1))))
        self.u_antialias = 1
        #init dummy vertex and index buffers.
        self.vbo = gloo.VertexBuffer(self.data)
        self.vbo_s = gloo.VertexBuffer(self.clusters)
        #Need to initialize thic/k lines.
        self.index = gloo.IndexBuffer(self.edges)
        self.index_s = gloo.IndexBuffer(self.edges_s)
        #Set the view matrices.
        self.view = np.eye(4, dtype=np.float32)
        self.model = np.eye(4, dtype=np.float32)
        self.projection = np.eye(4, dtype=np.float32)
        #init shaders used for vertices of the full graph.
        self.program = gloo.Program(vert, frag)
        self.program.bind(self.vbo)
        self.program['u_size'] = 1
        self.program['u_antialias'] = self.u_antialias
        self.program['u_model'] = self.model
        self.program['u_view'] = self.view
        self.program['u_projection'] = self.projection
        #self.program['u_graph_size'] = [1.0, 1.0, 1.0]
        self.program['u_picking'] = False
        self.vbo_line = gloo.VertexBuffer(self.line_data)
        #init shades used for the edges in the graph
        self.program_e = gloo.Program(vs, fs)
#        self.program_e['u_size'] = 1
        self.program_e['u_model'] = self.model
        self.program_e['u_view'] = self.view
        self.program_e['u_projection'] = self.projection
        #self.program_e['l_color'] = self.linecolor.astype(np.float32)
        self.program_e.bind(self.vbo_line)
    
        #init shaders used to the vertices in the subgraph graph.
        self.program_s = gloo.Program(vert_s, frag_s)
        self.program_s.bind(self.vbo_s)
        self.program_s['u_model'] = self.model
        self.program_s['u_view'] = self.view
        self.program_s['u_projection'] = self.projection
        #self.program_s['u_graph_size'] = [1.0, 1.0, 1.0]
        self.program_s['u_picking'] = False
        #init shaders used for the subgraph-edges
        self.program_e_s = gloo.Program(vs_s, fs_s)
        self.program_e_s['u_model'] = self.model
        self.program_e_s['u_view'] = self.view
        self.program_e_s['u_projection'] = self.projection
        #self.program_e['l_color'] = self.linecolor.astype(np.float32)
        self.vbo_cluster_lines = gloo.VertexBuffer(self.cluster_line_data)
        self.program_e_s.bind(self.vbo_cluster_lines)
        #set up the viewport and the gl state.
        set_viewport(0, 0, *self.physical_size)
        set_state(clear_color='white', depth_test=True, blend=True,
                  blend_func=('src_alpha', 'one_minus_src_alpha'), depth_func = ('lequal'))
        
        
        self.timer = app.Timer('auto', connect=self.on_timer, start=False)
        #self.constant = app.Timer('auto', connect=self.update, start=True)
        self.num=0
        self.current_color = ""
        self.update_text(self.current_color)
        self.update_color_bar(self.current_color)
        
        print(self.context.config)
        
    def on_timer(self, event):
        #get the temporary positions of the vertices (and edges)
        self.old_pos = np.add(self.old_pos, self.slopes)
        #during each iteration set the new positions in the GPU 
        self.data['a_position'] = self.old_pos
        #Adjust the edges
        edges = self.G.get_edges()
        for e in range(edges.shape[0]):
            idx = int(4*edges[e][2])
            p0 = self.old_pos[edges[e][0], :]
            p1 = self.old_pos[edges[e][1], :]
            d = np.subtract(p1, p0)
            #d_norm = np.multiply(d, 1/np.sqrt(np.power(d[0],2) + np.power(d[1],2)))
            d_norm = d[0:2]
            d_norm = d_norm / np.sqrt(np.power(d_norm[0],2) + np.power(d_norm[1],2))
            norm = np.zeros((2,), dtype=np.float32)
            norm[0] = d_norm[1]
            norm[1] = d_norm[0]*-1
            self.edge_dict[int(edges[e][0]), int(edges[e][1])] = int(edges[e][2])
            self.line_data['a_position'][idx] = p0
            self.line_data['a_normal'][idx] = norm
            self.line_data['a_position'][idx+1] = p1
            self.line_data['a_normal'][idx+1] = norm
            self.line_data['a_position'][idx+2] = p0
            self.line_data['a_normal'][idx+2] = -norm
            self.line_data['a_position'][idx+3] = p1
            self.line_data['a_normal'][idx+3] = -norm
        
        #send data to GPU renderer
        self.vbo.set_data(self.data)
        self.program.bind(self.vbo)
        self.vbo_line = gloo.VertexBuffer(self.line_data)
        self.program_e.bind(self.vbo_line)
        if(self.subgraphs):
            self.update_clusters(self.old_pos)
            edges = self.G_cluster.get_edges()
    #        #generate the vertex buffer and the connections buffer.
            for e in range(edges.shape[0]):
                idx = int(4*edges[e][2])
                p0 = self.cluster_pos[int(edges[e][0])]
                p1 = self.cluster_pos[int(edges[e][1])]
                #p0 = self.G_cluster.vertex_properties["pos"][self.G_cluster.vertex(edges[e][0])]
                #p1 = self.G_cluster.vertex_properties["pos"][self.G_cluster.vertex(edges[e][1])]
                d = np.subtract(p1, p0)
                #d_norm = np.multiply(d, 1/np.sqrt(np.power(d[0],2) + np.power(d[1],2)))
                d_norm = d[0:2]
                d_norm = d_norm / np.sqrt(np.power(d_norm[0],2) + np.power(d_norm[1],2))
                norm = np.zeros((2,), dtype=np.float32)
                norm[0] = d_norm[1]
                norm[1] = d_norm[0]*-1
                #print(np.sqrt(norm[0]*norm[0] + norm[1]*norm[1]))
                #thickness = G.edge_properties["thickness"][e]
                #self.cluster_dict[int(edges[e][0]), int(edges[e][1])] = int(edges[e][2])
                self.cluster_line_data['a_position'][idx] = p0
                self.cluster_line_data['a_normal'][idx] = norm
    
                self.cluster_line_data['a_position'][idx+1] = p1
                self.cluster_line_data['a_normal'][idx+1] = norm
    
                self.cluster_line_data['a_position'][idx+2] = p0
                self.cluster_line_data['a_normal'][idx+2] = -norm
    
                self.cluster_line_data['a_position'][idx+3] = p1
                self.cluster_line_data['a_normal'][idx+3] = -norm
    
            self.vbo_cluster_lines.set_data(self.cluster_line_data)
            self.vbo_s.set_data(self.clusters)
            self.program_s.bind(self.vbo_s)
            self.program_e_s.bind(self.vbo_cluster_lines)
        self.refresh()
    """
        Function that recolors vertices based on the selected statistic
        Maps the statisic stored in G to a colormap passed to the function
        Then updates the necessary color array.
    """    
    def color_vertices(self, G, vertex_property, dtype = False, cm = 'plasma'):
        #if we are visualing the clusters we should use a discrete colormap
        #otherwise use the passed colormap
        if dtype == True:
            G.vertex_properties["RGBA"] = nwt.Network.map_property_to_color(G, G.vertex_properties["clusters"])
        else:
            G.vertex_properties["RGBA"] = nwt.Network.map_property_to_color(G, G.vertex_properties[vertex_property], colormap=cm)
        #set the color and update the Vertices.
        self.current_color = vertex_property
        color = G.vertex_properties["RGBA"].get_2d_array(range(4)).T
        self.data['a_bg_color'] = color
        self.vbo = gloo.VertexBuffer(self.data)
        self.program.bind(self.vbo)
        #self.program_e.bind(self.vbo)
        self.update_text(self.current_color)
        self.update_color_bar(self.current_color)
        self.refresh()
        
    def update_color_buffers(self):
        color = self.G.vertex_properties["RGBA"].get_2d_array(range(4)).T
        self.data['a_bg_color'] = color
        edges = self.G.get_edges()
        for e in range(edges.shape[0]):
            idx = int(4*edges[e][2])
            self.line_data['a_fg_color'][idx] = color[edges[e][0]]
            self.line_data['a_fg_color'][idx+1] = color[edges[e][1]]
            self.line_data['a_fg_color'][idx+2] = color[edges[e][0]]
            self.line_data['a_fg_color'][idx+3] = color[edges[e][1]]
        self.vbo = gloo.VertexBuffer(self.data)
        self.vbo_line = gloo.VertexBuffer(self.line_data)
        self.program.bind(self.vbo)
        self.program_e.bind(self.vbo_line)
        
        
    """
        Function takes a graph and a state and sets all vertices and edges to 
        the transparency defined by state
    """
    def make_all_transparent(self, state):
        for v in self.G.vertices():
            temp = self.G.vertex_properties["RGBA"][v]
            temp[3] = state
            self.G.vertex_properties["RGBA"][v] = temp
        for e in self.G.edges():
            temp = self.G.edge_properties["RGBA"][e]
            temp[3] = state
            self.G.edge_properties["RGBA"][e] = temp
        self.update_color_buffers()
    """
        Maps a statistic of the vertices based on the size of the canvas to size of
        the drawn object.
    """
    def size_vertices(self, G, propertymap):
        size = nwt.Network.map_vertices_to_range(G, [30*self.pixel_scale, 8*self.pixel_scale], propertymap).get_array()
        self.data['a_size'] = size
        self.vbo = gloo.VertexBuffer(self.data)
        self.program.bind(self.vbo)
        #self.program_e.bind(self.vbo)
        self.refresh()
    """
        Function to dim all nodes and edges that do not belong to a cluster chosen 
        in the graph view. Returns a copy of the graph with the alpha channel saved.
        OPTMIZE HERE: could just return an alpha array to reduce memory usage.
    """
    def focus_on_cluster(self, G, c_id):
        G_copy = nwt.gt.Graph(G, directed=False)
        e_color = G_copy.edge_properties["RGBA"].get_2d_array(range(4)).T
        vertices = np.argwhere(self.labels != c_id)
        for v in range(vertices.shape[0]):
            idx = vertices[v][0]
            vtx = G_copy.vertex(idx)
            for e in vtx.all_edges():
                if (int(e.source()), int(e.target())) in self.edge_dict.keys():
                    index = int(self.edge_dict[int(e.source()), int(e.target())])
                    if vtx == int(e.source()):
                        e_color[index][3] = 0.05
                    elif vtx == int(e.target()):
                        e_color[index][3] = 0.05
                else:
                    index = int(self.edge_dict[int(e.target()), int(e.source())])
                    if vtx == int(e.target()):
                        e_color[index][3] = 0.05
                    elif vtx == int(e.source()):
                        e_color[index][3] = 0.05
        G_copy.edge_properties["RGBA"] = G_copy.new_edge_property("vector<double>", vals = e_color)
        return G_copy
    """
        Function that sets the size of the vertices based on the distance from the 
        camera.
    """
    def vertexSizeFromDistance(self, G, camera_pos):
        location = G.vertex_properties["p"].get_2d_array(range(3)).T
        cam_array = np.zeros(location.shape, dtype=np.float32)
        len_array = np.zeros(location.shape[0])
        offset_array = np.zeros(location.shape, dtype=np.float32)
        cam_array[:][0:3] = camera_pos
        offset = [(self.bbu[0]-self.bbl[0])/2, (self.bbu[1]-self.bbl[1])/2, (self.bbu[2]-self.bbl[2])/2]
        location = location - offset
        location = location - camera_pos
        for i in range(location.shape[0]):
            len_array[i] = np.sqrt(np.power(location[i][0],2) + np.power(location[i][1],2) + np.power(location[i][2],2))
        G.vertex_properties['dist_from_camera'] = G.new_vertex_property('float', vals=len_array)
        self.data['a_size'] = nwt.Network.map_vertices_to_range(G, [1*self.pixel_scale, 60*self.pixel_scale], 'dist_from_camera').get_array()
        size = nwt.Network.map_vertices_to_range(G, [1.0, 0.5], 'dist_from_camera').get_array()
        edges = G.get_edges()
        for e in range(edges.shape[0]):
            idx = int(4*edges[e][2])
            self.line_data['a_linewidth'][idx] = size[edges[e][0]]
            self.line_data['a_linewidth'][idx+1] = size[edges[e][1]]
            self.line_data['a_linewidth'][idx+2] = size[edges[e][0]]
            self.line_data['a_linewidth'][idx+3] = size[edges[e][1]]
        #self.vbo = gloo.VertexBuffer(self.data)
        #self.vbo_line = gloo.VertexBuffer(self.line_data)
        #self.program.bind(self.vbo)
        #self.program_e.bind(self.vbo_line)
        #self.update()
    """
        Function that scales the alpha channel of each vertex in the graph based on
        The distance from the camera.
        Sometimes needs to be done separetly.
    """
    def vertexAlphaFromDistance(self, G, camera_pos):
        location = G.vertex_properties["p"].get_2d_array(range(3)).T
        cam_array = np.zeros(location.shape, dtype=np.float32)
        len_array = np.zeros(location.shape[0])
        #offset_array = np.zeros(location.shape, dtype=np.float32)
        cam_array[:][0:3] = camera_pos
        offset = [(self.bbu[0]-self.bbl[0])/2, (self.bbu[1]-self.bbl[1])/2, (self.bbu[2]-self.bbl[2])/2]
        location = location - offset
        location = location - camera_pos
        for i in range(location.shape[0]):
            len_array[i] = np.sqrt(np.power(location[i][0],2) + np.power(location[i][1],2) + np.power(location[i][2],2))
        test = nwt.Network.map_vertices_to_range(G, [0.0, 1.0], 'dist_from_camera').get_array()
        color = G.vertex_properties["RGBA"].get_2d_array(range(4)).T
        for i in range(location.shape[0]):
            color[i][3] = test[i]
        G.vertex_properties["RGBA"] = G.new_vertex_property("vector<double>", vals = color)
        self.data['a_bg_color'] = color
        edges = G.get_edges()
        for e in range(edges.shape[0]):
            idx = int(4*edges[e][2])
            self.line_data['a_fg_color'][idx] = color[edges[e][0]]
            self.line_data['a_fg_color'][idx+1] = color[edges[e][1]]
            self.line_data['a_fg_color'][idx+2] = color[edges[e][0]]
            self.line_data['a_fg_color'][idx+3] = color[edges[e][1]]
        self.vbo = gloo.VertexBuffer(self.data)
        self.vbo_line = gloo.VertexBuffer(self.line_data)
        self.program.bind(self.vbo)
        self.program_e.bind(self.vbo_line)
        self.refresh()
    """
        Sets the edge color based on the the cluster the vertices belongs to
        Propertymap is a VERTEXPROPERTYMAP since the color of the edges is based
        on the clusters the edges belong to.
    """    
    def color_edges(self, G, propertymap="clusters"):
        if propertymap == "clusters":
            for e in G.edges():
                if G.vertex_properties[propertymap][e.source()] == G.vertex_properties[propertymap][e.target()]:
                    G.edge_properties["RGBA"][e] = G.vertex_properties["RGBA"][e.source()]
                else:
                    G.edge_properties["RGBA"][e] = [0.0, 0.0, 0.0, 0.8]
    """
        Test function that only binds the buffer
    """
    def gen_vertex_vbo_minimalist(self):
        self.refresh()
        self.vbo.set_data(self.data)
        self.program.bind(self.vbo)
        self.refresh()
    """
        Helper function that generates the framebuffer object that stores the vertices
        Generates the vertex buffer based on the graph G that is passed to the function
        Sets the color, generates the graph and subgraph color if necessary.
    """
    def gen_vertex_vbo(self, G):
        color = G.vertex_properties["RGBA"].get_2d_array(range(4)).T
        size = nwt.Network.map_vertices_to_range(G, [30*self.pixel_scale, 8*self.pixel_scale], 'degree').get_array()
        position = G.vertex_properties["pos"].get_2d_array(range(3)).T
        #for p in range(position.shape[0]):
        #    position[p][0] = position[p][0] + self.clusters["a_position"][G.vertex_properties["clusters"][G.vertex(p)]][0]
        #    position[p][1] = position[p][1] + self.clusters["a_position"][G.vertex_properties["clusters"][G.vertex(p)]][1]
        #    position[p][2] = position[p][2] + self.clusters["a_position"][G.vertex_properties["clusters"][G.vertex(p)]][2]
        #G.vertex_properties["pos"] = G.new_vertex_property("vector<double>", vals = position)
        edges = G.get_edges();
        edges = edges[:, 0:2]
        #width = nwt.Network.map_edges_to_range(G, [1*self.pixel_scale, 5*self.pixel_scale], 'volume').get_array()
        #ecolor = G.edge_properties["RGBA"].get_2d_array(range(4)).T
        self.data = np.zeros(G.num_vertices(), dtype=[('a_position', np.float32, 3),
                  ('a_fg_color', np.float32, 4),
                  ('a_bg_color', np.float32, 4),
                  ('a_size', np.float32, 1),
                  ('a_linewidth', np.float32, 1),
                  ('a_unique_id', np.float32, 4),
                  ('a_selection', np.float32, 1),
                  ])
        #self.edges = edges.astype(np.uint32)
        self.data['a_position'] = position
        #fg color is the color of the ring
        self.data['a_fg_color'] = 0, 0, 0, 1
        self.data['a_bg_color'] = color
        self.data['a_size'] = size
        self.data['a_linewidth'] = 4.*self.pixel_scale
        self.data['a_unique_id'] = self.gen_vertex_id(G)
        self.data['a_selection'] = G.vertex_properties["selection"].get_array()
        #self.data['a_graph_size'] = [bbu-bbl]
        #self.program['u_graph_size'] = [self.bbu-self.bbl]
        self.vbo = gloo.VertexBuffer(self.data)
        self.gen_line_vbo(G)
        if(self.subgraphs):
            self.vbo_s = gloo.VertexBuffer(self.clusters)
            self.index_s = gloo.IndexBuffer(self.edges_s)
        #self.index = gloo.IndexBuffer(self.edges)
        self.program_e.bind(self.vbo_line)
        self.program.bind(self.vbo)
        if(self.subgraphs):
            #self.program_e_s.bind(self.vbo_s)
            self.program_s.bind(self.vbo_s)
        if DEBUG:
            print(self.view)
        self.refresh()
    """
        Helper function that creates colored "block" lines based on the edges
        in the graph. Generates the framebuffer object and fills it with the relavant data.
        Note that each line segment is saved as a two triangles that share the same
        two points on the centerline, but are offset according to the normal of the
        line segmente to control thickness dynamically.
    """    
    def gen_line_vbo(self, G):
        #Set the data.
        self.line_data = np.zeros(G.num_edges()*4, dtype=[('a_position', np.float32, 3),
                                  ('a_normal', np.float32, 2),
                                  ('a_fg_color', np.float32, 4),
                                  ('a_linewidth', np.float32, 1),
                                  ])
        self.edges = np.random.randint(size=(G.num_edges()*2, 3), low=0,
                                  high=(G.num_edges()-1)).astype(np.uint32)
        color = G.edge_properties["RGBA"].get_2d_array(range(4)).T
        edges = G.get_edges()
        #size need to be changed to the size based on the current property map
        size = nwt.Network.map_vertices_to_range(G, [1.0, 0.5], 'degree').get_array()
        for e in range(edges.shape[0]):
            idx = int(4*edges[e][2])
            p0 = G.vertex_properties["pos"][G.vertex(edges[e][0])]
            p1 = G.vertex_properties["pos"][G.vertex(edges[e][1])]
            d = np.subtract(p1, p0)
            #d_norm = np.multiply(d, 1/np.sqrt(np.power(d[0],2) + np.power(d[1],2)))
            d_norm = d[0:2]
            d_norm = d_norm / np.sqrt(np.power(d_norm[0],2) + np.power(d_norm[1],2))
            norm = np.zeros((2,), dtype=np.float32)
            norm[0] = d_norm[1]
            norm[1] = d_norm[0]*-1
            #print(np.sqrt(norm[0]*norm[0] + norm[1]*norm[1]))
            #thickness = G.edge_properties["thickness"][e]
            thickness = 1.0
            self.edge_dict[int(edges[e][0]), int(edges[e][1])] = int(edges[e][2])
            self.line_data['a_position'][idx] = p0
            self.line_data['a_normal'][idx] = norm
            self.line_data['a_fg_color'][idx] = color[edges[e][2]]
            #a_linewidth is a vector.
            self.line_data['a_linewidth'][idx] = size[edges[e][0]]
            self.line_data['a_position'][idx+1] = p1
            self.line_data['a_normal'][idx+1] = norm
            self.line_data['a_fg_color'][idx+1] = color[edges[e][2]]
            self.line_data['a_linewidth'][idx+1] = size[edges[e][1]]
            self.line_data['a_position'][idx+2] = p0
            self.line_data['a_normal'][idx+2] = -norm
            self.line_data['a_fg_color'][idx+2] = color[edges[e][2]]
            self.line_data['a_linewidth'][idx+2] = size[edges[e][0]]
            self.line_data['a_position'][idx+3] = p1
            self.line_data['a_normal'][idx+3] = -norm
            self.line_data['a_fg_color'][idx+3] = color[edges[e][2]]
            self.line_data['a_linewidth'][idx+3] = size[edges[e][1]]
            self.edges[e*2] = [idx, idx+1, idx+3]
            self.edges[e*2+1] = [idx, idx+2, idx+3]
        
        #Set the buffer object and update the shader programs.
        self.program_e = gloo.Program(vs, fs)
        #self.program_e['l_color'] = self.linecolor.astype(np.float32)
        self.vbo_line = gloo.VertexBuffer(self.line_data)
        self.index = gloo.IndexBuffer(self.edges)
#        self.program_e['u_size'] = 1
        self.program_e['u_model'] = self.model
        self.program_e['u_view'] = self.view
        self.program_e['u_projection'] = self.projection
        self.program_e.bind(self.vbo_line)
    """
        Helper function that generates the edges between the cluster in the layout.
        Color is based on the cluster source/target color and transitions between the
        two.
    """
    def gen_cluster_line_vbo(self, G):
        
        self.G_cluster = nwt.gt.Graph(directed=False)
        self.G_cluster.vertex_properties["pos"] = self.G_cluster.new_vertex_property("vector<double>", val=np.zeros((3,1), dtype=np.float32))
        self.G_cluster.vertex_properties["RGBA"] = self.G_cluster.new_vertex_property("vector<double>", val=np.zeros((4,1), dtype=np.float32))
        for v in range(len(self.cluster_pos)):
            self.G_cluster.add_vertex()
            self.G_cluster.vertex_properties["pos"][self.G_cluster.vertex(v)] = np.asarray(self.cluster_pos[v], dtype=np.float32)
        self.G_cluster.edge_properties["weight"] = self.G_cluster.new_edge_property("int", val = 0)
        self.G_cluster.edge_properties["volume"] = self.G_cluster.new_edge_property("float", val = 0.0)
        #for each edge in the original graph, generate appropriate subgraph edges without repretiions
        #i.e. controls the thichness of the edges in the subgraph view.
        for e in G.edges():
            #if the source and target cluster is not equal to each other
            #add an inter subgraph edge.
            if(G.vertex_properties["clusters"][e.source()] != G.vertex_properties["clusters"][e.target()]):
                t0 = e.source()
                t1 = e.target()
                ct0 = self.G_cluster.vertex(G.vertex_properties["clusters"][t0])
                ct1 = self.G_cluster.vertex(G.vertex_properties["clusters"][t1])
                if(self.G_cluster.edge(ct0, ct1) == None):
                    if(self.G_cluster.edge(ct1, ct0) == None):
                #temp_e.append([G.vertex_properties["clusters"][e.source()], G.vertex_properties["clusters"][e.target()]])
                        self.G_cluster.add_edge(self.G_cluster.vertex(G.vertex_properties["clusters"][t0]), \
                                                 self.G_cluster.vertex(G.vertex_properties["clusters"][t1]))
                        self.G_cluster.edge_properties["weight"][self.G_cluster.edge(self.G_cluster.vertex(G.vertex_properties["clusters"][t0]), \
                                                       self.G_cluster.vertex(G.vertex_properties["clusters"][t1]))] += 1
                        self.G_cluster.edge_properties["volume"][self.G_cluster.edge(self.G_cluster.vertex(G.vertex_properties["clusters"][t0]), \
                                                   self.G_cluster.vertex(G.vertex_properties["clusters"][t1]))] += G.edge_properties["volume"][e]
                        self.G_cluster.vertex_properties["RGBA"][self.G_cluster.vertex(G.vertex_properties["clusters"][t0])]    \
                                                = G.vertex_properties["RGBA"][t0]
                        self.G_cluster.vertex_properties["RGBA"][self.G_cluster.vertex(G.vertex_properties["clusters"][t1])]    \
                                                = G.vertex_properties["RGBA"][t1]
                    else:
                        self.G_cluster.edge_properties["weight"][self.G_cluster.edge(self.G_cluster.vertex(G.vertex_properties["clusters"][t1]), \
                                                       self.G_cluster.vertex(G.vertex_properties["clusters"][t0]))] += 1
                        self.G_cluster.edge_properties["volume"][self.G_cluster.edge(self.G_cluster.vertex(G.vertex_properties["clusters"][t1]), \
                                                   self.G_cluster.vertex(G.vertex_properties["clusters"][t0]))] += G.edge_properties["volume"][e]
                        self.G_cluster.vertex_properties["RGBA"][self.G_cluster.vertex(G.vertex_properties["clusters"][t1])]    \
                                                = G.vertex_properties["RGBA"][t1]
                        self.G_cluster.vertex_properties["RGBA"][self.G_cluster.vertex(G.vertex_properties["clusters"][t0])]    \
                                                = G.vertex_properties["RGBA"][t0]
                else:
                    self.G_cluster.edge_properties["weight"][self.G_cluster.edge(self.G_cluster.vertex(G.vertex_properties["clusters"][t0]), \
                                             self.G_cluster.vertex(G.vertex_properties["clusters"][t1]))] += 1
                    self.G_cluster.edge_properties["volume"][self.G_cluster.edge(self.G_cluster.vertex(G.vertex_properties["clusters"][t0]), \
                                               self.G_cluster.vertex(G.vertex_properties["clusters"][t1]))] += G.edge_properties["volume"][e]
                    self.G_cluster.vertex_properties["RGBA"][self.G_cluster.vertex(G.vertex_properties["clusters"][t0])]    \
                                            = G.vertex_properties["RGBA"][t0]
                    self.G_cluster.vertex_properties["RGBA"][self.G_cluster.vertex(G.vertex_properties["clusters"][t1])]    \
                                            = G.vertex_properties["RGBA"][t1]
        #create a graph that stores the edges of between the clusters
#        self.G_cluster = nwt.gt.Graph(directed=False)
#        self.G_cluster.vertex_properties["pos"] = self.G_cluster.new_vertex_property("vector<double>", val=np.zeros((3,1), dtype=np.float32))
#        self.G_cluster.vertex_properties["RGBA"] = self.G_cluster.new_vertex_property("vector<double>", val=np.zeros((4,1), dtype=np.float32))
#        for v in range(len(self.cluster_pos)):
#            self.G_cluster.add_vertex()
#            self.G_cluster.vertex_properties["pos"][self.G_cluster.vertex(v)] = np.asarray(self.cluster_pos[v], dtype=np.float32)
#        self.G_cluster.edge_properties["weight"] = self.G_cluster.new_edge_property("int", val = 0)
#        #for each edge in the original graph, generate appropriate subgraph edges without repretiions
#        #i.e. controls the thichness of the edges in the subgraph view.
#        for e in G.edges():
#            #if the source and target cluster is not equal to each other
#            #add an inter subgraph edge.
#            if(G.vertex_properties["clusters"][e.source()] != G.vertex_properties["clusters"][e.target()]):
#                #temp_e.append([G.vertex_properties["clusters"][e.source()], G.vertex_properties["clusters"][e.target()]])
#                self.G_cluster.add_edge(self.G_cluster.vertex(G.vertex_properties["clusters"][e.source()]), \
#                                         self.G_cluster.vertex(G.vertex_properties["clusters"][e.target()]))
#                self.G_cluster.edge_properties["weight"][self.G_cluster.edge(self.G_cluster.vertex(G.vertex_properties["clusters"][e.source()]), \
#                                               self.G_cluster.vertex(G.vertex_properties["clusters"][e.target()]))] += 1
#                self.G_cluster.vertex_properties["RGBA"][self.G_cluster.vertex(G.vertex_properties["clusters"][e.source()])]    \
#                                        = G.vertex_properties["RGBA"][e.source()]
#                self.G_cluster.vertex_properties["RGBA"][self.G_cluster.vertex(G.vertex_properties["clusters"][e.target()])]    \
#                                        = G.vertex_properties["RGBA"][e.target()]
        self.G_cluster.vertex_properties["degree"] = self.G_cluster.degree_property_map("total")
        self.cluster_line_data = np.zeros(self.G_cluster.num_edges()*4, dtype=[('a_position', np.float32, 3),
                          ('a_normal', np.float32, 2),
                          ('a_fg_color', np.float32, 4),
                          ('a_linewidth', np.float32, 1),
                          ])
        self.cluster_edges = np.random.randint(size=(self.G_cluster.num_edges()*2, 3), low=0,
                                  high=(G.num_edges()-1)).astype(np.uint32)
        edges = self.G_cluster.get_edges()
        #size need to be changed to the size based on the current property map
        size = nwt.Network.map_edges_to_range(self.G_cluster, [1.0, 0.5], 'weight').get_array()
        color = self.G_cluster.vertex_properties["RGBA"].get_2d_array(range(4)).T
        #generate the vertex buffer and the connections buffer.
        for e in range(edges.shape[0]):
            idx = int(4*edges[e][2])
            p0 = self.G_cluster.vertex_properties["pos"][self.G_cluster.vertex(edges[e][0])]
            p1 = self.G_cluster.vertex_properties["pos"][self.G_cluster.vertex(edges[e][1])]
            d = np.subtract(p1, p0)
            #d_norm = np.multiply(d, 1/np.sqrt(np.power(d[0],2) + np.power(d[1],2)))
            d_norm = d[0:2]
            d_norm = d_norm / np.sqrt(np.power(d_norm[0],2) + np.power(d_norm[1],2))
            norm = np.zeros((2,), dtype=np.float32)
            norm[0] = d_norm[1]
            norm[1] = d_norm[0]*-1
            #print(np.sqrt(norm[0]*norm[0] + norm[1]*norm[1]))
            #thickness = G.edge_properties["thickness"][e]
            self.cluster_dict[int(edges[e][0]), int(edges[e][1])] = int(edges[e][2])
            self.cluster_line_data['a_position'][idx] = p0
            self.cluster_line_data['a_normal'][idx] = norm
            self.cluster_line_data['a_fg_color'][idx] = color[edges[e][0]]
            self.cluster_line_data['a_linewidth'][idx] = size[e]
            self.cluster_line_data['a_position'][idx+1] = p1
            self.cluster_line_data['a_normal'][idx+1] = norm
            self.cluster_line_data['a_fg_color'][idx+1] = color[edges[e][1]]
            self.cluster_line_data['a_linewidth'][idx+1] = size[e]
            self.cluster_line_data['a_position'][idx+2] = p0
            self.cluster_line_data['a_normal'][idx+2] = -norm
            self.cluster_line_data['a_fg_color'][idx+2] = color[edges[e][0]]
            self.cluster_line_data['a_linewidth'][idx+2] = size[e]
            self.cluster_line_data['a_position'][idx+3] = p1
            self.cluster_line_data['a_normal'][idx+3] = -norm
            self.cluster_line_data['a_fg_color'][idx+3] = color[edges[e][1]]
            self.cluster_line_data['a_linewidth'][idx+3] = size[e]
            self.cluster_edges[e*2] = [idx, idx+1, idx+3]
            self.cluster_edges[e*2+1] = [idx, idx+2, idx+3]
        self.program_e_s = gloo.Program(vs_s, fs_s)
        self.index_clusters_s = gloo.IndexBuffer(self.cluster_edges)
        self.vbo_cluster_lines = gloo.VertexBuffer(self.cluster_line_data)
        self.program_e_s['u_model'] = self.model
        self.program_e_s['u_view'] = self.view
        self.program_e_s['u_projection'] = self.projection
        self.program_e_s.bind(self.vbo_cluster_lines)
    """
        Updates the vertex buffers based on the current position of the cluster.
        Updates it's position.    
    """
    def update_cluster_line_vbo(self):
        for v in range(len(self.cluster_pos)):
            self.G_cluster.vertex_properties["pos"][self.G_cluster.vertex(v)] = np.asarray(self.cluster_pos[v], dtype=np.float32)
        #OPTIMIZE HERE to update only one cluster at a time.
        edges = self.G_cluster.get_edges()
        #size need to be changed to the size based on the current property map
        size = nwt.Network.map_edges_to_range(self.G_cluster, [1.0, 0.5], 'weight').get_array()
        color = self.G_cluster.vertex_properties["RGBA"].get_2d_array(range(4)).T
        for e in range(edges.shape[0]):
            idx = int(4*edges[e][2])
            p0 = self.G_cluster.vertex_properties["pos"][self.G_cluster.vertex(edges[e][0])]
            p1 = self.G_cluster.vertex_properties["pos"][self.G_cluster.vertex(edges[e][1])]
            d = np.subtract(p1, p0)
            #d_norm = np.multiply(d, 1/np.sqrt(np.power(d[0],2) + np.power(d[1],2)))
            d_norm = d[0:2]
            d_norm = d_norm / np.sqrt(np.power(d_norm[0],2) + np.power(d_norm[1],2))
            norm = np.zeros((2,), dtype=np.float32)
            norm[0] = d_norm[1]
            norm[1] = d_norm[0]*-1
            #print(np.sqrt(norm[0]*norm[0] + norm[1]*norm[1]))
            #thickness = G.edge_properties["thickness"][e]
            self.cluster_dict[int(edges[e][0]), int(edges[e][1])] = int(edges[e][2])
            self.cluster_line_data['a_position'][idx] = p0
            self.cluster_line_data['a_normal'][idx] = norm
            self.cluster_line_data['a_fg_color'][idx] = color[edges[e][0]]
            self.cluster_line_data['a_linewidth'][idx] = size[e]
            self.cluster_line_data['a_position'][idx+1] = p1
            self.cluster_line_data['a_normal'][idx+1] = norm
            self.cluster_line_data['a_fg_color'][idx+1] = color[edges[e][1]]
            self.cluster_line_data['a_linewidth'][idx+1] = size[e]
            self.cluster_line_data['a_position'][idx+2] = p0
            self.cluster_line_data['a_normal'][idx+2] = -norm
            self.cluster_line_data['a_fg_color'][idx+2] = color[edges[e][0]]
            self.cluster_line_data['a_linewidth'][idx+2] = size[e]
            self.cluster_line_data['a_position'][idx+3] = p1
            self.cluster_line_data['a_normal'][idx+3] = -norm
            self.cluster_line_data['a_fg_color'][idx+3] = color[edges[e][1]]
            self.cluster_line_data['a_linewidth'][idx+3] = size[e]
        self.program_e_s = gloo.Program(vs_s, fs_s)
        self.index_clusters_s = gloo.IndexBuffer(self.cluster_edges)
        self.vbo_cluster_lines = gloo.VertexBuffer(self.cluster_line_data)
        
        self.program_e_s['u_model'] = self.model
        self.program_e_s['u_view'] = self.view
        self.program_e_s['u_projection'] = self.projection
        self.program_e_s.bind(self.vbo_cluster_lines)
    """
        Genererates a unique index for every vertex.
    """    
    def gen_vertex_id(self, G):
        self.color_dict = {}
        base = [0, 0, 0, 255]
        idx = 0
        #colors = cm.get_cmap('Wistia', G.num_vertices()*2)
        v_id = np.zeros((G.num_vertices(), 4), dtype=np.float32)
        for v in G.vertices():
            color = np.multiply(base, 1/255.0)
            v_id[int(v)] = color
            self.color_dict[tuple(color)] = int(v)
            idx += 1
            base = [int(idx/(255*255)), int((idx/255)%255), int(idx%255), 255]
        return(v_id)
    """
        Generates a unique index for every cluster.
    """
    def gen_cluster_id(self, G):
        self.cluster_dict = {}
        base = [0, 0, 0, 255]
        idx = 0
        #colors = cm.get_cmap('Wistia', G.num_vertices()*2)
        v_id = np.zeros((self.n_c, 4), dtype=np.float32)
        for v in range(self.n_c):
            color = np.multiply(base, 1/255.0)
            v_id[int(v)] = color
            self.cluster_dict[tuple(color)] = int(v)
            idx += 1
            base = [int(idx/(255*255)), int((idx/255)%255), int(idx%255), 255]
        return(v_id)
    """
        Generates the bounding box of the radial glyph.
    """
    def gen_cluster_coords(self, center, diameter):
        radius = diameter/2.0
        top = center[1]+radius
        bottom = center[1]-radius
        left = center[0]-radius
        right = center[0]+radius
        positions = [[right, bottom, center[2]],
                    [right, top, center[2]],
                    [left, top, center[2]],
                    [left, bottom, center[2]]]
        values = [[1.0, -1.0],
                  [1.0, 1.0,],
                  [-1.0, 1.0],
                  [-1.0, -1.0]]
        return positions, values
    """
        Generates a hierarchical layout based on the cluster graph (spdf) and subclusters
    """
    def voronoi_layout(self, G = None, n_c = None, G_c = None):
        
        def gen_subclusters(G, G_cluster, i, reposition = False):
            vfilt = np.zeros([G.num_vertices(), 1], dtype='bool')
            labels = G.vertex_properties["clusters"].get_array()
            num_v_in_cluster = len(np.argwhere(labels == i))
            vfilt[np.argwhere(labels == i)] = 1
            vfilt_prop = G.new_vertex_property("bool", vals = vfilt)
            G.set_vertex_filter(vfilt_prop)
            
            g = nwt.gt.Graph(G, prune=True, directed=False)
        
            
            if reposition == True:
                vbetweeness_centrality = g.new_vertex_property("double")
                ebetweeness_centrality = g.new_edge_property("double")
                nwt.gt.graph_tool.centrality.betweenness(g, vprop=vbetweeness_centrality, eprop=ebetweeness_centrality, norm=True)
                g.vertex_properties["bc"] = vbetweeness_centrality
                g.edge_properties["bc"] = ebetweeness_centrality
                g.vertex_properties["pos"] = nwt.gt.sfdp_layout(g, eweight = ebetweeness_centrality)
    
            positions = g.vertex_properties["pos"].get_2d_array(range(2)).T
            center = np.sum(positions, 0)/num_v_in_cluster
            G.clear_filters()
            return g, center
        
        if G_c == None:
            G_c = self.G_cluster
            G_c.vertex_properties["pos"] = nwt.gt.sfdp_layout(G_c, eweight=G_c.edge_properties["volume"], vweight=G_c.vertex_properties["degree"], C = 1.0, K = 10)
            if(G == None):
                G = self.G
            if(n_c == None):
                n_c = self.n_c
        else:
            if(G == None):
                G = self.G
            if(n_c == None):
                n_c = self.n_c
            if self.n_c == G_c.num_vertices():
                for i in range(n_c):
                    self.G_cluster.vertex_properties["pos"][self.G_cluster.vertex(i)] = G_c.vertex_properties["pos"][G_c.vertex(i)] 
            
        
        for i in range(n_c):
            g, center = gen_subclusters(G, G_c, i, reposition=True)
            d = G_c.vertex_properties["pos"][i] - center
            for v in g.vertices():
                G.vertex_properties["pos"][g.vertex_properties["idx"][v]] = g.vertex_properties["pos"][v] + d
                g.vertex_properties["pos"][v] = g.vertex_properties["pos"][v] + d
        #print("stuff")
        
    """
        Layout algorithm that expands the cluster based on the location of the of the clusters
    """
    def expand_based_on_clusters(self, G, n):
        pos = G.vertex_properties["pos"].get_2d_array(range(3)).T
        for p in range(pos.shape[0]):
            pos[p][0] = pos[p][0] - self.clusters["a_position"][G.vertex_properties["clusters"][G.vertex(p)]][0]
            pos[p][1] = pos[p][1] - self.clusters["a_position"][G.vertex_properties["clusters"][G.vertex(p)]][1]
            pos[p][2] = pos[p][2] - self.clusters["a_position"][G.vertex_properties["clusters"][G.vertex(p)]][2]
        G.vertex_properties["pos"] = G.new_vertex_property("vector<double>", vals = pos)
#        for i in range(n):
#            index = 4*i
#            #generate the vertex filter for this cluster
#            num_v_in_cluster = len(np.argwhere(self.labels == i))
#            vfilt = np.zeros([G.num_vertices(), 1], dtype="bool")
#            vfilt[np.argwhere(self.labels == i)] = 1
#            vfilt_prop = G.new_vertex_property("bool", vals = vfilt)
#            G.set_vertex_filter(vfilt_prop)
#
#            #get the filtered properties
#            g = nwt.gt.Graph(G, prune=True, directed=False)
#            positions = g.vertex_properties["pos"].get_2d_array(range(3)).T
#            position = np.sum(positions, 0)/num_v_in_cluster
#            p, v = self.gen_cluster_coords(position, np.sum(g.vertex_properties['degree'].get_array()))
#            self.clusters['a_position'][index:index+4] = np.asarray(p, dtype=np.float32)
#            self.clusters['a_value'][index:index+4] = np.asarray(v, dtype=np.float32)
#            G.clear_filters()
#            self.cluster_pos[i] = position
#        color = G.vertex_properties["RGBA"].get_2d_array(range(4)).T
#        size = nwt.Network.map_vertices_to_range(G, [30*self.pixel_scale, 8*self.pixel_scale], 'degree').get_array()
#
#        position = G.vertex_properties["pos"].get_2d_array(range(3)).T
#        #for p in range(position.shape[0]):
#        #    position[p][0] = position[p][0] + self.clusters["a_position"][G.vertex_properties["clusters"][G.vertex(p)]][0]
#        #    position[p][1] = position[p][1] + self.clusters["a_position"][G.vertex_properties["clusters"][G.vertex(p)]][1]
#        #    position[p][2] = position[p][2] + self.clusters["a_position"][G.vertex_properties["clusters"][G.vertex(p)]][2]
#        #G.vertex_properties["pos"] = G.new_vertex_property("vector<double>", vals = position)
#        
#        
#        edges = G.get_edges();
#        edges = edges[:, 0:2]
#        #width = nwt.Network.map_edges_to_range(G, [1*self.pixel_scale, 5*self.pixel_scale], 'volume').get_array()
#        #ecolor = G.edge_properties["RGBA"].get_2d_array(range(4)).T
#
#        self.data = np.zeros(G.num_vertices(), dtype=[('a_position', np.float32, 3),
#                  ('a_fg_color', np.float32, 4),
#                  ('a_bg_color', np.float32, 4),
#                  ('a_size', np.float32, 1),
#                  ('a_linewidth', np.float32, 1),
#                  ('a_unique_id', np.float32, 4),
#                  ('a_selection', np.float32, 1),
#                  ])
#
#        #self.edges = edges.astype(np.uint32)
#        self.data['a_position'] = position
#        #fg color is the color of the ring
#        self.data['a_fg_color'] = 0, 0, 0, 1
#        self.data['a_bg_color'] = color
#        self.data['a_size'] = size
#        self.data['a_linewidth'] = 4.*self.pixel_scale
#        self.data['a_unique_id'] = self.gen_vertex_id(G)
#        self.data['a_selection'] = G.vertex_properties["selection"].get_array()
#        #self.data['a_graph_size'] = [bbu-bbl]
#
#        self.program['u_graph_size'] = [self.bbu-self.bbl]
#        self.vbo = gloo.VertexBuffer(self.data)
#        self.gen_line_vbo(G)
#        if(self.subgraphs):
#            self.vbo_s = gloo.VertexBuffer(self.clusters)
#            self.index_s = gloo.IndexBuffer(self.edges_s)
#        #self.index = gloo.IndexBuffer(self.edges)
#        self.program_e.bind(self.vbo_line)
#        self.program.bind(self.vbo)
#        if(self.subgraphs):
#            #self.program_e_s.bind(self.vbo_s)
#            self.program_s.bind(self.vbo_s)
#        if DEBUG:
#            print(self.view)
#        self.refresh()
    """
        Function that updates the cluster positions based on new vertex positions
        in the graph. Primarity used for animation.
    """
    def update_clusters(self, new_pos):
        clusters = self.G.vertex_properties["clusters"].get_array().T
        for i in range(self.n_c):
            idx = np.argwhere(clusters == i)
            pos = np.sum(new_pos[idx], 0)/len(idx)
            self.cluster_pos[i] = pos.reshape(3)
            index = i*4
            p, v = self.gen_cluster_coords(self.cluster_pos[i], self.cluster_size[i])
            self.clusters['a_position'][index:index+4] = np.asarray(p, dtype=np.float32)
            self.clusters['a_value'][index:index+4] = np.asarray(v, dtype=np.float32)
            self.G_cluster.vertex_properties["pos"][self.G_cluster.vertex(i)] = self.cluster_pos[i]
            
    """
        Function that creates the clusters, assuming that all the data is set already.
    """
    def gen_cluster_vbo(self, G, bbl, bbu, num_clusters, edge_metric = 'volume', vertex_metric = 'degree', update_color = True):
               #add colormap
        if(update_color == True):
            G.vertex_properties["RGBA"] = nwt.Network.map_property_to_color(G, G.vertex_properties["clusters"])
        #generate an empty property set for the clusters.
        self.clusters = np.zeros(num_clusters*4, dtype=[('a_position', np.float32, 3),
                      ('a_value', np.float32, 2),
                      ('a_bg_color', np.float32, 4),
                      ('a_cluster_color', np.float32, 4),
                      ('a_arc_length', np.float32, 1),
                      ('a_outer_arc_length', np.float32, 4),
                      ('a_unique_id', np.float32, 4),
                      ])
        self.edges_s = np.random.randint(size=(num_clusters*2, 3), low=0,
                                  high=4).astype(np.uint32)
        #fill the foreground color as halo
        #self.clusters['a_fg_color'] = 1., 1., 1., 0.0
        #self.clusters['a_linewidth'] = 4.*self.pixel_scale
        G.vertex_properties["pos"] = nwt.gt.sfdp_layout(G, groups = G.vertex_properties["clusters"], pos = G.vertex_properties["pos"], C = 1.0, K = 10)
        temp = []
        temp_pos = []
        #Find the global total of the metric.
        global_metric = np.sum(G.edge_properties[edge_metric].get_array(), 0)
        unique_color = self.gen_cluster_id(G)
        #generate the property values for every cluster
        for i in range(num_clusters):
            idx = 4*i
            #generate the vertex filter for this cluster
            num_v_in_cluster = len(np.argwhere(self.labels == i))
            vfilt = np.zeros([G.num_vertices(), 1], dtype="bool")
            vfilt[np.argwhere(self.labels == i)] = 1
            vfilt_prop = G.new_vertex_property("bool", vals = vfilt)
            G.set_vertex_filter(vfilt_prop)
            #get the filtered properties
            g = nwt.gt.Graph(G, prune=True, directed=False)
            positions = g.vertex_properties["pos"].get_2d_array(range(3)).T
            position = np.sum(positions, 0)/num_v_in_cluster
            #calculate the arclength for the global statistic
            arc_length = np.sum(g.edge_properties[edge_metric].get_array(), 0)/global_metric*np.pi*2
            arc_length_vertex = np.ones((4,1), dtype = np.float32)
            array = g.vertex_properties[vertex_metric].get_array()
            #calculate metric distribution and turn it into arc_lengths
            t_vertex_metric = np.sum(array)
            arc_length_vertex[0] = np.sum(array < 2)/t_vertex_metric
            arc_length_vertex[1] = np.sum(array == 2)/t_vertex_metric
            arc_length_vertex[2] = np.sum(array == 3)/t_vertex_metric
            arc_length_vertex[3] = np.sum(array > 3)/t_vertex_metric
            #arc_length_vertex = np.asarray(arc_length_vertex, dtype = np.float32)
            #arc_length_vertex = (max(arc_length_vertex) - min(arc_length_vertex)) \
            #* (arc_length_vertex- min(arc_length_vertex))
            for j in range(len(arc_length_vertex)):
                if j != 0:
                    arc_length_vertex[j] += arc_length_vertex[j-1]
            if DEBUG:
                print("arc_length before ", arc_length_vertex, " and sum to ", sum(arc_length_vertex))
            arc_length_vertex = np.asarray(arc_length_vertex, dtype = np.float32)
            arc_length_vertex = (np.pi - -np.pi)/(max(arc_length_vertex) - min(arc_length_vertex)) \
            * (arc_length_vertex- min(arc_length_vertex)) + (-np.pi)
            if DEBUG:
                print(arc_length_vertex)
            #print(arc_length)
            temp_pos.append(position)
            #generate the color for every vertex,
            #since all vertices belong to the same cluster we can check only
            #one vertex for the cluster color.
            self.clusters['a_cluster_color'][idx:idx+4] = g.vertex_properties["RGBA"][g.vertex(0)]
            self.clusters['a_bg_color'][idx:idx+4] = [0.1, 0.1, 0.1, 1.0]
            self.clusters['a_unique_id'][idx:idx+4] = unique_color[i]
            #The arc-length representing one global metric.
            self.clusters['a_arc_length'][idx:idx+4] = arc_length
            self.clusters['a_outer_arc_length'][idx:idx+4] = arc_length_vertex[:].T
            temp.append(np.sum(g.vertex_properties['degree'].get_array()))
            G.clear_filters()
        if DEBUG:
            print(self.clusters['a_outer_arc_length'])
        maximum = max(temp)
        minimum = min(temp)
        temp = ((temp-minimum)/(maximum-minimum)*(60*self.pixel_scale)+20*self.pixel_scale)*2.0
        for i in range(num_clusters):
            index = i*4
            index_t = i*2
            p, v = self.gen_cluster_coords(temp_pos[i], temp[i])
            self.clusters['a_position'][index:index+4] = np.asarray(p, dtype=np.float32)
            self.clusters['a_value'][index:index+4] = np.asarray(v, dtype=np.float32)
            self.edges_s[index_t] = [index, index+1, index+2]
            self.edges_s[index_t+1] = [index, index+2, index+3]
            #self.edges_s[i][0:4] = np.asarray(range(index, index+4), dtype=np.uint32)
            #self.edges_s[i]
        self.cluster_pos = temp_pos
        self.cluster_size = temp
        #self.expand_based_on_clusters(G, self.n_c)
        G.clear_filters()
#        self.edges_s[1][0:4] = np.asarray(range(0, 0+4), dtype=np.uint32)
#        self.edges_s[1][4] = 0
#        self.edges_s[1][5] = 0+2
#
#        self.edges_s[0][0:4] = np.asarray(range(index, index+4), dtype=np.uint32)
#        self.edges_s[0][4] = index
#        self.edges_s[0][5] = index+2
        #self.clusters['a_size'] = temp
        self.gen_cluster_line_vbo(G)
        #self.program_s['u_graph_size'] = [bbu-bbl]
        #if len(temp_e) > 0:
        #    self.edges_s = np.unique(np.asarray(temp_e, np.uint32), axis=0)
        #else:
        #    self.edges_s = []
        #print(self.edges_s)
    """
        Function that generates the clusters for an unclustered graph
        These are to be represented by the arcs
    """    
    def gen_clusters(self, G, bbl, bbu, n_c = None, edge_metric = 'volume', vertex_metric = 'degree'):
        #Generate the clusters
        self.labels = nwt.Network.spectral_clustering(G,'length', n_clusters = n_c)
        bb = nwt.AABB(G)
        #print("FLJKKHDFLKJFDLKJFDLKJ ", m)
        pts = []
        x, y, z = bb.project_grid(3)
        for i in range(3):
            for j in range(3):
                for k in range(3):
                    pts.append(np.array([x[i], y[j], z[k]]))
        
        
        #self.labels = nwt.Network.spectral_clustering(G,'length')
        #Add clusters as a vertex property
        G.vertex_properties["clusters"] = G.new_vertex_property("int", vals=self.labels)
        num_clusters = len(np.unique(self.labels))
        self.n_c = n_c
        new_indices = []
        pos = G.vertex_properties["p"].get_2d_array(range(3)).T
        
        #for each cluster find the average vertex position and match to closest point
        #in the unique grid.
        for i in range(n_c):
            point = np.sum(pos[np.argwhere(self.labels == i)], axis=0)/len(np.argwhere(self.labels == i))
            d = 100000000.0
            idx = -1
            for j in range(len(pts)):
                dist = np.sqrt(np.power(pts[j][0]-point[0,0],2) + np.power(pts[j][1]-point[0,1],2) + np.power(pts[j][2]-point[0,2],2))
                if dist < d:
                    d = dist
                    idx = j
            new_indices.append(idx)
            pts[idx] = np.array([100000000.0, 1000000000.0, 100000000.0])
        #since there are more points than clusters, we need to make the indices range from
        #[0, n_c)
        j=0
        unique_indices = np.array(new_indices)
        for i in range(n_c):
            idx = np.argmin(new_indices)
            unique_indices[idx] = j
            j += 1
            new_indices[idx] = 100
            
        lbl = np.zeros(self.labels.shape)
        for i in range(n_c):
            idxs = np.argwhere(self.labels == i)
            new_idx = np.argwhere(unique_indices == i)
            lbl[idxs] = unique_indices[i]
            
        self.labels = lbl
        G.vertex_properties["clusters"] = G.new_vertex_property("int", vals=self.labels)
        self.gen_cluster_vbo(self.G, bbl, bbu, num_clusters, edge_metric, vertex_metric)
        
    """
        Function that expands that generates the layout and updates the buffer
    
    """
    def expand_clusters(self, G, n_c):
        self.expand_based_on_clusters(G, n_c)
        self.gen_cluster_line_vbo(G)
        if(self.subgraphs):
            self.vbo_s = gloo.VertexBuffer(self.clusters)
            self.index_s = gloo.IndexBuffer(self.edges_s)
        self.program_e.bind(self.vbo_line)
        self.program.bind(self.vbo)
        if(self.subgraphs):
            self.program_s.bind(self.vbo_s)
        if DEBUG:
            print(self.view)
        self.refresh()
    """
        Function for generating a distance field based on the clusters in the 
        clustered 3D network.
    """
    def distancefield(self, G):
        
        import scipy as sp
        #generate a meshgrid of the appropriate size and resolution to surround the network
        #get the space occupied by the network
        lower = self.bbl
        upper = self.bbu
        R = np.asarray(np.floor(abs(lower-upper)), dtype=np.int)
        x = np.linspace(lower[0], upper[0], R[0])   #get the grid points for uniform sampling of this space
        y = np.linspace(lower[1], upper[1], R[1])
        z = np.linspace(lower[2], upper[2], R[2])
        X, Y, Z = np.meshgrid(x, y, z, indexing='ij')
               
        Q = np.stack((X, Y, Z), 3)
        #get a list of all node positions in the network
        P = []
        #get a mirrored list of all the point labels
        L = []
      
        for e in G.edges():
            X_p = G.edge_properties["x"][e]
            Y_p = G.edge_properties["y"][e]
            Z_p = G.edge_properties["z"][e]
            l = list(np.array([X_p,Y_p,Z_p]).T)
            #generate points list foe each edge
            P = P + l
            #generate labels list for each edge
            if G.vertex_properties["clusters"][e.source()] == G.vertex_properties["clusters"][e.target()]:
                c = G.vertex_properties["clusters"][e.source()]
                for i in range(len(l)):
                    L.append(c)
            #if source and target have the same label, all points have that label
            else:
                #if source != target label, then point takes on the closest label
                source = []
                source.append(0.0)
                target = []
                for i in range(1, len(l)):
                    dist = math.sqrt(pow(l[i][0]-l[i-1][0],2) + pow(l[i][1]-l[i-1][1],2) + pow(l[i][2]-l[i-1][2],2))
                    source.append(dist + source[len(source)-1])
                target = source[::-1]
                for i in range(len(l)):
                    if source[i] > target[i]:
                        L.append(G.vertex_properties["clusters"][e.source()])
                    else:
                        L.append(G.vertex_properties["clusters"][e.target()])
                        
        #turn that list into a Numpy array so that we can create a KD tree
        P = np.array(P)
      
        #generate a KD-Tree out of the network point array
        tree = sp.spatial.cKDTree(P)
        
        D, I = tree.query(Q)
        C = np.zeros(I.shape, dtype=np.int)
        for i in range(I.shape[0]):
            for j in range(I.shape[1]):
                for k in range(I.shape[2]):
                    C[i,j,k] = L[I[i,j,k]]
        
        self.write_VTK_R(G, "./test_field.vtk", Q, C, X, Y, Z)
        return D, x, y, z, C
    """
        Function for saving the distance field based on the clusters in the 
        clustered 3D network.
    """
    def write_VTK_R(self, G, filepath, Q, C, X, Y, Z):\
    
        from pyevtk.hl import gridToVTK
        from pyevtk.hl import imageToVTK
        from pyevtk.vtk import VtkFile, VtkImageData
        import vtk
        #T = C.reshape(C.shape[0]*C.shape[1]*C.shape[2])
        #C = T.reshape((C.shape[0], C.shape[1], C.shape[2]), order = 'C')
        ColorR = np.zeros((C.shape[0], C.shape[1], C.shape[2]))
        ColorG = np.zeros((C.shape[0], C.shape[1], C.shape[2]))
        ColorB = np.zeros((C.shape[0], C.shape[1], C.shape[2]))
        ColorA = np.zeros((C.shape[0], C.shape[1], C.shape[2]))
        #ColorR = ColorG = ColorB = ColorA = np.chararray((P.shape[0], P.shape[1], P.shape[2]))
        G.vertex_properties["RGBA"] = nwt.Network.map_property_to_color(G, G.vertex_properties["clusters"])
        self.color_edges(G)
        thisdict = {}
        otherdict = {}
        for v in G.vertices():
            thisdict[G.vertex_properties["clusters"][v]] = G.vertex_properties["RGBA"][v]
            color = G.vertex_properties["RGBA"][v].get_array()
            c = '#%02x%02x%02x%02x' % (int(color[0]*255), int(color[1]*255), int(color[2]*255), int(color[3]*255*0.3))
            otherdict[G.vertex_properties["clusters"][v]] = c
        
        print(thisdict, file=open('myfile.txt', 'w'))    
        i = 0
        for i in range(C.shape[0]):
            for j in range(C.shape[1]):
                for k in range(C.shape[2]):
                    c = thisdict[C[i,j,k]]
                    ColorR[i,j,k] = c[0]
                    ColorG[i,j,k] = c[1]
                    ColorB[i,j,k] = c[2]
                    ColorA[i,j,k] = c[3]
        
        
    #    fig = plt.figure()
    #    ax = fig.gca(projection='3d')
    #    for e in G.edges():
    #        X = G.edge_properties["x"][e]
    #        Y = G.edge_properties["y"][e]
    #        Z = G.edge_properties["z"][e]
    #        color = G.edge_properties['RGBA'][e].get_array()
    #        c = '#%02x%02x%02x' % (int(color[0]*255), int(color[1]*255), int(color[2]*255))
    #        ax.plot(X,Y,Z, color=c)
    #        print("plotting line")
    #    
    #    
    #    print("generating cells")
    #    x, y, z = np.indices(np.array(C.shape) + 1).astype(float)
    #    filled = np.ones(C.shape)
    #    print("replacing dict")
    #    vox = replace_with_dict(C, otherdict)
    #    
    #    print("plotting voxels")
    #    ax.voxels(x, y, z, filled, facecolors=vox)
        
        
        #ax.imshow(np.stack((ColorR[:,:,0], ColorG[:,:,0], ColorB[:,:,0]), axis = 2))
        
        #plt.show()
            
            
            
        
        
        filename = "./image_fixed.vti"
        imageData = vtk.vtkImageData()
        imageData.SetDimensions(C.shape[0], C.shape[1], C.shape[2])
        imageData.SetOrigin(0.0, 0.0, 0.0)
        imageData.SetSpacing(1.0, 1.0, 1.0)
        if vtk.VTK_MAJOR_VERSION <= 5:
            imageData.SetNumberOfScalarComponents(4)
            imageData.SetScalarTypeToDouble()
        else:
            imageData.AllocateScalars(vtk.VTK_DOUBLE, 4)
            
        for z in range(C.shape[2]):
            for y in range(C.shape[1]):
                for x in range(C.shape[0]):
                    imageData.SetScalarComponentFromDouble(x, y, z, 0, ColorR[x,y,z])
                    imageData.SetScalarComponentFromDouble(x, y, z, 1, ColorG[x,y,z])
                    imageData.SetScalarComponentFromDouble(x, y, z, 2, ColorB[x,y,z])
                    imageData.SetScalarComponentFromDouble(x, y, z, 3, ColorA[x,y,z])
        
        
        writer = vtk.vtkXMLImageDataWriter()
        writer.SetFileName(filename)
        if vtk.VTK_MAJOR_VERSION <= 5:
            writer.SetInputConnection(imageData.GetProducerPort())
        else:
            writer.SetInputData(imageData)
        
        writer.Write()
        nwt.Network.write_vtk(G, "./vessels_fixed.vtk", binning = False)
    def set_graph(self, G, bbl, bbu, subgraph = False):
        self.G = G
        self.bbl = bbl
        self.bbu = bbu
        clear(color=True, depth=True)
        self.subgraphs = subgraph
        self.current_color = "clusters"
        self.color_edges(G)                    
        print(self.G)
        color = G.vertex_properties["RGBA"].get_2d_array(range(4)).T
        size = nwt.Network.map_vertices_to_range(G, [30*self.pixel_scale, 8*self.pixel_scale], 'degree').get_array()
        position = G.vertex_properties["pos"].get_2d_array(range(3)).T
        #for p in range(position.shape[0]):
        #    position[p][0] = position[p][0] + self.clusters["a_position"][G.vertex_properties["clusters"][G.vertex(p)]][0]
        #    position[p][1] = position[p][1] + self.clusters["a_position"][G.vertex_properties["clusters"][G.vertex(p)]][1]
        #    position[p][2] = position[p][2] + self.clusters["a_position"][G.vertex_properties["clusters"][G.vertex(p)]][2]
        #G.vertex_properties["pos"] = G.new_vertex_property("vector<double>", vals = position)
        edges = G.get_edges();
        edges = edges[:, 0:2]
        #width = nwt.Network.map_edges_to_range(G, [1*self.pixel_scale, 5*self.pixel_scale], 'volume').get_array()
        #ecolor = G.edge_properties["RGBA"].get_2d_array(range(4)).T
        self.data = np.zeros(G.num_vertices(), dtype=[('a_position', np.float32, 3),
                  ('a_fg_color', np.float32, 4),
                  ('a_bg_color', np.float32, 4),
                  ('a_size', np.float32, 1),
                  ('a_linewidth', np.float32, 1),
                  ('a_unique_id', np.float32, 4),
                  ('a_selection', np.float32, 1),
                  ])
        #self.edges = edges.astype(np.uint32)
        self.data['a_position'] = position
        #fg color is the color of the ring
        self.data['a_fg_color'] = 0, 0, 0, 1
        self.data['a_bg_color'] = color
        self.data['a_size'] = size
        self.data['a_linewidth'] = 4.*self.pixel_scale
        self.data['a_unique_id'] = self.gen_vertex_id(G)
        self.data['a_selection'] = G.vertex_properties["selection"].get_array()
        #self.data['a_graph_size'] = [bbu-bbl]
        #self.program['u_graph_size'] = [bbu-bbl]
        self.vbo = gloo.VertexBuffer(self.data)
        self.gen_line_vbo(G)
        #self.gen_cylinder_vbo(G)
        if(self.subgraphs):
            self.labels = self.G.vp["clusters"].get_array()
            self.gen_cluster_vbo(self.G, bbl, bbu, self.n_c)
            self.vbo_s = gloo.VertexBuffer(self.clusters)
            self.index_s = gloo.IndexBuffer(self.edges_s)
        #self.index = gloo.IndexBuffer(self.edges)
        self.program_e.bind(self.vbo_line)
        self.program.bind(self.vbo)
        if(self.subgraphs):
            #self.program_e_s.bind(self.vbo_s)
            self.program_s.bind(self.vbo_s)
        if DEBUG:
            print(self.view)
        self.update_text(self.current_color)
        self.update_color_bar(self.current_color)
        self.refresh()
   
    
    def update_text(self, text):
        self.t1 = Text(text, parent=self.scene, color = 'black', method='gpu', anchor_x = 'right', anchor_y='top')
        self.t1.font_size = 24
        #self.t1.anchor_y = 'top'
        #self.t1.anchor_x = 'right'
#        print(self.t1.bounds(0), self.t1.bounds(1))
        self.t1.pos = self.size[0]-10, self.size[1] // 24
        self.refresh()
        
    def update_color_bar(self, color_property):
        if color_property != "":
            prop = self.G.vp[color_property].get_array().T
            mx = max(prop)
            mn = min(prop)
        else:
            mx = 1.0
            mn = 0.0
        if(color_property=="clusters"):
            cm = 'tab20'
        else:
            cm = 'plasma'
        self.c_bar = ColorBar(cmap=cm, orientation = 'bottom', 
                              size = (self.size[0] // 3, self.size[1] // 24), clim = (mn, mx),
                              border_width = 10.0,border_color = 'white', 
                              parent=self.scene, 
                              pos=(self.size[0] // 3 // 2 + 20, self.size[1] // 24),
                              padding = (100, 100)
                              )
        self.refresh()
        
    """
        Loads the data G and generates all the buffers necessary as well as performs
        spectral clustering on the graph passed if the subgraph is set to true.
    """
    def set_data(self, G, bbl, bbu, subgraph=True, G_other = None):
        
        def in_hull(point, hull, tolerance=1e-6):
            return all(
                (np.dot(eq[:-1], point) + eq[-1] <= tolerance)
                for eq in hull.equations)
            
#        def in_hull(p, hull):
#            if not isinstance(hull,Delaunay):
#                hull = Delaunay(hull)
#        
#            return hull.find_simplex(p)>=0
        
        if DEBUG:
            print("Setting data")
        self.G = G
        self.bbl = bbl
        self.bbu = bbu
        clear(color=True, depth=True)
        self.subgraphs = True
        self.current_color = "clusters"
        if(subgraph==True and G_other == None):
            self.gen_clusters(G, bbl, bbu, n_c=19)
            self.G.vertex_properties["idx"] = self.G.vertex_index
            #color based on clusters
            self.color_edges(G)
        else:
            #polygons = []
            self.G.vertex_properties["idx"] = self.G.vertex_index
            self.G.vertex_properties["clusters"] = self.G.new_vertex_property("int", vals=np.full(self.G.num_vertices(), -1, dtype="int"))
            num_clusters = len(np.unique(G_other.vertex_properties["clusters"].get_array().T))
            color_lookup = []
            lbls = G_other.vertex_properties["clusters"].get_array().T
            for i in range(num_clusters):
                idx = np.where(lbls == i)[0][0]
                color_lookup.append(G_other.vertex_properties["RGBA"][G_other.vertex(idx)])
                
                
            self.n_c = num_clusters
            D, x, y, z, C = self.distancefield(G_other)
            print(len(x), len(y), len(z))
            for v in self.G.vertices():
                p = self.G.vertex_properties["p"][v]
                x_temp = np.fabs(x - p[0])
                idx_x = x_temp.argmin()
                y_temp = np.fabs(y - p[1])
                idx_y = y_temp.argmin()
                z_temp = np.fabs(z - p[2])
                idx_z = z_temp.argmin()
                cluster = C[idx_x][idx_y][idx_z]
                self.G.vertex_properties["clusters"][v] = cluster
                self.G.vertex_properties["RGBA"][v] = color_lookup[cluster]
                
            
            
            ###############OLD Comparison mechanic########################
#            polygons = []
#            for i in range(num_clusters):
#                #num_v_in_cluster = len(np.argwhere(self.labels == i))
#                vfilt = np.zeros([G_other.num_vertices(), 1], dtype="bool")
#                vfilt[np.argwhere(G_other.vertex_properties["clusters"].get_array().T == i)] = 1
#                vfilt_prop = G_other.new_vertex_property("bool", vals = vfilt)
#                G_other.set_vertex_filter(vfilt_prop)
#            
#                #get the filtered properties
#                g = nwt.gt.Graph(G_other, prune=True, directed=False)
#                color = g.vertex_properties["RGBA"][g.vertex(0)]
#                positions = g.vertex_properties["p"].get_2d_array(range(3)).T
#                hull = ConvexHull(positions)
#                #hull = Delaunay(positions)
#                #hull = multipoint.convex_hull
#                polygons.append(hull)
#                G_other.clear_filters()
#                for v in self.G.vertices():
#                    if in_hull(self.G.vertex_properties["p"][v], hull) and self.G.vertex_properties["clusters"][v] == -1:
#                        self.G.vertex_properties["clusters"][v] = i
#                        self.G.vertex_properties["RGBA"][v] = color
##                    if hull.contains(Point(self.G.vertex_properties["p"][v])):
##                        self.G.vertex_properties["clusters"][v] = i
##                        self.G.vertex_properties["RGBA"][v] = color
#            
#            unassigned = np.argwhere(self.G.vertex_properties["clusters"].get_array().T == -1)
#            while len(unassigned > 0):
#                for i in range(len(unassigned)):
#                    gen = self.G.vertex(unassigned[i]).all_neighbors()
#                    neighbors = []
#                    neighbors_clusters = []
#                    for j in gen:
#                        neighbors.append(j)
#                        neighbors_clusters.append(self.G.vertex_properties["clusters"][j])
#                    if len(np.unique(neighbors_clusters)) == 1 and np.unique(neighbors_clusters[0]) != -1:
#                        self.G.vertex_properties["clusters"][self.G.vertex(unassigned[i])] = \
#                            self.G.vertex_properties["clusters"][neighbors[0]]
#                        self.G.vertex_properties["RGBA"][self.G.vertex(unassigned[i])] = \
#                            self.G.vertex_properties["RGBA"][neighbors[0]]
#                    else:
#                        c, count = np.unique(neighbors_clusters, return_counts=True)
#                        for k in range(len(c)):
#                            if c[k] == -1:
#                                c = np.delete(c, k)
#                                count = np.delete(count, k)
#                                break
#                        if len(c) > 0:
#                            cluster = np.argwhere(count == max(count))[0]
#                            self.G.vertex_properties["clusters"][self.G.vertex(unassigned[i])] = c[cluster[0]]
#                            for v in range(len(neighbors)):
#                                if self.G.vertex_properties["clusters"][self.G.vertex(unassigned[i])] == c[cluster[0]]:
#                                    self.G.vertex_properties["RGBA"][self.G.vertex(unassigned[i])] = \
#                                        self.G.vertex_properties["RGBA"][neighbors[v]]
#                unassigned = np.argwhere(self.G.vertex_properties["clusters"].get_array().T == -1)
#                    #print("stuff")
#                    #for j in range(len(neighbors_clusters)):
#                        
#            #self.G.vertex_properties["RGBA"] = nwt.Network.map_property_to_color(self.G, self.G.vertex_properties["clusters"])
            temp = self.G.vertex_properties["clusters"].get_array().T
            print(np.unique(temp))
            self.labels = copy.copy(temp)
#            c = np.unique(temp)
#            idx = 0
#            for i in range(len(c)):
#                self.labels[np.argwhere(temp == c[i])] = idx
#                idx += 1
#                
            ###############/OLD Comparison mechanic########################
            self.G.vertex_properties["clusters"] = self.G.new_vertex_property("int", vals = self.labels)
            self.n_c = len(np.unique(self.labels))
            self.gen_cluster_vbo(self.G, bbl, bbu, self.n_c, update_color = False)
            self.color_edges(self.G)
                        
        color = G.vertex_properties["RGBA"].get_2d_array(range(4)).T
        size = nwt.Network.map_vertices_to_range(G, [30*self.pixel_scale, 8*self.pixel_scale], 'degree').get_array()
        position = G.vertex_properties["pos"].get_2d_array(range(3)).T
        #for p in range(position.shape[0]):
        #    position[p][0] = position[p][0] + self.clusters["a_position"][G.vertex_properties["clusters"][G.vertex(p)]][0]
        #    position[p][1] = position[p][1] + self.clusters["a_position"][G.vertex_properties["clusters"][G.vertex(p)]][1]
        #    position[p][2] = position[p][2] + self.clusters["a_position"][G.vertex_properties["clusters"][G.vertex(p)]][2]
        #G.vertex_properties["pos"] = G.new_vertex_property("vector<double>", vals = position)
        edges = G.get_edges();
        edges = edges[:, 0:2]
        #width = nwt.Network.map_edges_to_range(G, [1*self.pixel_scale, 5*self.pixel_scale], 'volume').get_array()
        #ecolor = G.edge_properties["RGBA"].get_2d_array(range(4)).T
        self.data = np.zeros(G.num_vertices(), dtype=[('a_position', np.float32, 3),
                  ('a_fg_color', np.float32, 4),
                  ('a_bg_color', np.float32, 4),
                  ('a_size', np.float32, 1),
                  ('a_linewidth', np.float32, 1),
                  ('a_unique_id', np.float32, 4),
                  ('a_selection', np.float32, 1),
                  ])
        #self.edges = edges.astype(np.uint32)
        self.data['a_position'] = position
        #fg color is the color of the ring
        self.data['a_fg_color'] = 0, 0, 0, 1
        self.data['a_bg_color'] = color
        self.data['a_size'] = size
        self.data['a_linewidth'] = 4.*self.pixel_scale
        self.data['a_unique_id'] = self.gen_vertex_id(G)
        self.data['a_selection'] = G.vertex_properties["selection"].get_array()
        #self.data['a_graph_size'] = [bbu-bbl]
        #self.program['u_graph_size'] = [bbu-bbl]
        self.vbo = gloo.VertexBuffer(self.data)
        self.gen_line_vbo(G)
        #self.gen_cylinder_vbo(G)
        if(self.subgraphs):
            self.vbo_s = gloo.VertexBuffer(self.clusters)
            self.index_s = gloo.IndexBuffer(self.edges_s)
        #self.index = gloo.IndexBuffer(self.edges)
        self.program_e.bind(self.vbo_line)
        self.program.bind(self.vbo)
        if(self.subgraphs):
            #self.program_e_s.bind(self.vbo_s)
            self.program_s.bind(self.vbo_s)
        if DEBUG:
            print(self.view)
        self.update_text(self.current_color)
        self.update_color_bar(self.current_color)
        self.refresh()
    """
        Function that changes and redraws the buffer during a resize event.
    """
    def on_resize(self, event):
        set_viewport(0, 0, *event.physical_size)
        self.fbo = gloo.FrameBuffer(color=gloo.RenderBuffer(self.size[::-1]), depth=gloo.RenderBuffer(self.size[::-1]))
        self.update_text(self.current_color)
        self.update_color_bar(self.current_color)
        self.refresh()
        app.Canvas.update(self)
    """
        Overloaded function that is called during every self.update() call
    """
    def on_draw(self, event):
        gloo.clear()
        clear(color='white', depth=True)
        self.program_e.draw('triangles', indices=self.index)
        self.program.draw('points')
        #self.program_e.draw('lines')
        if(self.subgraphs):
            self.program_e_s.draw('triangles', indices=self.index_clusters_s)
            self.program_s.draw('triangles', indices=self.index_s)
        #print("updated  ", self.num)
        #self.num += 1
        self.t1.draw()
        self.c_bar.draw()
        #self._u
        #self._update_pending = True
        #app.Canvas.update(self)
        #super(scene.SceneCanvas, self).update()    #This forces redrawd
    """
        refreshes the canvas and forces the redraw. A workaround for issue in
        vispy 0.6.3
    """
    def refresh(self):
        self.update_view_global()
        self.update()
        app.Canvas.update(self)
#    """
#        A function to animate from one layout to another layout given a new G.
#    """
    def animate(self, old_pos, new_pos):
            
#        old_pos = self.G.vertex_properties["pos"].get_2d_array(range(3)).T
#        new_pos = new_G.vertex_properties["pos"].get_2d_array(range(3)).T
        #we want to animate the move over 2 seconds. At 60 frames per second
        #we'd get 120 different positions along the transitional distance.
        self.new_pos = new_pos
        self.old_pos = old_pos
        self.slopes = np.zeros(old_pos.shape)
        for i in range(old_pos.shape[0]):
            self.slopes[i, 0] =  (new_pos[i, 0] - old_pos[i, 0])/120.0
            self.slopes[i, 1] =  (new_pos[i, 1] - old_pos[i, 1])/120.0
            
        #self.timer.start()
        #for i in range(120):
        #self.old_pos = np.add(old_pos, self.slopes)
        self.timer.start(iterations=120)
        #self.data['a_position'] = old_pos
        #self.gen_vertex_vbo_minimalist()
        #self.update()
##            self.gen_line_vbo(self.G)
##            self.program_e.bind(self.vbo_line)
##            self.program.bind(self.vbo)
#            self.update()
#            #self.program_e.draw('lines')
#            print(i)
        #self.timer.stop()
        
        #self.G = new_G
        #self.gen_vertex_vbo(self.G)
        #self.update()
        
#    def on_timer(self, event):
#        self.vbo = gloo.VertexBuffer(self.data)
#        #self.gen_line_vbo(self.G)
#        #self.program_e.bind(self.vbo_line)
#        self.program.bind(self.vbo)
#        self.update()
#        gloo.wrappers.flush()
    """
        Function performed during a mouse click (either right or left)
        gets the unique id of the object drawn underneath the cursor
        handles the cases depending on whether the click happened to a cluster
        or a vertex. Edges are not interactable (yet)
    """
    def get_clicked_id(self, event, clusters = False):
        #Get the framebuffer coordinates of the click
        coord = self.transforms.get_transform('canvas', 'framebuffer').map(event.pos)
        #get the framebuffer where each element is rendered as a unique color
        size = self.size;
        self.fbo = gloo.FrameBuffer(color=gloo.RenderBuffer(size[::-1]), depth=gloo.RenderBuffer(size[::-1]))
        buff = gloo.read_pixels((0,0,self.physical_size[0], self.physical_size[1]))
        #imsave("test_ori.png", buff)
        self.fbo.activate()
        if clusters == False:
            self.refresh()
            self.program['u_picking'] = True
            clear(color='white', depth=True)
            self.program.draw('points')
            buff = gloo.read_pixels((0,0,self.physical_size[0], self.physical_size[1]))
            #imsave("test.png", buff)
            #return to the original state
            self.fbo.deactivate()
            self.program['u_picking'] = False
        else:
            self.program_s['u_picking'] = True
            clear(color='white', depth=True)
            self.program_s.draw('triangles', indices=self.index_s)
            buff = gloo.read_pixels((0,0,self.physical_size[0], self.physical_size[1]))
            #imsave("test.png", buff)
            #return to the original state
            self.fbo.deactivate()
            self.program_s['u_picking'] = False
        #print(buff[self.physical_size[1]-int(coord[1]), int(coord[0])])
        #Get the color under the click.
        #Keep in mind that the buff is y, x
        #And 0,0 is in the top RIGHT corner.
        #IGNORE THE DOCUMENTATION
        color = np.multiply(buff[self.physical_size[1]-int(coord[1]), int(coord[0])], 1/255.0)
        #if (tuple(color) not in self.color_dict):
        #    print("clicked on nothing")
        #else:
        #    print(self.color_dict[tuple(color)])
        #reset the original buffer
        self.refresh()
        #Return the element under the click.
        if clusters == False:
            if(tuple(color) not in self.color_dict):
                return None
            else:
                return self.color_dict[tuple(color)]
        else:
            if(tuple(color) not in self.cluster_dict):
                return None
            else:
                return self.cluster_dict[tuple(color)]
    def update_view_global(self):
        self.program['u_view'] = self.view
        self.program_e['u_view'] = self.view
        self.program_s['u_view'] = self.view
        self.program_e_s['u_view'] = self.view
    """
        Top level handle-mouse presee event for either left or right click
    """
    def on_mouse_press(self, event):
        def update_view():
            self.location = event.pos
            self.program['u_view'] = self.view
            self.program_e['u_view'] = self.view
            self.program_s['u_view'] = self.view
            self.program_e_s['u_view'] = self.view
            self.down = True
#        if(event.button == 2):
##            menu = QtWidgets.QMenu(self.parent)
##            NS = menu.addAction('Node Size')
##            NC = menu.addAction('Node Color')
##            action = menu.exec_(self.parent.globalPos())
##            if action == NS:
#            print("right_click")
#            #if menu.exec_(event.globalPos()):
#            #    print(item.text())
        if(event.button == 1):
            if(self.view[0][0] > 0.0024):
                self.refresh()
                c_id = self.get_clicked_id(event)
                self.refresh()
                if(c_id != None):
                    self.original_point = self.G.vertex_properties["pos"][self.G.vertex(c_id)]
                    self.location = event.pos
                    self.moving = True
                    self.down = True
                    self.c_id = [c_id]
                else:
                    update_view()
                    #print("Clicked on:", event.pos)
            else:
#                c_id = None
                self.refresh()
                c_id = self.get_clicked_id(event, True)
                self.refresh()
                if DEBUG:
                    print(c_id)
                if(c_id != None):
                    self.original_point = self.cluster_pos[c_id]
                    self.location = event.pos
                    self.moving = True
                    self.down = True
                    self.c_id = [c_id]
                    self.moving_cluster = True
                else:
                    update_view()
    """
        Gets the path and formats it in terms of vertex-to-vertex
        instead of source(obj)-to-source(obj)
    """
    
    def get_cluster(self, cluster_id):
        p = []
        num_v_in_cluster = len(np.argwhere(self.labels == cluster_id))
        vfilt = np.zeros([self.G.num_vertices(), 1], dtype="bool")
        vfilt[np.argwhere(self.labels == cluster_id)] = 1
        vfilt_prop = self.G.new_vertex_property("bool", vals = vfilt)
        self.G.set_vertex_filter(vfilt_prop)
    
        #get the filtered properties
        g = nwt.gt.Graph(self.G, prune=True, directed=False)
        self.G.clear_filters()
        for e in g.edges():
            source = g.vp["idx"][e.source()]
            target = g.vp["idx"][e.target()]
            temp = (int(source), int(target))
            p.append(temp)
            
        return p
    
    
    def get_path(self):
        p = []
        for s in self.path:
            for e in s.e_path:
                temp = (int(e.source()), int(e.target()))
                if (temp not in p):
                    p.append(temp)
                    
        return p
        
    def update_path(self, event):
        #Method to update the vertex buffer of the nodes in the graph view
        def update_vbo(self):
            self.vbo = gloo.VertexBuffer(self.data)
            self.program.bind(self.vbo)
            self.refresh()
        def update_vertex_alpha(self, vertex, alpha):
            temp = self.G.vertex_properties["RGBA"][vertex]
            temp[3] = alpha
            self.G.vertex_properties["RGBA"][vertex] = temp
        #updates the path structure of the class
        #source and target are of type "source" defined in this class.
        def add_to_path(self, source, target):
            vl, el = nwt.gt.graph_tool.topology.shortest_path(self.G, self.G.vertex(source.idx), self.G.vertex(target.idx), weights=self.G.edge_properties["av_radius"])
            for v in range(1, len(vl)-1):
                if (self.G.vertex_properties["selection"][vl[v]] != 1.0):
                    self.G.vertex_properties["selection"][vl[v]] = 2.0
                    update_vertex_alpha(self, self.G.vertex(vl[v]), 1.0)
                    self.data['a_selection'][int(vl[v])] = 2.0
                    self.G.vp["exclude"][self.G.vertex(vl[v])] = True
                source.v_path.append(int(vl[v]))
            for e in el:
                source.e_path.append(e)
                temp = self.G.edge_properties["RGBA"][e]
                temp[3] = 1.0
                self.G.edge_properties["RGBA"][e] = temp
                self.G.edge_properties["exclude"][e] = True
                
        
        def remove_from_path(self, source):
            for v in source.v_path:
                self.G.vertex_properties["selection"][self.G.vertex(v)] = 0.0
                update_vertex_alpha(self,self.G.vertex(v), 0.5)
                self.data['a_selection'][v] = 0.0
                self.G.vertex_properties["exclude"][v] = False
            for e in source.e_path:
                temp = self.G.edge_properties["RGBA"][e]
                temp[3] = 0.0
                self.G.edge_properties["RGBA"][e] = temp
                self.G.edge_properties["exclude"][e] = False
            source.clear_path()
        if (event.button == 1):
            if(self.view[0][0] > 0.0024):
                self.refresh()
                c_id = self.get_clicked_id(event)
                self.refresh()
            if(c_id != None):
                #check whether this is the first node to be selected
                if(self.pathing == False):
                    #if it is, select that node and turn the pathing variable on.
                    self.G.vertex_properties["selection"][self.G.vertex(c_id)] = 1.0
                    self.pathing = True
                    self.path.append(path_point(c_id))
                    self.data['a_selection'][c_id] = 1.0
                    self.make_all_transparent(0.5)
                    update_vertex_alpha(self, self.G.vertex(c_id), 1.0)
                    self.update_color_buffers()
                    print("I turned on the first node")
                else:
                    #If the node is selected already, unselect it and remove from path the last occurance in the path
                    if(self.G.vertex_properties["selection"][self.G.vertex(c_id)] == 1.0):
                        self.G.vertex_properties["selection"][self.G.vertex(c_id)] = 0.0
                        update_vertex_alpha(self, self.G.vertex(c_id), 1.0)
                        self.data['a_selection'][c_id] = 0.0
                        s_id = self.path.index(path_point(c_id))
                        if(s_id == 0):
                            remove_from_path(self, self.path[s_id])
                        else:
                            remove_from_path(self, self.path[s_id-1])
                            remove_from_path(self, self.path[s_id])
                        self.path.remove(path_point(c_id))
                        #self.data['a_selection'][c_id] = 0.0
                        #update_vbo(self)
                        print("I turned off a node")
                    elif(self.G.vertex_properties["selection"][self.G.vertex(c_id)] == 0.0):
                        self.G.vertex_properties["selection"][self.G.vertex(c_id)] = 1.0
                        #if the source is not in the path add it
                        if(path_point(c_id) not in self.path):
                            self.path.append(path_point(c_id))
                            self.G.vertex_properties["selection"][self.G.vertex(c_id)] = 1.0
                            update_vertex_alpha(self, self.G.vertex(c_id), 1.0)
                            self.data['a_selection'][c_id] = 1.0
                        #if the source is not LAST in the path, add it.
                        elif(self.path[len(self.path)-1] != path_point(c_id)):
                            self.path.append(path_point(c_id))
                            self.G.vertex_properties["selection"][self.G.vertex(c_id)] = 1.0
                            update_vertex_alpha(self, self.G.vertex(c_id), 1.0)
                            self.data['a_selection'][c_id] = 1.0
                        print("I turned on a node")
                    if(len(self.path) >= 1):
                        for i in range(len(self.path)-1):
                            add_to_path(self, self.path[i], self.path[i+1])
                        self.update_color_buffers()
                            #THIS IS WHERE I LEFT IT OFF.
                    if(np.sum(self.G.vertex_properties["selection"].get_array()) == 0):
                        self.pathing = False
                        self.make_all_transparent(1.0)
                        self.update_color_buffers()
                        self.refresh()
#                elif(np.sum(self.G.vertex_properties["selection"].get_array()) :
#                    self.G.vertex_properties["selection"][self.G.vertex(c_id)] == False
                print("clicked on: ", c_id, " ", self.path)
    """
        Handles the double click event that it responsible for path selection.
        Generates paths our of consecutive paths out of the selected vertices.
    """
    def on_mouse_double_click(self, event):
        n=1
                
    """
        Resets the variables that are used during the pressdown and move events
    """
    def on_mouse_release(self, event):
        self.down = False
        self.moving = False
        self.moving_cluster = False
        self.c_id = []
        #self.location = event.pos
        #print("Clicked off:", event.pos)
    """
        used during the drag evern to update the position of the clusters
    """
    def update_cluster_position(self, G, pos, offset, c_id):
        v_pos = G.vertex_properties["pos"].get_2d_array(range(3)).T
        vertices = np.argwhere(self.labels == c_id)
        for v in range(vertices.shape[0]):
            idx = vertices[v][0]
            v_pos[idx][0] = v_pos[idx][0] + offset[0]
            v_pos[idx][1] = v_pos[idx][1] + offset[1]
            v_pos[idx][2] = v_pos[idx][2] + offset[2]
            self.data['a_position'][idx] = np.asarray([v_pos[idx][0], v_pos[idx][1], v_pos[idx][2]], dtype = np.float32)
            #update the edge data by finding all edges connected to the vertex
            vtx = self.G.vertex(idx)
            for e in vtx.all_edges():
                d = np.subtract(G.vertex_properties["pos"][e.source()], G.vertex_properties["pos"][e.target()])
                d_norm = d[0:2]
                d_norm = d_norm / np.sqrt(np.power(d_norm[0],2) + np.power(d_norm[1],2))
                norm = np.zeros((2,), dtype=np.float32)
                norm[0] = d_norm[1]
                norm[1] = d_norm[0]*-1
                if (int(e.source()), int(e.target())) in self.edge_dict.keys():
                    index = int(self.edge_dict[int(e.source()), int(e.target())])
                    if vtx == int(e.source()):
                        self.line_data['a_position'][index*4]   = v_pos[idx]
                        self.line_data['a_position'][index*4+2] = v_pos[idx]
                        self.line_data['a_normal'][index*4] = norm
                        self.line_data['a_normal'][index*4+2] = -norm
                        self.line_data['a_normal'][index*4+1] = norm
                        self.line_data['a_normal'][index*4+3] = -norm
                    elif vtx == int(e.target()):
                        self.line_data['a_position'][index*4+1] = v_pos[idx]
                        self.line_data['a_position'][index*4+3] = v_pos[idx]
                        self.line_data['a_normal'][index*4] = norm
                        self.line_data['a_normal'][index*4+2] = -norm
                        self.line_data['a_normal'][index*4+1] = norm
                        self.line_data['a_normal'][index*4+3] = -norm
                else:
                    index = int(self.edge_dict[int(e.target()), int(e.source())])
                    if vtx == int(e.target()):
                        self.line_data['a_position'][index*4]   = v_pos[idx]
                        self.line_data['a_position'][index*4+2] = v_pos[idx]
                        self.line_data['a_normal'][index*4] = norm
                        self.line_data['a_normal'][index*4+2] = -norm
                        self.line_data['a_normal'][index*4+1] = norm
                        self.line_data['a_normal'][index*4+3] = -norm
                    elif vtx == int(e.source()):
                        self.line_data['a_position'][index*4+1] = v_pos[idx]
                        self.line_data['a_position'][index*4+3] = v_pos[idx]
                        self.line_data['a_normal'][index*4] = norm
                        self.line_data['a_normal'][index*4+2] = -norm
                        self.line_data['a_normal'][index*4+1] = norm
                        self.line_data['a_normal'][index*4+3] = -norm
        G.vertex_properties["pos"] = G.new_vertex_property("vector<double>", vals = v_pos)
        index = 4*c_id
        #generate the vertex filter for this cluster
        vfilt = np.zeros([G.num_vertices(), 1], dtype="bool")
        vfilt[np.argwhere(self.labels == c_id)] = 1
        vfilt_prop = G.new_vertex_property("bool", vals = vfilt)
        G.set_vertex_filter(vfilt_prop)
        #get the filtered properties
        g = nwt.gt.Graph(G, prune=True, directed=False)
        p, v = self.gen_cluster_coords(pos, self.cluster_size[c_id])
        self.clusters['a_position'][index:index+4] = np.asarray(p, dtype=np.float32)
        self.clusters['a_value'][index:index+4] = np.asarray(v, dtype=np.float32)
        G.clear_filters()
        self.cluster_pos[c_id] = pos
        self.original_point = pos
    """
        function that handles the mouse move event in a way that depends on a set
        of variables: state of the mouse button, the type of object selected and
        the number of objects.
    """
    def on_mouse_move(self, event):
        if(self.down == True):
            if(self.moving == True and self.moving_cluster == False):
                if(len(self.c_id) < 2):
                    #Project into GLSpace and get before and after move coordinates
                    coord = self.transforms.get_transform('canvas', 'render').map(self.location)[:2]
                    coord2 = self.transforms.get_transform('canvas', 'render').map(event.pos)[:2]
                    cur_pos = self.G.vertex_properties["pos"][self.G.vertex(self.c_id[0])]
                    #print(cur_pos, " Before")
                    #Adjust the position of the node based on the current view matrix.
                    cur_pos[0] = cur_pos[0] - (coord[0]-coord2[0])/self.view[0][0]
                    cur_pos[1] = cur_pos[1] - (coord[1]-coord2[1])/self.view[0][0]
                    #print(cur_pos, " After")
                    #Upload the changed data.
                    self.G.vertex_properties["pos"][self.G.vertex(self.c_id[0])] = cur_pos
                    self.data['a_position'][self.c_id[0]] = cur_pos
                    #update the edge data by finding all edges connected to the vertex
                    v = self.G.vertex(self.c_id[0])
                    for e in v.all_edges():
                        d = np.subtract(self.G.vertex_properties["pos"][e.source()], self.G.vertex_properties["pos"][e.target()])
                        d_norm = d[0:2]
                        d_norm = d_norm / np.sqrt(np.power(d_norm[0],2) + np.power(d_norm[1],2))
                        norm = np.zeros((2,), dtype=np.float32)
                        norm[0] = d_norm[1]
                        norm[1] = d_norm[0]*-1
                        if (int(e.source()), int(e.target())) in self.edge_dict.keys():
                            idx = int(self.edge_dict[int(e.source()), int(e.target())])
                            if self.c_id[0] == int(e.source()):
                                self.line_data['a_position'][idx*4] = cur_pos
                                self.line_data['a_position'][idx*4+2] = cur_pos
                                self.line_data['a_normal'][idx*4] = norm
                                self.line_data['a_normal'][idx*4+2] = -norm
                                self.line_data['a_normal'][idx*4+1] = norm
                                self.line_data['a_normal'][idx*4+3] = -norm
                            elif self.c_id[0] == int(e.target()):
                                self.line_data['a_position'][idx*4+1] = cur_pos
                                self.line_data['a_position'][idx*4+3] = cur_pos
                                self.line_data['a_normal'][idx*4] = norm
                                self.line_data['a_normal'][idx*4+2] = -norm
                                self.line_data['a_normal'][idx*4+1] = norm
                                self.line_data['a_normal'][idx*4+3] = -norm
                        else:
                            idx = int(self.edge_dict[int(e.target()), int(e.source())])
                            if self.c_id[0] == int(e.target()):
                                self.line_data['a_position'][idx*4] = cur_pos
                                self.line_data['a_position'][idx*4+2] = cur_pos
                                self.line_data['a_normal'][idx*4] = norm
                                self.line_data['a_normal'][idx*4+2] = -norm
                                self.line_data['a_normal'][idx*4+1] = norm
                                self.line_data['a_normal'][idx*4+3] = -norm
                            elif self.c_id[0] == int(e.source()):
                                self.line_data['a_position'][idx*4+1] = cur_pos
                                self.line_data['a_position'][idx*4+3] = cur_pos
                                self.line_data['a_normal'][idx*4] = norm
                                self.line_data['a_normal'][idx*4+2] = -norm
                                self.line_data['a_normal'][idx*4+1] = norm
                                self.line_data['a_normal'][idx*4+3] = -norm
                    #self.line_data['a_position'][self.c_id[0]] =
                    self.vbo = gloo.VertexBuffer(self.data)
                    self.vbo_line = gloo.VertexBuffer(self.line_data)
                    #Bind the buffer and redraw.
                    self.program.bind(self.vbo)
                    self.program_e.bind(self.vbo_line)
                    #self.program.draw('points')
                    self.location = event.pos
                    self.refresh()
            elif(self.moving == True and self.moving_cluster == True):
                if(len(self.c_id) < 2):
                    #Project into GLSpace and get before and after move coordinates
                    coord = self.transforms.get_transform('canvas', 'render').map(self.location)[:2]
                    coord2 = self.transforms.get_transform('canvas', 'render').map(event.pos)[:2]
                    cur_pos = np.zeros(self.cluster_pos[self.c_id[0]].shape, dtype = np.float32)
                    offset = np.zeros(self.cluster_pos[self.c_id[0]].shape, dtype = np.float32)
                    cur_pos[0] = self.cluster_pos[self.c_id[0]][0]
                    cur_pos[1] = self.cluster_pos[self.c_id[0]][1]
                    cur_pos[2] = self.cluster_pos[self.c_id[0]][2]
                    offset[0] = self.cluster_pos[self.c_id[0]][0]
                    offset[1] = self.cluster_pos[self.c_id[0]][1]
                    offset[2] = self.cluster_pos[self.c_id[0]][2]
#                    ofset = self.cluster_pos[self.c_id[0]]
                    #Adjust the position of the node based on the current view matrix.
                    offset[0] = self.original_point[0] - cur_pos[0] - (coord[0]-coord2[0])/self.view[0][0]
                    offset[1] = self.original_point[1] - cur_pos[1] - (coord[1]-coord2[1])/self.view[0][0]
                    cur_pos[0] = cur_pos[0] - (coord[0]-coord2[0])/self.view[0][0]
                    cur_pos[1] = cur_pos[1] - (coord[1]-coord2[1])/self.view[0][0]
                    self.update_cluster_position(self.G, cur_pos, offset, self.c_id[0])
                    #self.original_point = cur_pos
                    self.vbo = gloo.VertexBuffer(self.data)
                    self.vbo_line = gloo.VertexBuffer(self.line_data)
                    #Bind the buffer and redraw.
                    self.program.bind(self.vbo)
                    self.program_e.bind(self.vbo_line)
                    #self.program.draw('points')
                    self.location = event.pos
                    if(self.subgraphs):
                        self.vbo_s = gloo.VertexBuffer(self.clusters)
                        self.program_s.bind(self.vbo_s)
                    self.update_cluster_line_vbo()
                    self.refresh()
            else:
            #print("Mouse at:", event.pos)
            #new_model = np.eye(4, dtype=np.float32)
                coord = self.transforms.get_transform('canvas', 'render').map(self.location)[:2]
                coord2 = self.transforms.get_transform('canvas', 'render').map(event.pos)[:2]
                self.translate[0] += (coord[0]-coord2[0])/self.view[0][0]
                self.translate[1] += (coord[1]-coord2[1])/self.view[1][1]
                #self.view[3][0] = self.view[3][0]-(self.location[0]-event.pos[0])/10000.0
                #self.view[3][1] = self.view[3][1]+(self.location[1]-event.pos[1])/10000.0
                self.view = np.matmul(translate((self.translate[0], self.translate[1], 0)), scale((self.scale[0], self.scale[1], 0)))
                self.program['u_view'] = self.view
                self.program_e['u_view'] = self.view
                self.program_s['u_view'] = self.view
                self.program_e_s['u_view'] = self.view
                self.location = event.pos
                self.refresh()
    def set_view_matrix(self, matrix):
        self.view = matrix
        self.program['u_view'] = self.view
        self.program_e['u_view'] = self.view
        self.program_s['u_view'] = self.view
        self.program_e_s['u_view'] = self.view
        self.refresh()
    def center_camera_on(self, camera):
        self.translate[0] += (0.0 - camera[0])/self.view[0][0]
        self.translate[1] += (0.0 - camera[1])/self.view[1][1]
        #self.view[3][0] = self.view[3][0]-(self.location[0]-event.pos[0])/10000.0
        #self.view[3][1] = self.view[3][1]+(self.location[1]-event.pos[1])/10000.0
        self.view = np.matmul(translate((self.translate[0], self.translate[1], 0)), scale((self.scale[0], self.scale[1], 0)))
        self.program['u_view'] = self.view
        self.program_e['u_view'] = self.view
        self.program_s['u_view'] = self.view
        self.program_e_s['u_view'] = self.view
        self.refresh()
    def zoom_camera_on(self, zoom):
        self.scale[0] = zoom[0]
        self.scale[1] = zoom[1]
        self.view = np.matmul(translate((self.translate[0], self.translate[1], 0)),
        scale((self.scale[0], self.scale[1], 0)))
        self.program['u_view'] = self.view
        self.program_e['u_view'] = self.view
        self.program_s['u_view'] = self.view
        self.program_e_s['u_view'] = self.view
        #print(event.delta[1])
        self.refresh()
    """
        Handles the mouse wheel zoom event.
    """
    def on_mouse_wheel(self, event):
        #print(self.view)
        #TO_DO IMPLEMENT ZOOM TO CURSOR
        #self.view[3][0] = self.view[3][0]-event.pos[0]/10000.0
        #self.view[3][1] = self.view[3][1]-event.pos[1]/10000.0
        #print(self.scale[0] , self.scale[0]*event.delta[1]*0.05)
        self.scale[0] = self.scale[0] + self.scale[0]*event.delta[1]*0.05
        self.scale[1] = self.scale[1] + self.scale[1]*event.delta[1]*0.05
        self.view = np.matmul(translate((self.translate[0], self.translate[1], 0)),
        scale((self.scale[0], self.scale[1], 0)))
        #self.view[0][0] = self.view[0][0]+self.view[0][0]*event.delta[1]*0.05
        #self.view[1][1] = self.view[1][1]+self.view[1][1]*event.delta[1]*0.05
        #print(self.view[0][0], " ",self.view[1][1])
        #print(self.view)
        self.program['u_view'] = self.view
        self.program_e['u_view'] = self.view
        self.program_s['u_view'] = self.view
        self.program_e_s['u_view'] = self.view
        #print(event.delta[1])
        self.refresh()