我们从Python开源项目中，提取了以下50个代码示例，用于说明如何使用networkx.draw_networkx_edges()。
def create_image(g, path): path = os.path.relpath(path) if pygraphviz: a = nx.nx_agraph.to_agraph(g) # ['neato'|'dot'|'twopi'|'circo'|'fdp'|'nop'] a.layout(prog='neato', args="-Goverlap=false -Gsplines=true") # splines=true a.draw(path) elif plt: nodes = g.nodes(True) colors = [attrs['color'] for n, attrs in nodes] labels = {n: attrs['label'] for n, attrs in nodes} if graphviz_layout: pos = graphviz_layout(g) else: pos = nx.spring_layout(g) nx.draw_networkx_nodes(g, pos, node_shape='o', node_color=colors, alpha=0.3) nx.draw_networkx_edges(g, pos, style='solid', alpha=0.2) nx.draw_networkx_labels(g, pos, labels, alpha=0.5) # plt.show() plt.imsave(path) # todo: this is not tested! print 'Image saved to', path
def hungarian(B ,pos ,cost): n = len(cost) ret = 0; max_match = 0 xy = [-1] * n yx = [-1] * n slack = [0] * n slackx = [0] * n lx, ly = init_labels(cost) augment(cost, max_match, xy, yx, lx, ly, slack, slackx) for x in range(n): if (x, chr(xy[x]+97)) in B.edges(): nx.draw_networkx_edges(B, pos, edgelist = [(x, chr(xy[x]+97))], width = 2.5, alpha = 0.6, edge_color = 'r') #takes input from the file and creates a weighted bipartite graph
def BFS(G, source, pos): visited = [False]*(len(G.nodes())) queue = [] #a queue for BFS traversal queue.append(source) visited[source] = True while queue: curr_node = queue.pop(0) for i in G[curr_node]: #iterates through all the possible vertices adjacent to the curr_node if visited[i] == False: queue.append(i) visited[i] = True # nx.draw_networkx_edges(G, pos, edgelist = [(curr_node,i)], width = 2.5, alpha = 0.6, edge_color = 'r') return #takes input from the file and creates a weighted graph
def DrawDFSPath(G, dfs_stk): pos = nx.spring_layout(G) nx.draw(G, pos, with_labels = True) #with_labels=true is to show the node number in the output graph edge_labels = dict([((u,v,), d['length']) for u, v, d in G.edges(data = True)]) nx.draw_networkx_edge_labels(G, pos, edge_labels = edge_labels, label_pos = 0.3, font_size = 11) #prints weight on all the edges for i in dfs_stk: #if there is more than one node in the dfs-forest, then print the corresponding edges if len(i) > 1: for j in i[ :(len(i)-1)]: if i[i.index(j)+1] in G[j]: nx.draw_networkx_edges(G, pos, edgelist = [(j,i[i.index(j)+1])], width = 2.5, alpha = 0.6, edge_color = 'r') else: #if in case the path was reversed because all the possible neighbours were visited, we need to find the adj node to it. for k in i[1::-1]: if k in G[j]: nx.draw_networkx_edges(G, pos, edgelist = [(j,k)], width = 2.5, alpha = 0.6, edge_color = 'r') break #main function
def draw_transmat_graph_inner(G, edge_threshold=0, lw=1, ec='0.2', node_size=15): num_states = G.number_of_nodes() edgewidth = [ d['weight'] for (u,v,d) in G.edges(data=True)] edgewidth = np.array(edgewidth) edgewidth[edgewidth<edge_threshold] = 0 npos=circular_layout(G, scale=1, direction='CW') nx.draw_networkx_edges(G, npos, alpha=1.0, width=edgewidth*lw, edge_color=ec) nx.draw_networkx_nodes(G, npos, node_size=node_size, node_color='k',alpha=1.0) ax = plt.gca() ax.set_aspect('equal') return ax
def draw_transmat_graph_outer(Go, Gi, edge_threshold=0, lw=1, ec='0.2', nc='k', node_size=15): num_states = Go.number_of_nodes() edgewidth = [ d['weight'] for (u,v,d) in Go.edges(data=True)] edgewidth = np.array(edgewidth) edgewidth[edgewidth<edge_threshold] = 0 npos=double_circular_layout(Gi, scale=1, direction='CW') nx.draw_networkx_edges(Go, npos, alpha=1.0, width=edgewidth*lw, edge_color=ec) nx.draw_networkx_nodes(Go, npos, node_size=node_size, node_color=nc,alpha=1.0) ax = plt.gca() ax.set_aspect('equal') return ax
def save(self, path='out.png'): import networkx import matplotlib.pyplot as plt pos = networkx.spring_layout(self.graph, iterations=500) # pos = networkx.spectral_layout(self.graph) # pos = networkx.shell_layout(self.graph) # pos = networkx.fruchterman_reingold_layout(self.graph) nodelist = list(range(self.num_rooms)) networkx.draw_networkx_nodes(self.graph, pos, nodelist=nodelist) edgelist = sorted(self.edges - self.secret_edges) secret = sorted(self.secret_edges) networkx.draw_networkx_edges(self.graph, pos, edgelist=edgelist, edge_color='k') networkx.draw_networkx_edges(self.graph, pos, edgelist=secret, edge_color='r') networkx.draw_networkx_labels(self.graph, pos, self.labels) plt.savefig(path)
def Drawsubgraph(HG, DrawGraph): #Draw the graph #print HG.nodes() pos=nx.spring_layout(HG) nx.draw_networkx_edges(HG, pos, alpha=0.4) #nx.draw_networkx_labels(HG, pos, font_size=10, font_family='sans-serif') i = 0 colorList = ['SeaGreen','yellow','brown','pink','purple','blue','green','Salmon','red','c','magenta','orange','white','black','y','skyblue','GreenYellow','cyan']#,'aqua' for key in DrawGraph.keys(): nx.draw_networkx_nodes(HG, pos, nodelist=DrawGraph[key], node_size=20,node_color=colorList[i%len(colorList)]) i = i + 1 plt.title("Network Community Analysis") plt.show() ###========================================================================================
def drawcomgraph(HG, DrawGraph): #Draw the graph #print HG.nodes() pos=nx.spring_layout(HG) nx.draw_networkx_edges(HG, pos, alpha=0.4) nx.draw_networkx_labels(HG, pos, font_size=6, font_family='sans-serif') i = 0 colorList = ['SeaGreen','yellow','brown','pink','purple','blue','green','Salmon','red','c','magenta','orange','white','black','y','skyblue','GreenYellow','cyan']#,'aqua' for key in DrawGraph.keys(): nx.draw_networkx_nodes(HG, pos, nodelist=DrawGraph[key], node_size=100,node_color=colorList[i%len(colorList)]) i = i + 1 plt.title("Network Community Analysis") plt.show() #************************************************************************
def plot_networkx(self, with_label=False, block=True): nx.draw_networkx_edges( self.G, self.pos, edge_color=self.edges_color(self.G.edges()), alpha=0.08 ) if with_label: nx.draw_networkx_labels( self.G, self.pos, font_size=8, font_color='r', alpha=0.2 ) nodes = self.G.nodes() nx.draw_networkx_nodes( self.G, self.pos, node_size=self.nodes_size(nodes), node_color=self.nodes_color(nodes), alpha=0.85 ) plt.axis('off') plt.show(block=block) plt.clf()
def draw_graph(edges): G = nx.DiGraph() G.add_edges_from(edges) values = [1.0 for node in G.nodes()] # Specify the edges you want here edge_colours = ['black' for edge in G.edges()] black_edges = [edge for edge in G.edges()] # Need to create a layout when doing # separate calls to draw nodes and edges pos = nx.spring_layout(G) nx.draw_networkx_nodes(G, pos, cmap=plt.get_cmap('Reds'), node_color = values, node_size=4800) nx.draw_networkx_edges(G, pos, edgelist=black_edges, arrows=True) nx.draw_networkx_labels(G,pos,font_size=12,font_family='sans-serif') plt.axis('off') plt.show() # In[183]:
def plotRoadsGraphNetworkx(G): import matplotlib.pyplot as plt eoriginal=[(u,v) for (u,v,d) in G.edges(data=True) if d['level'] >= 3] enew=[(u,v) for (u,v,d) in G.edges(data=True) if d['level'] < 3] pos={} for n in G.nodes(): pos[n] = (G.node[n]['longitude'],G.node[n]['latitude']) # nodes nx.draw_networkx_nodes(G,pos,node_size=3) # edges nx.draw_networkx_edges(G,pos,edgelist=eoriginal,alpha=0.8,width=1) nx.draw_networkx_edges(G,pos,edgelist=enew,width=1,alpha=0.8,edge_color='r') # labels nx.draw_networkx_labels(G,pos,font_size=20,font_family='sans-serif') plt.show()
def drawRoads(G, edgeColorAttribute='level', edgeColorScale=9, nodeColorAttribute=None, nodeColorScale=None): pos={} for n in G.nodes(): pos[n] = (G.node[n]['longitude'],G.node[n]['latitude']) # nodes nx.draw_networkx_nodes(G,pos,node_size=0,alpha=0.8,color=colors[-1]) # edges for i in range(0,9): selectedEdges = [(u,v) for (u,v,d) in G.edges(data=True) if d['level'] == i] selectedColors = [colors[i] for e in selectedEdges] nx.draw_networkx_edges(G,pos,edgelist=selectedEdges,width=edgeWidth[i],edge_color=selectedColors) plt.show()
def draw_tier2(): print 'loading tier2 data' loadEntity(tier2filename) print 'entity size: ', len(entityList) G = nx.Graph() G.add_node(u'???') for entity in entityList: name = entity.name[0].decode('utf8') print name G.add_node(name) G.add_edge(u'???',name) for child in entity.child: cn = child.decode('utf8') G.add_node(cn) G.add_edge(name, cn) pos=nx.spring_layout(G) # positions for all nodes nx.draw_networkx_edges(G,pos,width=1.0,alpha=0.5) # labels nx.draw_networkx_labels(G,pos,font_size=15,font_family='sans-serif') plt.axis('off') plt.show() # draw tier 3 test
def prepare_plot(graph): """ Prepares a Matplotlib plot for further handling :param graph: datamodel.base.Graph instance :return: None """ G = graph.nxgraph # Color map for nodes: color is proportional to depth level # http://matplotlib.org/examples/color/colormaps_reference.html depth_levels_from_root = nx.shortest_path_length(G, graph.root_node) vmax = 1. colormap = plt.get_cmap('BuGn') step = 1./len(graph) node_colors = [vmax - step * depth_levels_from_root[n] for n in G.nodes()] # Draw! # https://networkx.github.io/documentation/networkx-1.10/reference/drawing.html pos = nx.spectral_layout(G) nx.draw_networkx_labels(G, pos, labels=dict([(n, n.name) for n in G.nodes()]), font_weight='bold', font_color='orangered') nx.draw_networkx_nodes(G, pos, node_size=2000, cmap=colormap, vmin=0., vmax=vmax, node_color=node_colors) nx.draw_networkx_edge_labels(G, pos, edge_labels=dict([((u, v,), d['name']) for u, v, d in G.edges(data=True)])) nx.draw_networkx_edges(G, pos, edgelist=[edge for edge in G.edges()], arrows=True)
def plot2d(self, title=None, domain=[-1, 1], codomain=[-1, 1], predict=True): f, ax = plt.subplots() x1 = np.linspace(*domain, 100) x2 = np.linspace(*codomain, 100) n_samples, n_features = self.X_.shape G = nx.from_scipy_sparse_matrix(self.A_) pos = {i: self.X_[i] for i in range(n_samples)} cm_sc = ListedColormap(['#AAAAAA', '#FF0000', '#0000FF']) if title is not None: ax.set_title(title) ax.set_xlabel('$x_1$') ax.set_ylabel('$x_2$') ax.set_xlim(domain) ax.set_ylim(codomain) nx.draw_networkx_nodes(G, pos, ax=ax, node_size=25, node_color=self.y_, cmap=cm_sc) if predict: xx1, xx2 = np.meshgrid(x1, x2) xfull = np.c_[xx1.ravel(), xx2.ravel()] z = self.predict(xfull).reshape(100, 100) levels = np.array([-1, 0, 1]) cm_cs = plt.cm.RdYlBu if self.params['gamma_i'] != 0.0: nx.draw_networkx_edges(G, pos, ax=ax, edge_color='#AAAAAA') ax.contourf(xx1, xx2, z, levels, cmap=cm_cs, alpha=0.25) return (f, ax)
def show_graph_communities(graph, partition, color_map=None, with_labels=False): if color_map is None: color_map = generate_color_map(partition) pos = nx.spring_layout(graph,k=0.1,iterations=50) comm_nodes = utils.partition_to_comm_nodes_map(partition) for comm, nodes in comm_nodes.items(): nx.draw_networkx_nodes(graph, pos, nodelist=nodes, node_size=400, node_color=color_map.get(comm), label=comm, cmap=plt.cm.jet) if with_labels: nx.draw_networkx_labels(graph, pos, font_size=12) nx.draw_networkx_edges(graph, pos, alpha=0.3) plt.legend() plt.axis('off') plt.show()
def show_graph(graph, with_labels=False): pos = nx.spring_layout(graph) nx.draw_networkx_nodes(graph, pos, node_size=200, node_color='red') if with_labels: nx.draw_networkx_labels(graph, pos, font_size=12) nx.draw_networkx_edges(graph, pos, alpha=0.3) plt.axis('off') plt.show()
def draw_partition(graph, partition): # requires matplotlib.pyplot, uncomment above # uses community code and sample from http://perso.crans.org/aynaud/communities/ to draw matplotlib graph in shades of gray g = graph count = 0 size = float(len(set(partition.values()))) pos = nx.spring_layout(g) for com in set(partition.values()): count = count + 1 list_nodes = [nodes for nodes in partition.keys() if partition[nodes] == com] nx.draw_networkx_nodes(g, pos, list_nodes, node_size=20, node_color=str(count / size)) nx.draw_networkx_edges(g, pos, alpha=0.5) plt.show()
def ped_group(pedgraph, nlist, nscale=600, nalpha=0.95, nsize=15, ealpha=0.2, ewidth=0.3, ecolor="#000000", atName="attr1", atCol=4): ''' Receives a networkx graph and plots. - needs matplotlib package :param pedgraph: networkX graph object (pedigree) :param nscale: scale of the plot :param nalpha: node transparency :param nsize: node size :param ncolor: node color :param ealpha: edge transparency :param ewidth: edge width :param ecolor: edge color :return: ''' grpList = [line.strip() for line in open(nlist, 'r')] part = add_node_attribute("ped_testherd.in", pedgraph, atName=atName, atCol=atCol) values = [part.get(node) for node in grpList] pos = nx.spring_layout(pedgraph, scale=nscale) nx.draw_networkx_nodes(pedgraph, pos, nodelist=grpList, alpha=nalpha, node_color=values, node_size=nsize, linewidths=0.1, cmap=plt.get_cmap('Paired')) # label plot not feasable for larger networks # nx.draw_networkx_labels(pedgraph, pos) # nx.draw_networkx_edges(pedgraph, pos, alpha=ealpha, width=ewidth, edge_color=ecolor) plt.axis("off") plt.show()
def aStarSearch(G, source, dest, heuristics, pos): visited = {} for node in G.nodes(): visited[node] = False final_path = [] goal = aStarSearchUtil(G, source, visited, final_path, dest, 0) prev = -1 for var in final_path: if prev != -1: curr = var nx.draw_networkx_edges(G, pos, edgelist = [(prev,curr)], width = 2.5, alpha = 0.8, edge_color = 'black') prev = curr else: prev = var return
def prims(G, pos): V = len(G.nodes()) # V denotes the number of vertices in G dist = [] # dist[i] will hold the minimum weight edge value of node i to be included in MST parent = [None]*V # parent[i] will hold the vertex connected to i, in the MST edge mstSet = [] # mstSet[i] will hold true if vertex i is included in the MST #initially, for every node, dist[] is set to maximum value and mstSet[] is set to False for i in range(V): dist.append(sys.maxsize) mstSet.append(False) dist[0] = 0 parent[0]= -1 #starting vertex is itself the root, and hence has no parent for count in range(V-1): u = minDistance(dist, mstSet, V) #pick the minimum distance vertex from the set of vertices mstSet[u] = True #update the vertices adjacent to the picked vertex for v in range(V): if (u, v) in G.edges(): if mstSet[v] == False and G[u][v]['length'] < dist[v]: dist[v] = G[u][v]['length'] parent[v] = u for X in range(V): if parent[X] != -1: #ignore the parent of the starting node if (parent[X], X) in G.edges(): nx.draw_networkx_edges(G, pos, edgelist = [(parent[X], X)], width = 2.5, alpha = 0.6, edge_color = 'r') return #takes input from the file and creates a weighted graph
def DrawGraph(G, egocentric_network_edge_list, egocentric_network_node_list, vert): pos = nx.spring_layout(G) nx.draw(G, pos, with_labels = True, node_color = 'blue', alpha = 0.8) #with_labels=true is to show the node number in the output graph nx.draw_networkx_edges(G, pos, edgelist = egocentric_network_edge_list , width = 2.5, alpha = 0.8, edge_color = 'red') nx.draw_networkx_nodes(G,pos, nodelist = egocentric_network_node_list, node_color = 'red', alpha = 0.5) nx.draw_networkx_nodes(G,pos,nodelist=[vert],node_color='green',node_size=500,alpha=0.8) return pos
def DrawGraph(G,egocentric_network_edge_list,egocentric_network_node_list): pos = nx.spring_layout(G) nx.draw(G, pos, with_labels = True, node_color = 'blue', alpha = 0.2) #with_labels=true is to show the node number in the output graph nx.draw_networkx_edges(G, pos, edgelist = egocentric_network_edge_list , width = 2.5, alpha = 0.8, edge_color = 'blue') nx.draw_networkx_nodes(G,pos, nodelist = egocentric_network_node_list, node_color = 'blue', alpha = 0.5) return pos
def DrawGraph(G,egocentric_network_edge_list,egocentric_network_node_list, vert): pos = nx.spring_layout(G) nx.draw(G, pos, with_labels = True, node_color = 'blue', alpha = 0.8) #with_labels=true is to show the node number in the output graph nx.draw_networkx_edges(G, pos, edgelist = egocentric_network_edge_list , width = 2.5, alpha = 0.8, edge_color = 'red') nx.draw_networkx_nodes(G,pos, nodelist = egocentric_network_node_list, node_color = 'red', alpha = 0.5) nx.draw_networkx_nodes(G,pos,nodelist=[vert],node_color='green',node_size=500,alpha=0.8) return pos
def kruskals(G, pos): eLen = len(G.edges()) # eLen denotes the number of edges in G vLen = len(G.nodes()) # vLen denotes the number of vertices in G mst = [] # mst contains the MST edges mstFlag = {} # mstFlag[i] will hold true if the edge i has been processed for MST for i in [ (u, v, edata['length']) for u, v, edata in G.edges(data = True) if 'length' in edata ]: mstFlag[i] = False parent = [None] * vLen # parent[i] will hold the vertex connected to i, in the MST order = [None] * vLen # order[i] will hold the order of appearance of the node in the MST for v in range(vLen): parent[v] = v order[v] = 0 while len(mst) < vLen - 1 : curr_edge = getMin(G, mstFlag) # pick the smallest egde from the set of edges mstFlag[curr_edge] = True # update the flag for the current edge y = findRoot(parent, curr_edge[1]) x = findRoot(parent, curr_edge[0]) # adds the edge to MST, if including it doesn't form a cycle if x != y: mst.append(curr_edge) union(parent, order, x, y) # Else discard the edge # marks the MST edges with red for X in mst: if (X[0], X[1]) in G.edges(): nx.draw_networkx_edges(G, pos, edgelist = [(X[0], X[1])], width = 2.5, alpha = 0.6, edge_color = 'r') return # takes input from the file and creates a weighted graph
def draw_transmat_graph(G, edge_threshold=0, lw=1, ec='0.2', node_size=15): num_states = G.number_of_nodes() edgewidth = [ d['weight'] for (u,v,d) in G.edges(data=True)] edgewidth = np.array(edgewidth) edgewidth[edgewidth<edge_threshold] = 0 labels = {} labels[0] = '1' labels[1]= '2' labels[2]= '3' labels[num_states-1] = str(num_states) npos=circular_layout(G, scale=1, direction='CW') lpos=circular_layout(G, scale=1.15, direction='CW') nx.draw_networkx_edges(G, npos, alpha=0.8, width=edgewidth*lw, edge_color=ec) nx.draw_networkx_nodes(G, npos, node_size=node_size, node_color='k',alpha=0.8) ax = plt.gca() nx.draw_networkx_labels(G, lpos, labels, fontsize=18, ax=ax); # fontsize does not seem to work :/ ax.set_aspect('equal') return ax
def nx_plot(adj, pos, value): # input: adjacent matrix, position, value map label = np.arange(len(pos)) G=nx.from_numpy_matrix(adj) nx.draw_networkx_nodes(G, pos, node_color = value) nx.draw_networkx_labels(G, pos) nx.draw_networkx_edges(G, pos) plt.ion() plt.show()
def nx_plot(adj, pos, value): # input: adjacent matrix, position, value map label = np.arange(len(pos)) G=nx.from_numpy_matrix(adj) nx.draw_networkx_nodes(G, pos, node_color = value) nx.draw_networkx_labels(G, pos) nx.draw_networkx_edges(G, pos, width=1.0) plt.ion() plt.show()
def nx_plot(adj, pos, value): # input: adjacent matrix, position, value map label = np.arange(len(pos)) G=nx.from_numpy_matrix(adj) nodes = nx.draw_networkx_nodes(G, pos, node_color=value, node_size=200) nodes.set_edgecolor('black') nx.draw_networkx_labels(G, pos, font_size=10) nx.draw_networkx_edges(G, pos, width=1.0) plt.ion() plt.show()
def malt_demo(nx=False): """ A demonstration of the result of reading a dependency version of the first sentence of the Penn Treebank. """ dg = DependencyGraph("""Pierre NNP 2 NMOD Vinken NNP 8 SUB , , 2 P 61 CD 5 NMOD years NNS 6 AMOD old JJ 2 NMOD , , 2 P will MD 0 ROOT join VB 8 VC the DT 11 NMOD board NN 9 OBJ as IN 9 VMOD a DT 15 NMOD nonexecutive JJ 15 NMOD director NN 12 PMOD Nov. NNP 9 VMOD 29 CD 16 NMOD . . 9 VMOD """) tree = dg.tree() tree.pprint() if nx: # currently doesn't work import networkx from matplotlib import pylab g = dg.nx_graph() g.info() pos = networkx.spring_layout(g, dim=1) networkx.draw_networkx_nodes(g, pos, node_size=50) # networkx.draw_networkx_edges(g, pos, edge_color='k', width=8) networkx.draw_networkx_labels(g, pos, dg.nx_labels) pylab.xticks([]) pylab.yticks([]) pylab.savefig('tree.png') pylab.show()
def draw_text_graph(G): plt.figure(figsize=(18,12)) pos = nx.spring_layout(G, scale=18) nx.draw_networkx_nodes(G, pos, node_color="white", linewidths=0, node_size=500) nx.draw_networkx_labels(G, pos, font_size=10) nx.draw_networkx_edges(G, pos) plt.xticks([]) plt.yticks([])
def draw_street_graph(networkx_graph, node_index): """ This function draws the networkx graph and visualise it. PARAMETERS ---------- networkx_graph : networkx graph class instance The networkx graph to be visualised. node_index : list of floats The index of the nodes in the networkx graph. """ import matplotlib.pyplot as plt node_pos = {} ntcnt = 0 for np in node_index: node_pos[ntcnt] = (np[0],np[1]) ntcnt+=1 nx.draw_networkx_labels(networkx_graph,pos=node_pos) nx.draw_networkx_nodes(networkx_graph,node_pos, node_size = 10) nx.draw_networkx_edges(networkx_graph,node_pos,width=1.0,alpha=0.5) plt.show() #================================================================================================================ #NSHFAI #================================================================================================================
def _draw_graph_diffing(graph1, graph2, differences): plt.subplot(121) pos = nx.pygraphviz_layout(graph1, prog='dot') nx.draw_networkx_nodes(graph1, pos, graph1.nodes(), node_color='b', node_size=200) nx.draw_networkx_nodes(graph1, pos, differences[0], node_color='r', node_size=600) nx.draw_networkx_nodes(graph1, pos, differences[2], node_color='y', node_size=600) nx.draw_networkx_edges(graph1, pos, graph1.edges()) nx.draw_networkx_labels(graph1, pos, font_size=8) plt.title('Graph 1') plt.axis('off') plt.subplot(122) pos = nx.pygraphviz_layout(graph2, prog='dot') nx.draw_networkx_nodes(graph2, pos, graph2.nodes(), node_color='b', node_size=200) nx.draw_networkx_nodes(graph2, pos, differences[1], node_color='r', node_size=600) nx.draw_networkx_nodes(graph2, pos, differences[3], node_color='g', node_size=600) nx.draw_networkx_edges(graph2, pos, graph2.edges()) nx.draw_networkx_labels(graph2, pos, font_size=8) plt.title('Graph 2') plt.axis('off') lr = plt.Circle((0, 0), 5, fc='r') lb = plt.Circle((0, 0), 5, fc='b') lg = plt.Circle((0, 0), 5, fc='g') ly = plt.Circle((0, 0), 5, fc='y') plt.legend([lb, lr, lg, ly], ['No changed', 'Changed', 'Added', 'Removed'], loc=4) # plt.savefig(graph1.name + '-' + graph2.name + '.png') plt.show()
def Drawcomgraph(G): #Draw the graph pos=nx.spring_layout(G) nx.draw_networkx_edges(G, pos, alpha=0.4) nx.draw_networkx_labels(G, pos, font_size=10, font_family='sans-serif') #colorList = ['SeaGreen','yellow','brown','pink','purple','blue','green','Salmon','red','c','magenta','orange','white','black','y','skyblue','GreenYellow','cyan']#,'aqua' nx.draw_networkx_nodes(G, pos, nodelist=G.nodes(), node_size=150) plt.title("Network Community Analysis") plt.show()
def plot_and_save_net(self, picpath='../output/net.png'): net = nx.DiGraph() edge_label = dict() for edge in self.edges: net.add_edge(edge[0], edge[1], weight=1) edge_label[(edge[0], edge[1])] = edge[3] if len(edge_label) > 8: break # edge_label.update({(edge[0], edge[1]) : edge[2]}) pos = nx.spring_layout(net, k=20) # positions for all nodes # nodes nx.draw_networkx_nodes(net, pos, node_size=6000, node_color="green") # edges nx.draw_networkx_edges(net, pos, width=1.5, alpha=0.5, arrows=True, edge_color='black') # labels nx.draw_networkx_labels(net, pos, font_size=20) nx.draw_networkx_edge_labels(net, pos, edge_labels=edge_label, label_pos=0.5, font_family='sans-serif') plt.axis('off') plt.savefig(picpath) # save as png plt.show() # display
def draw_relation_graph(G,node_ip,node_main,node_cname): from networkx.drawing.nx_pydot import graphviz_layout node_size =100 pos = graphviz_layout(G, prog="fdp") # neato fdp nx.draw_networkx_nodes(G, pos=pos, node_size=node_size, nodelist=node_ip, node_color='red', label="IP") nx.draw_networkx_nodes(G, pos=pos, node_size=node_size, nodelist=node_cname, node_color='green', label="CNAME") nx.draw_networkx_nodes(G, pos=pos, node_size=160, nodelist=node_main, node_color='blue', label="Main") nx.draw_networkx_edges(G, pos=pos) # nx.draw_networkx_labels(G, pos, font_size=10) # show the node labe plt.legend(loc='lower center', ncol=3, shadow=True, numpoints=1) plt.axis('off') plt.savefig('./graph/dd_type.png', dpi=75) plt.show()
def create_web_network_graph(self): ''' Functions that creates a NetworkX network visualization from the explored pages For documentation about NetworkX, check : https://networkx.github.io/''' #Create a directed graph web_graph=nx.DiGraph() # Add our start nodes first to the graph, as the center. web_graph.add_nodes_from(self.to_visit_urls[0]) #Now we explore our results to add the relevant websites to the graph for base_url in os.listdir(self.main_directory+"web_content/"): if self.is_danish_company(base_url): #Only Danish companies are added : web_graph.add_node(base_url) #Explore again to fill up all the edges (connections/links) between websites for base_url in os.listdir(self.main_directory+"web_content/"): if self.is_danish_company(base_url): # Same as before only Danish companies #Load up the links from this Danish company to other websites filename = self.main_directory+"web_content/"+base_url+"/external_urls_"+str(self.redirect_count)+"_redirect.p" external_base_urls=pickle.load(open(filename, "rb" )) #Now we also filter the list of external links for external_link in external_base_urls: if web_graph.has_node(external_link) : # The link is also in the graph, so the connection is added web_graph.add_edge(base_url,external_link) #Finally draw the network #plt.figure(figsize=(120, 90)) plt.figure(figsize=(40, 40)) pos = nx.random_layout(web_graph) nx.draw_networkx_nodes(web_graph,pos,node_size=2500) nx.draw_networkx_nodes(web_graph,nodelist=self.to_visit_urls[0],pos=pos,node_size=3000,node_color='b') #nx.draw_networkx_labels(web_graph,pos,fontsize=12) nx.draw_networkx_edges(web_graph,pos,alpha=0.5) plt.savefig(self.main_directory+"DTU network.png",dpi=40) plt.show()
def save_topology(adj, sample_folder, dataset_name, graph_name): graph = nx.Graph() path = sample_folder+'/'+dataset_name if not os.path.isdir(path): os.makedirs(path) # 1. transfer adj to nx # adj_list = list(np.squeeze(adj[0,:,:,:])) adj_list = list(adj) for src in range(len(adj_list)): graph.add_node(src) for dst in range(len(adj_list[src])): if adj_list[src][dst] >= 0.2: # ?? sample ?? ?? [0,1]??? graph.add_edge(src,dst) # 2. read position pos_file = glob.glob(path+'/*.pos') if pos_file == []: pos = nx.spring_layout(graph) pickle.dump(pos, open(path+'/graph.pos','wb')) else: pos = pickle.load(open(pos_file[0],'rb')) # 3. draw graph nx.draw_networkx_nodes(graph, pos, node_size=300, node_color='b', alpha=0.8) nx.draw_networkx_edges(graph, pos, width=1.5, alpha=0.8) nx.draw_networkx_labels(graph, pos, font_color='w') plt.savefig(path+'/'+graph_name+'.png') plt.savefig(path+'/'+graph_name+'.pdf') # plt.show() # 4. store graph pickle.dump(graph, open(path+'/'+graph_name+'.graph','wb'))
def save_topology(adj, path, graph_name, link_possibility): graph = nx.Graph() if not os.path.isdir(path): os.makedirs(path) # 1. transfer adj to nx adj_list = list(np.squeeze(adj[0,:,:,:])) for src in range(len(adj_list)): graph.add_node(src) for dst in range(len(adj_list[src])): if adj_list[src][dst] >= link_possibility: # ?? sample ?? ?? [0,1]??? graph.add_edge(src,dst) # 2. read position pos_file = glob.glob(path+'*.pos') if pos_file == []: node_size = len(graph.nodes()) tmp_graph = nx.barabasi_albert_graph(node_size,2) pos = nx.spring_layout(tmp_graph) pickle.dump(pos, open(path+'graph.pos','wb')) else: pos = pickle.load(open(pos_file[0],'rb')) # 3. draw graph nx.draw_networkx_nodes(graph, pos, node_size=300, node_color='b', alpha=0.8) nx.draw_networkx_edges(graph, pos, width=1.5, alpha=0.8) nx.draw_networkx_labels(graph, pos, font_color='w') plt.savefig(path+'/'+graph_name+'.png') plt.savefig(path+'/'+graph_name+'.pdf') # plt.show() plt.clf() # 4. store graph pickle.dump(graph, open(path+graph_name+'.graph','wb')) # ------------------------------ # show_all_variables() # @purpose: ??TF?????? # ------------------------------
def drawRoads(G, edgeColorAttribute='level', edgeColorScale=9, nodeColorAttribute=None, nodeColorScale=None): pos={} for n in G.nodes(): pos[n] = (G.node[n]['longitude'],G.node[n]['latitude']) # nodes nx.draw_networkx_nodes(G,pos,node_size=0,alpha=0.5,color=colors[-1]) # edges for i in range(0,9): selectedEdges = [(u,v) for (u,v,d) in G.edges(data=True) if d['level'] == i] selectedColors = [colors[i] for e in selectedEdges] nx.draw_networkx_edges(G,pos,edgelist=selectedEdges,width=edgeWidth[i],edge_color=selectedColors, alpha=0.5)
def draw_tier3(): print 'loading tier3 data' loadEntity(tier3filename) print 'entity size: ', len(entityList) G = nx.Graph() for entity in entityList: name = entity.name[0].decode('utf8') print name G.add_node(name) for parent in entity.parent: pr = parent.decode('utf8') G.add_node(pr) G.add_edge(pr, name) for child in entity.child: cn = child.decode('utf8') G.add_node(cn) G.add_edge(name, cn) pos=nx.spring_layout(G) # positions for all nodes nx.draw_networkx_edges(G,pos,width=1.0,alpha=0.5) # labels nx.draw_networkx_labels(G,pos,font_size=10,font_family='sans-serif') plt.axis('off') plt.show()
def plot_module_dependency_graph(graph): """ Plot a graph of specified yang modules. this function is used to plot both the full dependency graph of all yang modules in the DB, or a subgraph of dependencies for a specified module :param graph: Graph to be plotted :return: None """ # fixed_pos = { 'ietf-interfaces':(0.01,0.01) } # fixed_nodes = fixed_pos.keys() # pos = nx.spring_layout(graph, iterations=200, # pos=fixed_pos, fixed=fixed_nodes) #pos = nx.circular_layout(graph) pos = nx.spring_layout(graph, iterations=2000) # Draw RFC nodes (yang modules) in red nx.draw_networkx_nodes(graph, pos=pos, nodelist=prune_graph_nodes(graph, RFC_TAG), node_size=200, node_shape='s', node_color='red', alpha=0.5, linewidths=0.5) # Draw draft nodes (yang modules) in green nx.draw_networkx_nodes(graph, pos=pos, nodelist=prune_graph_nodes(graph, DRAFT_TAG), node_size=200, node_shape='o', node_color='green', alpha=0.5, linewidths=0.5) # Draw unknown nodes (yang modules) in orange nx.draw_networkx_nodes(graph, pos=pos, nodelist=prune_graph_nodes(graph, UNKNOWN_TAG), node_size=200, node_shape='^', node_color='orange', alpha=1.0, linewidths=0.5) # Draw edges in light gray (fairly transparent) nx.draw_networkx_edges(graph, pos=pos, alpha=0.25, linewidths=0.1, arrows=False) # Draw labels on nodes (modules) nx.draw_networkx_labels(graph, pos=pos, font_size=10, font_weight='bold', alpha=1.0)
