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_nodes()。
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 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 plot_embedding2D(node_pos, node_colors=None, di_graph=None): node_num, embedding_dimension = node_pos.shape if(embedding_dimension > 2): print("Embedding dimension greater than 2, use tSNE to reduce it to 2") model = TSNE(n_components=2) node_pos = model.fit_transform(node_pos) if di_graph is None: # plot using plt scatter plt.scatter(node_pos[:, 0], node_pos[:, 1], c=node_colors) else: # plot using networkx with edge structure pos = {} for i in range(node_num): pos[i] = node_pos[i, :] if node_colors: nx.draw_networkx_nodes(di_graph, pos, node_color=node_colors, width=0.1, node_size=100, arrows=False, alpha=0.8, font_size=5) else: nx.draw_networkx(di_graph, pos, node_color=node_colors, width=0.1, node_size=300, arrows=False, alpha=0.8, font_size=12)
def drawNodeGraph(g): 'Draws a graph.' # nx.draw_networkx_nodes(g,g,nodelist=g.keys(), # node_size=80, # node_color=(list(g.values())), # cmap=plt.cm.Reds_r) # plt.xlim(-0.05,1.05) # plt.ylim(-0.05,1.05) # plt.axis('off') # plt.savefig('random_geometric_graph.png') # plt.show()
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 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 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 visualise(self, graph, pos): import pylab nx.draw_networkx_nodes(graph, pos) nx.draw(graph, pos, with_labels=True, node_size=2000, node_color='w') pylab.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=0.5,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 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)
def get_fig(self, genes, e_color): fixed_pair = [(self.fixed_path[i], self.fixed_path[i+1]) for i in range(len(self.fixed_path) - 1)] for gene in genes: gene_pair = [(gene[i], gene[i+1]) for i in range(len(gene) - 1)] for layer_num, (pair, fixed) in enumerate(zip(gene_pair, fixed_pair)): for first_num in pair[0]: for second_num in pair[1]: first_node = self.node_ids[(layer_num, first_num)] second_node = self.node_ids[(layer_num + 1, second_num)] if self.graph.has_edge(first_node, second_node): self.node_upsize(first_node) self.node_upsize(second_node) weight = self.graph.get_edge_data(first_node, second_node)['weight'] weight += self.edge_weight_add self.graph.add_edge(first_node, second_node, color = e_color, weight = weight) else: self.graph.add_edge(first_node, second_node, color = e_color, weight = self.init_edge_weight) for fixed in fixed_pair: for f_1 in fixed[0]: for f_2 in fixed[1]: if (not f_1 == None) and (not f_2 == None): self.graph.add_edge(f_1, f_2, color = self.fixed_color, weight = self.fixed_weight) nodes = self.graph.nodes(data = True) node_color = 'g' node_size = [node[1]['size'] for node in nodes] node_shape = 's' edges = self.graph.edges() edge_color = [self.graph[u][v]['color'] for u,v in edges] weights = [self.graph[u][v]['weight'] for u,v in edges] nx.draw_networkx_nodes(self.graph, nodes = nodes, pos=nx.get_node_attributes(self.graph,'Position'), node_color = node_color, node_size = node_size, node_shape = node_shape) nx.draw_networkx_edges(self.graph, edges = edges, pos=nx.get_node_attributes(self.graph,'Position'), edge_color = edge_color, width = weights)
def draw_glowing_nodes(self, size): for i in range(self.glow_iter): glowsize = [ x + (6.0 * x * (i + 1) / self.glow_iter) for x in size] nx.draw_networkx_nodes(self.g, self.pos, alpha=0.025, cmap='gist_rainbow', node_color=self.colors, node_size=glowsize, linewidths=0) nx.draw_networkx_nodes(self.g, self.pos, alpha=1.0, cmap='gist_rainbow', node_color=self.colors, node_size=size, linewidths=0) return
def _draw_nodes(self, nodelist, **kwargs): node_color = kwargs.get('node_color', 'r') if isinstance(node_color, dict): node_color = [node_color[n] for n in nodelist] kwargs['node_color'] = node_color labels = kwargs.get('labels', {k: k for k in nodelist}) if labels is not False: self._draw_node_labels(labels) return nx.draw_networkx_nodes(self._G, self._pos, nodelist=nodelist, **kwargs)
