Question

我想为网络图设置动画以显示算法的进度。我正在使用NetworkX进行图表创建。

从this SO answer开始，我使用来自clear_ouput的{{1}}和命令IPython.display来提出解决方案来管理动画的速度。这适用于具有少量节点的小图，但是当我在10x10网格上实现时，动画非常慢并且减少plt.pause()中的参数似乎对动画速度没有任何影响。这是一个实现Dijkstra算法的MME，我在算法的每次迭代中更新节点的颜色：

plt.pause()

理想情况下，我想在不超过5秒的时间内显示整个动画，而目前需要几分钟才能完成算法，这表明import math import queue import random import networkx as nx import matplotlib.pyplot as plt from IPython.display import clear_output %matplotlib inline # plotting function def get_fig(G,current,pred): nColorList = [] for i in G.nodes(): if i == current: nColorList.append('red') elif i==pred: nColorList.append('white') elif i==N: nColorList.append('grey') elif node_visited[i]==1:nColorList.append('dodgerblue') else: nColorList.append('powderblue') plt.figure(figsize=(10,10)) nx.draw_networkx(G,pos,node_color=nColorList,width=2,node_size=400,font_size=10) plt.axis('off') plt.show() # graph creation G=nx.DiGraph() pos={} cost={} for i in range(100): x= i % 10 y= math.floor(i/10) pos[i]=(x,y) if i % 10 != 9 and i+1 < 100: cost[(i,i+1)] = random.randint(0,9) cost[(i+1,i)] = random.randint(0,9) if i+10 < 100: cost[(i,i+10)] = random.randint(0,9) cost[(i+10,i)] = random.randint(0,9) G.add_edges_from(cost) # algorithm initialization lab={} path={} node_visited={} N = random.randint(0,99) SE = queue.PriorityQueue() SE.put((0,N)) for i in G.nodes(): if i == N: lab[i] = 0 else: lab[i] = 9999 path[i] = None node_visited[i] = 0 # algorithm main loop while not SE.empty(): (l,j) = SE.get() if node_visited[j]==1: continue node_visited[j] = 1 for i in G.predecessors(j): insert_in_SE = 0 if lab[i] > cost[(i,j)] + lab[j]: lab[i] = cost[(i,j)] + lab[j] path[i] = j SE.put((lab[i],i)) clear_output(wait=True) get_fig(G,j,i) plt.pause(0.0001) print('end')无法按预期工作。

在阅读了关于图形动画（post 2和post 3）的SO帖子之后，似乎可以使用matplotlib中的plt.pause(0.0001)模块解决此问题，但我无法成功在我的算法中实现答案。第2篇中的答案建议使用来自matplotlib的animation，但我正在努力使FuncAnimation方法适应我的问题，第3篇中的答案导致了一个很好的教程，提出了类似的建议。

我的问题是如何为我的问题提高动画的速度：是否可以安排update和clear_output命令以加快动画效果，或者我应该使用plt.pause() matplotlib？如果它是后者，那么我该如何定义FuncAnimation函数？

感谢您的帮助。

编辑1

update

编辑2

import math
import queue
import random
import networkx as nx
import matplotlib.pyplot as plt

# plotting function
def get_fig(G,current,pred):   
    for i in G.nodes():        
        if i==current: G.node[i]['draw'].set_color('red')            
        elif i==pred: G.node[i]['draw'].set_color('white')
        elif i==N: G.node[i]['draw'].set_color('grey')        
        elif node_visited[i]==1: G.node[i]['draw'].set_color('dodgerblue')
        else: G.node[i]['draw'].set_color('powderblue')    

# graph creation
G=nx.DiGraph()
pos={}
cost={}
for i in range(100):
    x= i % 10
    y= math.floor(i/10)
    pos[i]=(x,y)    
    if i % 10 != 9 and i+1 < 100: 
        cost[(i,i+1)] = random.randint(0,9)
        cost[(i+1,i)] = random.randint(0,9)
    if i+10 < 100: 
        cost[(i,i+10)] = random.randint(0,9)
        cost[(i+10,i)] = random.randint(0,9)
G.add_edges_from(cost)

# algorithm initialization
plt.figure(1, figsize=(10,10))
lab={}
path={}
node_visited={}
N = random.randint(0,99)
SE = queue.PriorityQueue()
SE.put((0,N))
for i in G.nodes():       
    if i == N: lab[i] = 0        
    else: lab[i] = 9999
    path[i] = None
    node_visited[i] = 0 
    G.node[i]['draw'] = nx.draw_networkx_nodes(G,pos,nodelist=[i],node_size=400,alpha=1,with_labels=True,node_color='powderblue')
for i,j in G.edges():
    G[i][j]['draw']=nx.draw_networkx_edges(G,pos,edgelist=[(i,j)],width=2)    

plt.ion()
plt.draw()
plt.show()

# algorithm main loop  
while not SE.empty():
    (l,j) = SE.get()    
    if node_visited[j]==1: continue
    node_visited[j] = 1
    for i in G.predecessors(j):        
        insert_in_SE = 0               
        if lab[i] > cost[(i,j)] + lab[j]:
            lab[i] = cost[(i,j)] + lab[j]
            path[i] = j
            SE.put((lab[i],i))       
        get_fig(G,j,i)        
        plt.draw()
        plt.pause(0.00001)
plt.close()

Answer 1

如果您的图表不是太大，您可以尝试以下方法来设置单个节点和边的属性。诀窍是保存绘图函数的输出，使您可以处理对象属性，如颜色，透明度和可见性。

import networkx as nx
import matplotlib.pyplot as plt

G = nx.cycle_graph(12)
pos = nx.spring_layout(G)

cf = plt.figure(1, figsize=(8,8))
ax = cf.add_axes((0,0,1,1))

for n in G:
    G.node[n]['draw'] = nx.draw_networkx_nodes(G,pos,nodelist=[n], with_labels=False,node_size=200,alpha=0.5,node_color='r')
for u,v in G.edges():
    G[u][v]['draw']=nx.draw_networkx_edges(G,pos,edgelist=[(u,v)],alpha=0.5,arrows=False,width=5)

plt.ion()
plt.draw()

sp = nx.shortest_path(G,0,6)
edges = zip(sp[:-1],sp[1:])

for u,v in edges:
    plt.pause(1)
    G.node[u]['draw'].set_color('r')
    G.node[v]['draw'].set_color('r')
    G[u][v]['draw'].set_alpha(1.0)
    G[u][v]['draw'].set_color('r')
    plt.draw()

修改

以下是使用graphviz进行布局的10x10网格示例。整个过程在我的机器上运行大约1秒钟。

import networkx as nx import matplotlib.pyplot as plt G = nx.grid_2d_graph(10,10) pos = nx.graphviz_layout(G) cf = plt.figure(1, figsize=(8,8)) ax = cf.add_axes((0,0,1,1)) for n in G: G.node[n]['draw'] = nx.draw_networkx_nodes(G,pos,nodelist=[n], with_labels=False,node_size=200,alpha=0.5,node_color='k') for u,v in G.edges(): G[u][v]['draw']=nx.draw_networkx_edges(G,pos,edgelist=[(u,v)],alpha=0.5,arrows=False,width=5) plt.ion() plt.draw() plt.show() sp = nx.shortest_path(G,(0,0),(9,9)) edges = zip(sp[:-1],sp[1:]) for u,v in edges: G.node[u]['draw'].set_color('r') G.node[v]['draw'].set_color('r') G[u][v]['draw'].set_alpha(1.0) G[u][v]['draw'].set_color('r') plt.draw()

编辑2

这是另一种更快的方法（不重绘轴或所有节点）并使用广度优先搜索算法。这个在我的机器上运行大约2秒钟。我注意到一些后端更快 - 我正在使用GTKAgg。

import networkx as nx import matplotlib.pyplot as plt def single_source_shortest_path(G,source): ax = plt.gca() canvas = ax.figure.canvas background = canvas.copy_from_bbox(ax.bbox) level=0 # the current level nextlevel={source:1} # list of nodes to check at next level paths={source:[source]} # paths dictionary (paths to key from source) G.node[source]['draw'].set_color('r') G.node[source]['draw'].set_alpha('1.0') while nextlevel: thislevel=nextlevel nextlevel={} for v in thislevel: # canvas.restore_region(background) s = G.node[v]['draw'] s.set_color('r') s.set_alpha('1.0') for w in G[v]: if w not in paths: n = G.node[w]['draw'] n.set_color('r') n.set_alpha('1.0') e = G[v][w]['draw'] e.set_alpha(1.0) e.set_color('k') ax.draw_artist(e) ax.draw_artist(n) ax.draw_artist(s) paths[w]=paths[v]+[w] nextlevel[w]=1 canvas.blit(ax.bbox) level=level+1 return paths if __name__=='__main__': G = nx.grid_2d_graph(10,10) pos = nx.graphviz_layout(G) cf = plt.figure(1, figsize=(8,8)) ax = cf.add_axes((0,0,1,1)) for n in G: G.node[n]['draw'] = nx.draw_networkx_nodes(G,pos,nodelist=[n], with_labels=False,node_size=200,alpha=0.2,node_color='k') for u,v in G.edges(): G[u][v]['draw']=nx.draw_networkx_edges(G,pos,edgelist=[(u,v)],alpha=0.5,arrows=False,width=5) plt.ion() plt.show() path = single_source_shortest_path(G,source=(0,0))

动画网络图以显示算法的进度

1 个答案: