如何使用更新函數爲Matplotlib 2.0.0中的NetworkX圖形制作動畫？

Question

我想弄清楚如何使用matplotlib 2.0和animation模塊在裏面動畫我的networkx圖形。我看到了Using NetworkX with matplotlib.ArtistAnimation和Animate graph diffusion with NetworkX，但我無法弄清楚這些更新函數如何使用僞代碼。

我試圖通過一系列字母（節點），然後繪製從原點"O"到當前步驟的路徑。看看下面的情節會更有意義。我不想讓他們在Python 3.6之外製作.mp4。我認爲這將是一個很好的資源，讓人們瞭解這些更新功能如何操作以及如何將它們應用於可視化網絡。

如何使用update function來爲我的networkx圖表製作動畫？

顯然動畫不會在matplotlib figure與不同的ax對象，但這只是爲了說明如何佈置框架。

import networkx as nx 
import numpy as np 
import matplotlib.pyplot as plt 
import seaborn as sns 
import matplotlib.animation as animation 


def update_func(num, data, line): 
    # https://matplotlib.org/2.0.0/examples/animation/basic_example.html 
    line.set_data(data[..., :num]) 
    return line 

# Create Graph 
np.random.seed(2) 
G = nx.cubical_graph() 
G = nx.relabel_nodes(G, {0:"O", 1:"X", 2:"XZ", 3:"Z", 4:"Y", 5:"YZ", 6: "XYZ", 7:"XY"}) 
pos = nx.spring_layout(G) 

# Sequence of letters 
sequence_of_letters = "".join(['X', 'Y', 'Z', 'Y', 'Y', 'Z']) #np.random.RandomState(0).choice(list("XYZ"), size=6, replace=True) 
idx_colors = sns.cubehelix_palette(5, start=.5, rot=-.75)[::-1] 
idx_weights = [3,2,1] 

# General graph structure 
with plt.style.context("seaborn-white"): 
    fig, ax = plt.subplots() 
    nx.draw(G, pos=pos, with_labels=True, ax=ax) 
    print(ax.get_xlim(), ax.get_ylim()) 
# (-0.10500000000000001, 1.105) (-0.088398066788676247, 0.93028441715702148) 

# Build plot 
with plt.style.context("seaborn-white"): 
    fig, axes = plt.subplots(ncols=3, nrows=2, figsize=(10,5)) 
    for i in range(0, len(sequence_of_letters), 3): 
     triad = sequence_of_letters[i:i+3] 
     for j in range(1,4): 
      # Axes index for rows and cols 
      idx = i + j - 1 
      row_idx, col_idx = {True: (0,idx), False: (1,idx - 3)}[idx < 3] 
      ax = axes[row_idx][col_idx] 

      # Path in Graph 
      path = ["O"] + ["".join(sorted(set(triad[:k + 1]))) for k in range(j)] 

      # Background nodes 
      nx.draw_networkx_edges(G, pos=pos, ax=ax, edge_color="gray") 
      null_nodes = nx.draw_networkx_nodes(G, pos=pos, nodelist=set(G.nodes()) - set(path), node_color="white", ax=ax) 
      null_nodes.set_edgecolor("black") 

      # Query nodes 
      query_nodes = nx.draw_networkx_nodes(G, pos=pos, nodelist=path, node_color=idx_colors[:len(path)], ax=ax) 
      query_nodes.set_edgecolor("white") 
      nx.draw_networkx_labels(G, pos=pos, labels=dict(zip(path,path)), font_color="white", ax=ax) 
      edgelist = [path[k:k+2] for k in range(len(path) - 1)] 
      nx.draw_networkx_edges(G, pos=pos, edgelist=edgelist, width=idx_weights[:len(path)], ax=ax) 

      # Scale plot ax 
      ax.set_title("Frame %d: "%(idx+1) + " - ".join(path), fontweight="bold") 
      ax.set_xlim((-0.10500000000000001, 1.105)) 
      ax.set_ylim((-0.088398066788676247, 0.93028441715702148)) 
      ax.set_xticks([]) 
      ax.set_yticks([]) 

Answer 1

的答案既鏈接的問題提供瞭如何一個動畫圖形networkx很好的例子。它們比這個問題中允許的示例代碼的答案要複雜得多。

因此，我在此着重討論如何使用更新函數爲問題中的networkx圖創建動畫。

解決方法是將兩個for-loops中的所有內容放入一個函數中，該函數至少需要一個索引作爲參數。然後可以使用該索引來生成圖像。

import networkx as nx 
import numpy as np 
import matplotlib.pyplot as plt 
import seaborn.apionly as sns 
import matplotlib.animation 

# Create Graph 
np.random.seed(2) 
G = nx.cubical_graph() 
G = nx.relabel_nodes(G, {0:"O", 1:"X", 2:"XZ", 3:"Z", 4:"Y", 5:"YZ", 6: "XYZ", 7:"XY"}) 
pos = nx.spring_layout(G) 

# Sequence of letters 
sequence_of_letters = "".join(['X', 'Y', 'Z', 'Y', 'Y', 'Z']) 
idx_colors = sns.cubehelix_palette(5, start=.5, rot=-.75)[::-1] 
idx_weights = [3,2,1] 

# Build plot 
fig, ax = plt.subplots(figsize=(6,4)) 


def update(num): 
    ax.clear() 
    i = num // 3 
    j = num % 3 + 1 
    triad = sequence_of_letters[i:i+3] 
    path = ["O"] + ["".join(sorted(set(triad[:k + 1]))) for k in range(j)] 

    # Background nodes 
    nx.draw_networkx_edges(G, pos=pos, ax=ax, edge_color="gray") 
    null_nodes = nx.draw_networkx_nodes(G, pos=pos, nodelist=set(G.nodes()) - set(path), node_color="white", ax=ax) 
    null_nodes.set_edgecolor("black") 

    # Query nodes 
    query_nodes = nx.draw_networkx_nodes(G, pos=pos, nodelist=path, node_color=idx_colors[:len(path)], ax=ax) 
    query_nodes.set_edgecolor("white") 
    nx.draw_networkx_labels(G, pos=pos, labels=dict(zip(path,path)), font_color="white", ax=ax) 
    edgelist = [path[k:k+2] for k in range(len(path) - 1)] 
    nx.draw_networkx_edges(G, pos=pos, edgelist=edgelist, width=idx_weights[:len(path)], ax=ax) 

    # Scale plot ax 
    ax.set_title("Frame %d: "%(num+1) + " - ".join(path), fontweight="bold") 
    ax.set_xticks([]) 
    ax.set_yticks([]) 


ani = matplotlib.animation.FuncAnimation(fig, update, frames=6, interval=1000, repeat=True) 
plt.show()