這篇文章實現的演算法來源於PNAS雜誌：

程式碼參考：

# coding: utf-8
import networkx as nx
import matplotlib.pyplot as plt
from itertools import combinations

def visibility_graph(series):
    visibility_graph_edges=[]
    # convert list of magnitudes into list of tuples that hold the index
    tseries = []
    n = 1
    for magnitude in series:
        tseries.append( (n, magnitude ) )
        n += 1

    for a,b in combinations(tseries, 2):
        # two points, maybe connect
        (ta, ya) = a  #實現（1,2)（1,3）(1,4)(1,5)--(2,3)(2,4)(2,5)--(3,4)(3,5)--(4,5)任意兩個邊相互比較
        (tb, yb) = b
        connect = True
        medium=tseries[ta:tb-1]#此處需要多留意，ta是1到k，而tseris是從0下標開始  所以此處不能不是[ta+1:tb]

        for tc,yc in medium:
            #print yc,(yb + (ya - yb) * ( float(tb - tc) / (tb - ta) ))#一定要float(tb-tc)/(tb-ta)因為計算機裡1/2為0,1.0/2才為0.5
            if yc > yb + (ya - yb) * ( float(tb - tc) / (tb - ta) ):
                    connect =False
        if connect:
            visibility_graph_edges.append((ta,tb))
    return visibility_graph_edges

def DrawVisibilityGraph(graphEdges):

    G=nx.Graph()  #G=nx.Graph()無向圖    G=nx.DiGraph()有向圖
    #add the edge to graph G
    for i in graphEdges:
        G.add_edge(i[0],i[1])

    pos=nx.spring_layout(G)
    nx.draw_networkx_nodes(G,pos,node_size=700)
    nx.draw_networkx_edges(G,pos,width=5)
    nx.draw_networkx_labels(G,pos,font_size=20,font_family='sans-serif')
    #plt.savefig("test.png")   #save the picture
    plt.show()

if __name__=="__main__":
   #time series
   series = [0.87,0.49,0.36,0.83,0.87]
   #get the graph edges
   graphEdges=visibility_graph(series)
   #draw the visibility graph
   DrawVisibilityGraph(graphEdges)

 

繪圖參考連結：

http://networkx.github.io/documentation/latest/examples/drawing/weighted_graph.html