In [1]:

    

%matplotlib inline

from __future__ import division
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
P = np.array([[0.2, 0.5, 0.2],
             [0, 0.5, 0.5],
             [0, 0, 1]])
G = nx.MultiDiGraph()
labels={}
edge_labels={}
states = ['good', 'bad', 'mediocre']
for i, origin_state in enumerate(states):
    for j, destination_state in enumerate(states):
        rate = P[i][j]
        if rate > 0:
            G.add_edge(origin_state,
                       destination_state,
                       
                       label="{:.02f}".format(rate))
            edge_labels[(origin_state, destination_state)] = label="{:.02f}".format(rate)


    

In [5]:

    

plt.figure(figsize=(14,7))
node_size = 600
#pos = nx.random_layout(G)
pos = {'bad':[0,0], 'mediocre': [0,2], 'good': [1,1]}
nx.draw_networkx_edges(G,pos, node_size=node_size)
nx.draw_networkx_labels(G, pos, font_weight=2, node_size=node_size)
nx.draw_networkx_edge_labels(G, pos, edge_labels, node_size=node_size)
plt.axis('off');


    

In [6]:

    

nx.write_dot(G,'G.dot')


    

In [7]:

    

!neato -Tps -Goverlap=scale G.dot -o G.ps; convert G.ps G.png


    

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