Networkx绘图

#NetworkX提供了基本的绘图功能,但是更强调的是对图的分析,而不是图的可视化。在以后的功能中,

#图的可视化可能被移除,或只提供一个附加的包

#著名的绘图工具是:Cytoscape, Gephi, Graphviz and, for LaTeXtypesetting, PGF/TikZ.

#使用这些工具,需要将Networx的图转换成这些工具能够读的格式,列如:

#Cytoscape能够读GraphML格式的图,所以用networkx.writegraphml(G)可能是一个恰当的选择

Matplotlib

用Matplotlib绘图

matplotlib

http://matplotlib.org/

pygraphviz

http://pygraphviz.github.io/

In [6]:

# draw(G[, pos, ax, hold])  Draw the graph G with Matplotlib.

# draw_networkx(G[, pos, arrows, with_labels])  Draw the graph G using Matplotlib.

# draw_networkx_nodes(G, pos[, nodelist, ...])  Draw the nodes of the graph G.

# draw_networkx_edges(G, pos[, edgelist, ...])  Draw the edges of the graph G.

# draw_networkx_labels(G, pos[, labels, ...])   Draw node labels on the graph G.

# draw_networkx_edge_labels(G, pos[, ...])  Draw edge labels.

# draw_circular(G, **kwargs)    Draw the graph G with a circular layout.

# draw_random(G, **kwargs)  Draw the graph G with a random layout.

# draw_spectral(G, **kwargs)    Draw the graph G with a spectral layout.

# draw_spring(G, **kwargs)  Draw the graph G with a spring layout.

# draw_shell(G, **kwargs)   Draw networkx graph with shell layout.

# draw_graphviz(G[, prog])  Draw networkx graph with graphviz layout.

In [1]:

importnetworkx as nx

importmatplotlib.pyplot as plt

In [4]:

G = nx.complete_graph(5)

draw(G)

In [15]:

nx.draw_spectral(G)

plt.show()

In [39]:

G.nodes()

Out[39]:

[0, 1, 2,3, 4]

nx.draw_networkx

In [6]:

 

pos=nx.spring_layout(G)

nx.draw_networkx(G, pos, arrows=True, with_labels=True,nodelist=G.nodes(),\    #基本参数

               node_color=[1,2,3,4,5],node_shape="*",node_size=350,alpha=0.5,\ #结点参数,alpha是透明度

               width=2,edge_color=[1,2,3,4,5,6,7,8,9,0],style='dashed',\   #边参数(solid|dashed|dotted,dashdot)

               labels={0:'A',1:'B',2:'C',3:'D',4:'E'},font_size=20,font_weight='bold',\

                font_family="Times NewRoman",\

               label=['MyFun']

                )

plt.show()

. . .

In [7]:

pos=nx.spring_layout(G)

nx.draw_networkx(G, pos, arrows=True, with_labels=True,nodelist=G.nodes(),\

               node_color=[1,2,3,4,5],node_shape="*",node_size=350,alpha=0.5,\

               width=2,edge_color=[1,2,3,4,5,6,7,8,9,0],style='dashed',\

               labels={0:'A',1:'B',2:'C',3:'D',4:'E'},font_size=20,font_weight='bold',\

                font_family="Times NewRoman",\

               label=['MyFun']

                )

plt.show()

In [13]:

G = nx.complete_graph(5)

pos = nx.spring_layout(G)   #注意不能在下面函数中调用nx.spring_layout,而应该先定义一个pos,大家共用

nodes=nx.draw_networkx_nodes(G,pos,node_color=[1,2,3,4,5],label='AAAA')

edges=nx.draw_networkx_edges(G,pos)

edges=nx.draw_networkx_labels(G,pos)

#labels=nx.draw_networkx_labels(G,pos=nx.spring_layout(G))

plt.show()

draw_networkx_edge_labels

In [9]:

 

# G.add_edge('10','9',weight=1, label='I')

# edge_labels =dict([((u, v), d['label'])

#                   for u, v, d in G.edges(data=True)])

In [62]:

G.edges(data=True)

Out[62]:

[(0, 1,{}),
 (0, 2, {}),
 (0, 3, {}),
 (0, 4, {}),
 (1, 2, {}),
 (1, 3, {}),
 (1, 4, {}),
 (2, 3, {}),
 (2, 4, {}),
 (3, 4, {})]

In [11]:

 

# draw_networkx_edge_labels(G, pos, edge_labels=None,label_pos=0.5, font_size=10, font_color='k', \

#                          font_family='sans-serif', font_weight='normal', \

#                           alpha=1.0,bbox=None, ax=None, rotate=True, **kwds)

# n=nx.draw(G,pos)

#e=nx.draw_networkx_edge_labels(G,pos,edges_labes={G.edges()[0]:"0-1"})

# plt.show()

Graphviz AGraph (dot)

In [7]:

# Graphviz AGraph

# Interface to pygraphviz AGraph class.

AGraph的格式转换与读写

In [3]:

A=nx.nx_agraph.to_agraph(G)

H=nx.nx_agraph.from_agraph(A)    #注意  此处需要安装pygraphviz包

In [52]:

# from_agraph(A[, create_using])    Return a NetworkX Graph or DiGraph from aPyGraphviz graph.

# to_agraph(N) Return a pygraphviz graph from a NetworkX graph N.

# write_dot(G, path)    Write NetworkX graph G to Graphviz dotformat on path.

# read_dot(path)   Return a NetworkX graph from a dot file on path.

# graphviz_layout(G[, prog, root, args])    Create node positions for G using Graphviz.

# pygraphviz_layout(G[, prog, root, args])  Create node positions for G using Graphviz.

Graph Layout

In [53]:

# circular_layout(G[, dim, scale, center])  Position nodes on a circle.

# fruchterman_reingold_layout(G[, dim, k, ...])Position nodes using Fruchterman-Reingold force-directed algorithm.

# random_layout(G[, dim, scale, center])    Position nodes uniformly at random.

# shell_layout(G[, nlist, dim, scale, center])  Position nodes in concentric circles.

# spring_layout(G[, dim, k, pos, fixed, ...])   Position nodes using Fruchterman-Reingoldforce-directed algorithm.

# spectral_layout(G[, dim, weight, scale,center])  Position nodes using theeigenvectors of the graph Laplacian.

In [ ]:

 

# spring_layout(G, dim=2, k=None, pos=None,fixed=None, iterations=50, weight='weight', scale=1.0, center=None)

# Position nodes using Fruchterman-Reingoldforce-directed algorithm.

# Parameters:  

# G (NetworkX graph) –

# dim (int) – Dimension of layout

# k (float (default=None)) – Optimal distance betweennodes. If None the distance is set to 1/sqrt(n) where n is the number of nodes.Increase this value to move nodes farther apart.

# pos (dict or None optional (default=None)) –Initial positions for nodes as a dictionary with node as keys and values as alist or tuple. If None, then use random initial positions.

# fixed (list or None optional (default=None)) –Nodes to keep fixed at initial position. If any nodes are fixed, the scale andcenter features are not used.

# iterations (int optional (default=50)) – Number ofiterations of spring-force relaxation

# weight (string or None optional (default='weight'))– The edge attribute that holds the numerical value used for the effectivespring constant. If None, edge weights are 1.

# scale (float (default=1.0)) – Scale factor forpositions. The nodes are positioned in a box of size scale in each dim centeredat center.

# center (array-like (default scale/2 in each dim)) –Coordinate around which to center the layout.

# Returns: 

# A dictionary of positions keyed by node

# Return type: 

# dict


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