def nx_plot(adj, pos, value):
# input: adjacent matrix, position, value map
label = np.arange(len(pos))
G=nx.from_numpy_matrix(adj)
nx.draw_networkx_nodes(G, pos, node_color = value)
nx.draw_networkx_labels(G, pos)
nx.draw_networkx_edges(G, pos, width=1.0)
plt.ion()
plt.show()
python类draw_networkx_edges()的实例源码
def nx_plot(adj, pos, value):
# input: adjacent matrix, position, value map
label = np.arange(len(pos))
G=nx.from_numpy_matrix(adj)
nodes = nx.draw_networkx_nodes(G, pos, node_color=value, node_size=200)
nodes.set_edgecolor('black')
nx.draw_networkx_labels(G, pos, font_size=10)
nx.draw_networkx_edges(G, pos, width=1.0)
plt.ion()
plt.show()
def malt_demo(nx=False):
"""
A demonstration of the result of reading a dependency
version of the first sentence of the Penn Treebank.
"""
dg = DependencyGraph("""Pierre NNP 2 NMOD
Vinken NNP 8 SUB
, , 2 P
61 CD 5 NMOD
years NNS 6 AMOD
old JJ 2 NMOD
, , 2 P
will MD 0 ROOT
join VB 8 VC
the DT 11 NMOD
board NN 9 OBJ
as IN 9 VMOD
a DT 15 NMOD
nonexecutive JJ 15 NMOD
director NN 12 PMOD
Nov. NNP 9 VMOD
29 CD 16 NMOD
. . 9 VMOD
""")
tree = dg.tree()
tree.pprint()
if nx:
# currently doesn't work
import networkx
from matplotlib import pylab
g = dg.nx_graph()
g.info()
pos = networkx.spring_layout(g, dim=1)
networkx.draw_networkx_nodes(g, pos, node_size=50)
# networkx.draw_networkx_edges(g, pos, edge_color='k', width=8)
networkx.draw_networkx_labels(g, pos, dg.nx_labels)
pylab.xticks([])
pylab.yticks([])
pylab.savefig('tree.png')
pylab.show()
def draw_text_graph(G):
plt.figure(figsize=(18,12))
pos = nx.spring_layout(G, scale=18)
nx.draw_networkx_nodes(G, pos, node_color="white", linewidths=0, node_size=500)
nx.draw_networkx_labels(G, pos, font_size=10)
nx.draw_networkx_edges(G, pos)
plt.xticks([])
plt.yticks([])
def draw_street_graph(networkx_graph, node_index):
"""
This function draws the networkx graph and visualise it.
PARAMETERS
----------
networkx_graph : networkx graph class instance
The networkx graph to be visualised.
node_index : list of floats
The index of the nodes in the networkx graph.
"""
import matplotlib.pyplot as plt
node_pos = {}
ntcnt = 0
for np in node_index:
node_pos[ntcnt] = (np[0],np[1])
ntcnt+=1
nx.draw_networkx_labels(networkx_graph,pos=node_pos)
nx.draw_networkx_nodes(networkx_graph,node_pos, node_size = 10)
nx.draw_networkx_edges(networkx_graph,node_pos,width=1.0,alpha=0.5)
plt.show()
#================================================================================================================
#NSHFAI
#================================================================================================================
def _draw_graph_diffing(graph1, graph2, differences):
plt.subplot(121)
pos = nx.pygraphviz_layout(graph1, prog='dot')
nx.draw_networkx_nodes(graph1, pos,
graph1.nodes(), node_color='b', node_size=200)
nx.draw_networkx_nodes(graph1, pos, differences[0],
node_color='r', node_size=600)
nx.draw_networkx_nodes(graph1, pos, differences[2],
node_color='y', node_size=600)
nx.draw_networkx_edges(graph1, pos, graph1.edges())
nx.draw_networkx_labels(graph1, pos, font_size=8)
plt.title('Graph 1')
plt.axis('off')
plt.subplot(122)
pos = nx.pygraphviz_layout(graph2, prog='dot')
nx.draw_networkx_nodes(graph2, pos, graph2.nodes(), node_color='b',
node_size=200)
nx.draw_networkx_nodes(graph2, pos, differences[1], node_color='r',
node_size=600)
nx.draw_networkx_nodes(graph2, pos, differences[3], node_color='g',
node_size=600)
nx.draw_networkx_edges(graph2, pos, graph2.edges())
nx.draw_networkx_labels(graph2, pos, font_size=8)
plt.title('Graph 2')
plt.axis('off')
lr = plt.Circle((0, 0), 5, fc='r')
lb = plt.Circle((0, 0), 5, fc='b')
lg = plt.Circle((0, 0), 5, fc='g')
ly = plt.Circle((0, 0), 5, fc='y')
plt.legend([lb, lr, lg, ly], ['No changed', 'Changed', 'Added',
'Removed'], loc=4)
# plt.savefig(graph1.name + '-' + graph2.name + '.png')
plt.show()
def Drawcomgraph(G):
#Draw the graph
pos=nx.spring_layout(G)
nx.draw_networkx_edges(G, pos, alpha=0.4)
nx.draw_networkx_labels(G, pos, font_size=10, font_family='sans-serif')
#colorList = ['SeaGreen','yellow','brown','pink','purple','blue','green','Salmon','red','c','magenta','orange','white','black','y','skyblue','GreenYellow','cyan']#,'aqua'
nx.draw_networkx_nodes(G, pos, nodelist=G.nodes(), node_size=150)
plt.title("Network Community Analysis")
plt.show()
def plot_and_save_net(self, picpath='../output/net.png'):
net = nx.DiGraph()
edge_label = dict()
for edge in self.edges:
net.add_edge(edge[0], edge[1], weight=1)
edge_label[(edge[0], edge[1])] = edge[3]
if len(edge_label) > 8:
break
# edge_label.update({(edge[0], edge[1]) : edge[2]})
pos = nx.spring_layout(net, k=20) # positions for all nodes
# nodes
nx.draw_networkx_nodes(net, pos, node_size=6000, node_color="green")
# edges
nx.draw_networkx_edges(net, pos,
width=1.5, alpha=0.5, arrows=True, edge_color='black')
# labels
nx.draw_networkx_labels(net, pos, font_size=20)
nx.draw_networkx_edge_labels(net, pos, edge_labels=edge_label, label_pos=0.5, font_family='sans-serif')
plt.axis('off')
plt.savefig(picpath) # save as png
plt.show() # display
dependencygraph.py 文件源码
项目：PyDataLondon29-EmbarrassinglyParallelDAWithAWSLambda
作者: SignalMedia
项目源码
文件源码
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def malt_demo(nx=False):
"""
A demonstration of the result of reading a dependency
version of the first sentence of the Penn Treebank.
"""
dg = DependencyGraph("""Pierre NNP 2 NMOD
Vinken NNP 8 SUB
, , 2 P
61 CD 5 NMOD
years NNS 6 AMOD
old JJ 2 NMOD
, , 2 P
will MD 0 ROOT
join VB 8 VC
the DT 11 NMOD
board NN 9 OBJ
as IN 9 VMOD
a DT 15 NMOD
nonexecutive JJ 15 NMOD
director NN 12 PMOD
Nov. NNP 9 VMOD
29 CD 16 NMOD
. . 9 VMOD
""")
tree = dg.tree()
tree.pprint()
if nx:
# currently doesn't work
import networkx
from matplotlib import pylab
g = dg.nx_graph()
g.info()
pos = networkx.spring_layout(g, dim=1)
networkx.draw_networkx_nodes(g, pos, node_size=50)
# networkx.draw_networkx_edges(g, pos, edge_color='k', width=8)
networkx.draw_networkx_labels(g, pos, dg.nx_labels)
pylab.xticks([])
pylab.yticks([])
pylab.savefig('tree.png')
pylab.show()
def draw_relation_graph(G,node_ip,node_main,node_cname):
from networkx.drawing.nx_pydot import graphviz_layout
node_size =100
pos = graphviz_layout(G, prog="fdp") # neato fdp
nx.draw_networkx_nodes(G, pos=pos, node_size=node_size, nodelist=node_ip, node_color='red', label="IP")
nx.draw_networkx_nodes(G, pos=pos, node_size=node_size, nodelist=node_cname, node_color='green', label="CNAME")
nx.draw_networkx_nodes(G, pos=pos, node_size=160, nodelist=node_main, node_color='blue', label="Main")
nx.draw_networkx_edges(G, pos=pos)
# nx.draw_networkx_labels(G, pos, font_size=10) # show the node labe
plt.legend(loc='lower center', ncol=3, shadow=True, numpoints=1)
plt.axis('off')
plt.savefig('./graph/dd_type.png', dpi=75)
plt.show()
def malt_demo(nx=False):
"""
A demonstration of the result of reading a dependency
version of the first sentence of the Penn Treebank.
"""
dg = DependencyGraph("""Pierre NNP 2 NMOD
Vinken NNP 8 SUB
, , 2 P
61 CD 5 NMOD
years NNS 6 AMOD
old JJ 2 NMOD
, , 2 P
will MD 0 ROOT
join VB 8 VC
the DT 11 NMOD
board NN 9 OBJ
as IN 9 VMOD
a DT 15 NMOD
nonexecutive JJ 15 NMOD
director NN 12 PMOD
Nov. NNP 9 VMOD
29 CD 16 NMOD
. . 9 VMOD
""")
tree = dg.tree()
tree.pprint()
if nx:
# currently doesn't work
import networkx
from matplotlib import pylab
g = dg.nx_graph()
g.info()
pos = networkx.spring_layout(g, dim=1)
networkx.draw_networkx_nodes(g, pos, node_size=50)
# networkx.draw_networkx_edges(g, pos, edge_color='k', width=8)
networkx.draw_networkx_labels(g, pos, dg.nx_labels)
pylab.xticks([])
pylab.yticks([])
pylab.savefig('tree.png')
pylab.show()
def create_web_network_graph(self):
''' Functions that creates a NetworkX network visualization from the
explored pages
For documentation about NetworkX, check : https://networkx.github.io/'''
#Create a directed graph
web_graph=nx.DiGraph()
# Add our start nodes first to the graph, as the center.
web_graph.add_nodes_from(self.to_visit_urls[0])
#Now we explore our results to add the relevant websites to the graph
for base_url in os.listdir(self.main_directory+"web_content/"):
if self.is_danish_company(base_url): #Only Danish companies are added :
web_graph.add_node(base_url)
#Explore again to fill up all the edges (connections/links) between websites
for base_url in os.listdir(self.main_directory+"web_content/"):
if self.is_danish_company(base_url): # Same as before only Danish companies
#Load up the links from this Danish company to other websites
filename = self.main_directory+"web_content/"+base_url+"/external_urls_"+str(self.redirect_count)+"_redirect.p"
external_base_urls=pickle.load(open(filename, "rb" ))
#Now we also filter the list of external links
for external_link in external_base_urls:
if web_graph.has_node(external_link) : # The link is also in the graph, so the connection is added
web_graph.add_edge(base_url,external_link)
#Finally draw the network
#plt.figure(figsize=(120, 90))
plt.figure(figsize=(40, 40))
pos = nx.random_layout(web_graph)
nx.draw_networkx_nodes(web_graph,pos,node_size=2500)
nx.draw_networkx_nodes(web_graph,nodelist=self.to_visit_urls[0],pos=pos,node_size=3000,node_color='b')
#nx.draw_networkx_labels(web_graph,pos,fontsize=12)
nx.draw_networkx_edges(web_graph,pos,alpha=0.5)
plt.savefig(self.main_directory+"DTU network.png",dpi=40)
plt.show()
def malt_demo(nx=False):
"""
A demonstration of the result of reading a dependency
version of the first sentence of the Penn Treebank.
"""
dg = DependencyGraph("""Pierre NNP 2 NMOD
Vinken NNP 8 SUB
, , 2 P
61 CD 5 NMOD
years NNS 6 AMOD
old JJ 2 NMOD
, , 2 P
will MD 0 ROOT
join VB 8 VC
the DT 11 NMOD
board NN 9 OBJ
as IN 9 VMOD
a DT 15 NMOD
nonexecutive JJ 15 NMOD
director NN 12 PMOD
Nov. NNP 9 VMOD
29 CD 16 NMOD
. . 9 VMOD
""")
tree = dg.tree()
tree.pprint()
if nx:
# currently doesn't work
import networkx
from matplotlib import pylab
g = dg.nx_graph()
g.info()
pos = networkx.spring_layout(g, dim=1)
networkx.draw_networkx_nodes(g, pos, node_size=50)
# networkx.draw_networkx_edges(g, pos, edge_color='k', width=8)
networkx.draw_networkx_labels(g, pos, dg.nx_labels)
pylab.xticks([])
pylab.yticks([])
pylab.savefig('tree.png')
pylab.show()
def save_topology(adj, sample_folder, dataset_name, graph_name):
graph = nx.Graph()
path = sample_folder+'/'+dataset_name
if not os.path.isdir(path):
os.makedirs(path)
# 1. transfer adj to nx
# adj_list = list(np.squeeze(adj[0,:,:,:]))
adj_list = list(adj)
for src in range(len(adj_list)):
graph.add_node(src)
for dst in range(len(adj_list[src])):
if adj_list[src][dst] >= 0.2: # ?? sample ?? ?? [0,1]???
graph.add_edge(src,dst)
# 2. read position
pos_file = glob.glob(path+'/*.pos')
if pos_file == []:
pos = nx.spring_layout(graph)
pickle.dump(pos, open(path+'/graph.pos','wb'))
else:
pos = pickle.load(open(pos_file[0],'rb'))
# 3. draw graph
nx.draw_networkx_nodes(graph, pos, node_size=300, node_color='b', alpha=0.8)
nx.draw_networkx_edges(graph, pos, width=1.5, alpha=0.8)
nx.draw_networkx_labels(graph, pos, font_color='w')
plt.savefig(path+'/'+graph_name+'.png')
plt.savefig(path+'/'+graph_name+'.pdf')
# plt.show()
# 4. store graph
pickle.dump(graph, open(path+'/'+graph_name+'.graph','wb'))
def save_topology(adj, path, graph_name, link_possibility):
graph = nx.Graph()
if not os.path.isdir(path):
os.makedirs(path)
# 1. transfer adj to nx
adj_list = list(np.squeeze(adj[0,:,:,:]))
for src in range(len(adj_list)):
graph.add_node(src)
for dst in range(len(adj_list[src])):
if adj_list[src][dst] >= link_possibility: # ?? sample ?? ?? [0,1]???
graph.add_edge(src,dst)
# 2. read position
pos_file = glob.glob(path+'*.pos')
if pos_file == []:
node_size = len(graph.nodes())
tmp_graph = nx.barabasi_albert_graph(node_size,2)
pos = nx.spring_layout(tmp_graph)
pickle.dump(pos, open(path+'graph.pos','wb'))
else:
pos = pickle.load(open(pos_file[0],'rb'))
# 3. draw graph
nx.draw_networkx_nodes(graph, pos, node_size=300, node_color='b', alpha=0.8)
nx.draw_networkx_edges(graph, pos, width=1.5, alpha=0.8)
nx.draw_networkx_labels(graph, pos, font_color='w')
plt.savefig(path+'/'+graph_name+'.png')
plt.savefig(path+'/'+graph_name+'.pdf')
# plt.show()
plt.clf()
# 4. store graph
pickle.dump(graph, open(path+graph_name+'.graph','wb'))
# ------------------------------
# show_all_variables()
# @purpose: ??TF??????
# ------------------------------
def malt_demo(nx=False):
"""
A demonstration of the result of reading a dependency
version of the first sentence of the Penn Treebank.
"""
dg = DependencyGraph("""Pierre NNP 2 NMOD
Vinken NNP 8 SUB
, , 2 P
61 CD 5 NMOD
years NNS 6 AMOD
old JJ 2 NMOD
, , 2 P
will MD 0 ROOT
join VB 8 VC
the DT 11 NMOD
board NN 9 OBJ
as IN 9 VMOD
a DT 15 NMOD
nonexecutive JJ 15 NMOD
director NN 12 PMOD
Nov. NNP 9 VMOD
29 CD 16 NMOD
. . 9 VMOD
""")
tree = dg.tree()
tree.pprint()
if nx:
# currently doesn't work
import networkx
from matplotlib import pylab
g = dg.nx_graph()
g.info()
pos = networkx.spring_layout(g, dim=1)
networkx.draw_networkx_nodes(g, pos, node_size=50)
# networkx.draw_networkx_edges(g, pos, edge_color='k', width=8)
networkx.draw_networkx_labels(g, pos, dg.nx_labels)
pylab.xticks([])
pylab.yticks([])
pylab.savefig('tree.png')
pylab.show()
def drawRoads(G, edgeColorAttribute='level', edgeColorScale=9, nodeColorAttribute=None, nodeColorScale=None):
pos={}
for n in G.nodes():
pos[n] = (G.node[n]['longitude'],G.node[n]['latitude'])
# nodes
nx.draw_networkx_nodes(G,pos,node_size=0,alpha=0.5,color=colors[-1])
# edges
for i in range(0,9):
selectedEdges = [(u,v) for (u,v,d) in G.edges(data=True) if d['level'] == i]
selectedColors = [colors[i] for e in selectedEdges]
nx.draw_networkx_edges(G,pos,edgelist=selectedEdges,width=edgeWidth[i],edge_color=selectedColors, alpha=0.5)
def draw_tier3():
print 'loading tier3 data'
loadEntity(tier3filename)
print 'entity size: ', len(entityList)
G = nx.Graph()
for entity in entityList:
name = entity.name[0].decode('utf8')
print name
G.add_node(name)
for parent in entity.parent:
pr = parent.decode('utf8')
G.add_node(pr)
G.add_edge(pr, name)
for child in entity.child:
cn = child.decode('utf8')
G.add_node(cn)
G.add_edge(name, cn)
pos=nx.spring_layout(G) # positions for all nodes
nx.draw_networkx_edges(G,pos,width=1.0,alpha=0.5)
# labels
nx.draw_networkx_labels(G,pos,font_size=10,font_family='sans-serif')
plt.axis('off')
plt.show()
def malt_demo(nx=False):
"""
A demonstration of the result of reading a dependency
version of the first sentence of the Penn Treebank.
"""
dg = DependencyGraph("""Pierre NNP 2 NMOD
Vinken NNP 8 SUB
, , 2 P
61 CD 5 NMOD
years NNS 6 AMOD
old JJ 2 NMOD
, , 2 P
will MD 0 ROOT
join VB 8 VC
the DT 11 NMOD
board NN 9 OBJ
as IN 9 VMOD
a DT 15 NMOD
nonexecutive JJ 15 NMOD
director NN 12 PMOD
Nov. NNP 9 VMOD
29 CD 16 NMOD
. . 9 VMOD
""")
tree = dg.tree()
tree.pprint()
if nx:
# currently doesn't work
import networkx
from matplotlib import pylab
g = dg.nx_graph()
g.info()
pos = networkx.spring_layout(g, dim=1)
networkx.draw_networkx_nodes(g, pos, node_size=50)
# networkx.draw_networkx_edges(g, pos, edge_color='k', width=8)
networkx.draw_networkx_labels(g, pos, dg.nx_labels)
pylab.xticks([])
pylab.yticks([])
pylab.savefig('tree.png')
pylab.show()
def plot_module_dependency_graph(graph):
"""
Plot a graph of specified yang modules. this function is used to plot
both the full dependency graph of all yang modules in the DB, or a
subgraph of dependencies for a specified module
:param graph: Graph to be plotted
:return: None
"""
# fixed_pos = { 'ietf-interfaces':(0.01,0.01) }
# fixed_nodes = fixed_pos.keys()
# pos = nx.spring_layout(graph, iterations=200,
# pos=fixed_pos, fixed=fixed_nodes)
#pos = nx.circular_layout(graph)
pos = nx.spring_layout(graph, iterations=2000)
# Draw RFC nodes (yang modules) in red
nx.draw_networkx_nodes(graph, pos=pos, nodelist=prune_graph_nodes(graph, RFC_TAG), node_size=200,
node_shape='s', node_color='red', alpha=0.5, linewidths=0.5)
# Draw draft nodes (yang modules) in green
nx.draw_networkx_nodes(graph, pos=pos, nodelist=prune_graph_nodes(graph, DRAFT_TAG), node_size=200,
node_shape='o', node_color='green', alpha=0.5, linewidths=0.5)
# Draw unknown nodes (yang modules) in orange
nx.draw_networkx_nodes(graph, pos=pos, nodelist=prune_graph_nodes(graph, UNKNOWN_TAG), node_size=200,
node_shape='^', node_color='orange', alpha=1.0, linewidths=0.5)
# Draw edges in light gray (fairly transparent)
nx.draw_networkx_edges(graph, pos=pos, alpha=0.25, linewidths=0.1, arrows=False)
# Draw labels on nodes (modules)
nx.draw_networkx_labels(graph, pos=pos, font_size=10, font_weight='bold', alpha=1.0)
