def show_graph_with_labels(fname, adjacency_matrix, mylabels):
def make_label_dict(labels):
l = {}
for i, label in enumerate(labels):
l[i] = label
return l
gr = nx.Graph(adjacency_matrix)
for k, v in make_label_dict(mylabels).items():
gr.node[k]["label"] = v
nx.draw(gr,
node_color='#36C8FF',
edge_color='#36C8FF',
node_size=1,
labels=make_label_dict(mylabels),
with_labels=True)
nx.write_gexf(gr, "graphlogMI.gexf")
nx.relabel_gexf_graph(gr)
def plotDegreeRatio(gexFile):
'''plot the degree ratio from a supported gexFile'''
#setup our digraph from a gexf (gephi) format #read_gexf
DG = nx.DiGraph(nx.read_gexf(gexFile))
#turn node labels into dictionary node keys
DG = nx.relabel_gexf_graph(DG)
#generate networkx friendly position format
#dictionary keyed by node label with values being a float32 ndarray
pos = dict()
for i in range(1, len(DG.node)+1):
xPos = DG.node[str(i)]['viz']['position']['x']
yPos = DG.node[str(i)]['viz']['position']['y']
pos[str(i)] = np.array([xPos,yPos])
#get degree ratio (in to out) for color information
inDeg = DG.in_degree()
outDeg = DG.out_degree()
degreeRatio = dict()
#node labels start at 1
for i in range(1,len(DG.node)+1):
#dont want to divide by zero, we'll handle this below
if outDeg[str(i)] != 0:
#degrees are ints so convert
degreeRatio[str(i)] = float(inDeg[str(i)]) / float(outDeg[str(i)])
#we're going to drop it to 1 when over 1
if degreeRatio[str(i)]>1:
degreeRatio[str(i)]=1
#in this case, we'll set +inf to 1
elif inDeg[str(i)] > 0:
degreeRatio[str(i)]=1
else: #both were zero
degreeRatio[str(i)]=0
#draw!
nx.draw_networkx_edges(DG,pos,nodelist=degreeRatio.keys(),alpha=0.05,
arrows=True)
nx.draw_networkx_nodes(DG,pos,nodelist=degreeRatio.keys(),
node_size=20,
node_color= degreeRatio.values(),
cmap=plt.cm.jet)
plt.show()
nx.draw_networkx_nodes(G, pos, node_size=[x * scaler for x in si])
# edges
mst = nx.minimum_spanning_edges(G, data=False)
nx.draw_networkx_edges(G, pos, width=1, edgelist=list(mst))
# labels; make them but dont actually draw them
labels = {ii: label for ii, label in enumerate(labels)}
nx.draw_networkx_labels(G, pos, font_size=10, font_family='sans-serif')
plt.axis('off')
# plt.savefig("weighted_graph.png") # save as png
# legend
props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
# ax.text(-0.15, 0.95, textstr, transform=ax.transAxes,
# fontsize=14, verticalalignment='top', bbox=props)
plt.axis('off')
# write to file in gexf format, gephi is much better for plotting networks
mst = nx.minimum_spanning_tree(G)
ws = nx.get_edge_attributes(mst, 'weight')
for key, value in ws.items():
ws[key] = round(1 - value * value / 2, 2)
nx.set_edge_attributes(mst, 'weight', ws)
nx.set_node_attributes(mst, "label", labels)
nx.relabel_gexf_graph(mst)
nx.write_gexf(mst, "mst_courses.gexf")
plt.show() # display
