python plot

import networkx as nx
import numpy as np
import collections,math

import matplotlib.pyplot as plt
import matplotlib

def (ax,G,title,alpha,bias):
    degree_sequence=nx.degree(G) 
    size = len(degree_sequence) # total number of nodes
    cnt = collections.Counter(degree_sequence.values())

    # first, make dict_keys() type to list!!!
    x = list(cnt.keys())
    y = [n/size for n in list(cnt.values())] # list comprehension

    # in subplot, should use ax.set_XXX, while plt.XXX is okay
    ax.set_xscale('log')
    ax.set_yscale('log')
    ax.set_xlim((1e0,1e3)) # set the limit of x-axis
    ax.set_ylim((1e-4,1e0))
    ax.scatter(x,y,marker='x')

    x.remove(0) # remove the first encountered 0
    # ajust slope and bias
    y = [math.pow(n,-alpha)*math.pow(10,bias) for n in x]
    line, =ax.plot(x,y,linestyle='--',c='g')

    ax.set_title(title)
    ax.set_xlabel("Degree, k")
    ax.set_ylabel("Probability, P(k)")
    ax.legend([line], ['slope=-'+str(alpha)])


fG1 = nx.read_graphml("film1.graphml")
fG2 = nx.read_graphml("film2.graphml")

aG1 = nx.read_graphml("artist1.graphml")
aG2 = nx.read_graphml("artist2.graphml")

plt.close('all')

# Two subplots, the axes array is 1-d
f, axarr = plt.subplots(2, 2)

draw(axarr[0,0],fG1, "Simple Weighting Film Projection Network",2.2,1.1)
draw(axarr[0,1],fG2, "Hyperbolic Weighting Film Projection Network",2.2,1.1)
draw(axarr[1,0],aG1, "Simple Weighting Artist Projection Network",2.1,0.75)
draw(axarr[1,1],aG2, "Hyperbolic Weighting Artist Projection Network",2.1,0.75)

plt.show()