import networkx as nx

import matplotlib.pylab as plt

import scipy.stats as stats

import numpy as np

G = nx.degree(G=nx.read_edgelist("carolinedata"))

options = {

}

temp = []

for i in list(G):

try:

temp.append(float(i[1]))

except:

pass

# temp.sort()

h = enumerate(range(0, len(temp)))

print(temp)

x1 = np.array(temp) # random data, normal distribution

xs = np.linspace(x1.min() - 1, x1.max() + 1, len(x1))

print(x1)

kde1 = stats.gaussian_kde(x1)

kde2 = stats.gaussian_kde(x1, bw_method='silverman')

fig = plt.figure(figsize=(8, 6))

plt.plot(xs, kde2(xs), 'b-', label="Happiness line")

plt.legend(loc=1)

plt.xlabel("Exercise hours per week")

plt.ylabel("happiness density distribution")

plt.title("Probability density function for exercise versus happiness ")

plt.savefig("m silverman log non ranged estimation.png", dpi=500)

plt.show()