<gh_stars>10-100
import matplotlib.pyplot as plt
import matplotlib.ticker as tkr
import matplotlib.axes as axes
import math
import numpy as np

with open("../../../data/output/ChungLu/condMat/ddChapelCL_E.csv", 'r') as f:
	output_ddCL_E = f.read().splitlines()

with open("../../../data/output/ChungLu/condMat/ddChapelCL_V.csv", 'r') as f:
	output_ddCL_V = f.read().splitlines()

with open("../../../data/output/ChungLu/condMat/mpdChapelCL_E.csv", 'r') as f:
	output_mpdCL_E = f.read().splitlines()

with open("../../../data/output/ChungLu/condMat/mpdChapelCL_V.csv", 'r') as f:
	output_mpdCL_V = f.read().splitlines()


with open("../../../data/condMat/dd_E.csv", 'r') as f:
	input_dd_E = f.read().splitlines()

with open("../../../data/condMat/dd_V.csv", 'r') as f:
	input_dd_V = f.read().splitlines()

with open("../../../data/condMat/mpd_E.csv", 'r') as f:
	input_mpd_E = f.read().splitlines()

with open("../../../data/condMat/mpd_V.csv", 'r') as f:
	input_mpd_V = f.read().splitlines()

input_e_x = []
input_e_y = []
input_v_x = []
input_v_y = []
output_e_x = []
output_e_y = []
output_v_x = []
output_v_y = []

#output_mpdCL_E.sort(reverse=True)
#output_mpdCL_V.sort(reverse=True)
#input_mpd_E.sort(reverse=True)
#input_mpd_V.sort(reverse=True)

for i in range(len(output_mpdCL_E)):
	#for j in range(len(output_ddCL_E[i])):
	#	output_e_x.append(i+1)
	#	output_e_y.append(output_mpdCL_E[i])
    output_e_x.append(i+1)
    output_e_y.append(float(output_mpdCL_E[i]))

for i in range(len(output_mpdCL_V)):
	#for j in range(len(output_ddCL_V[i])):
	#	output_v_x.append(i+1)
	#	output_v_y.append(output_mpdCL_V[i])
    output_v_x.append(i+1)
    output_v_y.append(float(output_mpdCL_V[i]))

for i in range(len(input_mpd_E)):
	#for j in range(len(input_dd_E[i])):
	#	input_e_x.append(i+1)
	#	input_e_y.append(input_mpd_E[i])
    input_e_x.append(i+1)
    input_e_y.append(float(input_mpd_E[i]))

for i in range(len(input_mpd_V)):
	#for j in range(len(input_dd_V[i])):
	#	input_v_x.append(i+1)
	#	input_v_y.append(input_mpd_V[i])
    input_v_x.append(i+1)
    input_v_y.append(float(input_mpd_V[i]))
    #print(input_mpd_V[i])

#plt.axis(xscale='log', yscale='log')

#fig, ax = plt.subplots()

plt.semilogx(output_e_x, output_e_y, marker = 'o', linestyle = '--', color = 'r', label='Output E')
plt.semilogx(output_v_x, output_v_y, marker = 'o', linestyle = '--', color='b', label='Output V')
plt.semilogx(input_e_x, input_e_y, marker = 'o', linestyle = '--', color='g', label='Input E')
plt.semilogx(input_v_x, input_v_y, marker = 'o', linestyle = '--', color='black', label='Input V')
plt.xlabel('Degree')
plt.ylabel('PDMC')
plt.title('Chung Lu Visual Verification')
plt.legend()
plt.show()

import matplotlib.pyplot as plt
import matplotlib.ticker as tkr
import matplotlib.axes as axes
import math
import numpy as np

with open("../../../data/output/ChungLu/condMat/ddChapelCL_E.csv", 'r') as f:
	output_ddCL_E = f.read().splitlines()

with open("../../../data/output/ChungLu/condMat/ddChapelCL_V.csv", 'r') as f:
	output_ddCL_V = f.read().splitlines()

with open("../../../data/output/ChungLu/condMat/mpdChapelCL_E.csv", 'r') as f:
	output_mpdCL_E = f.read().splitlines()

with open("../../../data/output/ChungLu/condMat/mpdChapelCL_V.csv", 'r') as f:
	output_mpdCL_V = f.read().splitlines()


with open("../../../data/condMat/dd_E.csv", 'r') as f:
	input_dd_E = f.read().splitlines()

with open("../../../data/condMat/dd_V.csv", 'r') as f:
	input_dd_V = f.read().splitlines()

with open("../../../data/condMat/mpd_E.csv", 'r') as f:
	input_mpd_E = f.read().splitlines()

with open("../../../data/condMat/mpd_V.csv", 'r') as f:
	input_mpd_V = f.read().splitlines()

input_e_x = []
input_e_y = []
input_v_x = []
input_v_y = []
output_e_x = []
output_e_y = []
output_v_x = []
output_v_y = []

#output_mpdCL_E.sort(reverse=True)
#output_mpdCL_V.sort(reverse=True)
#input_mpd_E.sort(reverse=True)
#input_mpd_V.sort(reverse=True)

for i in range(len(output_mpdCL_E)):
	#for j in range(len(output_ddCL_E[i])):
	#	output_e_x.append(i+1)
	#	output_e_y.append(output_mpdCL_E[i])
    output_e_x.append(i+1)
    output_e_y.append(float(output_mpdCL_E[i]))

for i in range(len(output_mpdCL_V)):
	#for j in range(len(output_ddCL_V[i])):
	#	output_v_x.append(i+1)
	#	output_v_y.append(output_mpdCL_V[i])
    output_v_x.append(i+1)
    output_v_y.append(float(output_mpdCL_V[i]))

for i in range(len(input_mpd_E)):
	#for j in range(len(input_dd_E[i])):
	#	input_e_x.append(i+1)
	#	input_e_y.append(input_mpd_E[i])
    input_e_x.append(i+1)
    input_e_y.append(float(input_mpd_E[i]))

for i in range(len(input_mpd_V)):
	#for j in range(len(input_dd_V[i])):
	#	input_v_x.append(i+1)
	#	input_v_y.append(input_mpd_V[i])
    input_v_x.append(i+1)
    input_v_y.append(float(input_mpd_V[i]))
    #print(input_mpd_V[i])

#plt.axis(xscale='log', yscale='log')

#fig, ax = plt.subplots()

plt.semilogx(output_e_x, output_e_y, marker = 'o', linestyle = '--', color = 'r', label='Output E')
plt.semilogx(output_v_x, output_v_y, marker = 'o', linestyle = '--', color='b', label='Output V')
plt.semilogx(input_e_x, input_e_y, marker = 'o', linestyle = '--', color='g', label='Input E')
plt.semilogx(input_v_x, input_v_y, marker = 'o', linestyle = '--', color='black', label='Input V')
plt.xlabel('Degree')
plt.ylabel('PDMC')
plt.title('Chung Lu Visual Verification')
plt.legend()
plt.show()
#output_mpdCL_E.sort(reverse=True) #output_mpdCL_V.sort(reverse=True) #input_mpd_E.sort(reverse=True) #input_mpd_V.sort(reverse=True) #for j in range(len(output_ddCL_E[i])): #	output_e_x.append(i+1) #	output_e_y.append(output_mpdCL_E[i]) #for j in range(len(output_ddCL_V[i])): #	output_v_x.append(i+1) #	output_v_y.append(output_mpdCL_V[i]) #for j in range(len(input_dd_E[i])): #	input_e_x.append(i+1) #	input_e_y.append(input_mpd_E[i]) #for j in range(len(input_dd_V[i])): #	input_v_x.append(i+1) #	input_v_y.append(input_mpd_V[i]) #print(input_mpd_V[i]) #plt.axis(xscale='log', yscale='log') #fig, ax = plt.subplots()