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analyze_reports.py
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analyze_reports.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Feb 24 00:10:06 2020
@author: sshanto
"""
import numpy as np
import matplotlib.pyplot as plt
def ana():
f = open("draft_2/experiment_1/data_files/average_parameters_for_all_cases_combined.txt","r")
titles = []
vals = []
cases = []
time = []
time_t = []
clsize = []
clnum = []
clsize_t = []
clnum_t = []
for x in f:
x = x.split(" ")
y = x[2:5]
title = "_".join(y)
val = float(x[-3])
case = x[-1]
case = [i for i in case if i != "\n"]
case = "".join(case)
if title == "Time_period_of":
if val != 1199.0 :
time.append(val)
time_t.append(case)
if title == "Mean_cluster_size":
clsize.append(val)
clsize_t.append(case)
if title == "Number_of_clusters":
clnum.append(val)
clnum_t.append(case)
titles.append(title)
vals.append(val)
cases.append(case)
return titles, vals, cases, time, time_t, clsize, clnum, clsize_t, clnum_t
sim_type = ana()[2]
param = ana()[0]
param_val = ana()[1]
time = ana()[3]
group = ana()[4]
grp = ana()[7]
clsize = ana()[5]
clnum =ana()[6]
def shrt(q):
x = []
for i in q:
x.append(i[0])
res = "".join(x)
return res
def plotter(fig, t, group,key):
"""
types = []
for i in group:
types.append(shrt(i.split("_")))
x_coords = [i for i in range(len(t))]
y_coords = t
# print(types)
j = 0
for i,type in enumerate(types):
x = x_coords[i]
y = y_coords[i]
barlist = plt.bar(x, y, label = type)
# plt.legend()
plt.text(x-0.5, y+0.006, type, fontsize=9)
barlist[0].set_color('r')
if j%5 == 0:
barlist[0].set_color('g')
if j%5 == 1:
barlist[0].set_color('b')
if j%5 == 2:
barlist[0].set_color('k')
if j%5 == 3:
barlist[0].set_color('y')
j+=1
plt.show()
"""
# set width of bar
barWidth = 0.15
# set height of bar
bars1 = [t[0],t[5],t[10]]
bars2 = [t[1],t[6],t[11]]
bars3 = [t[2],t[7],t[12]]
bars4 = [t[3],t[8],t[13]]
bars5 = [t[4],t[9],t[14]]
# Set position of bar on X axis
r1 = np.arange(len(bars1))
r2 = [x + barWidth for x in r1]
r3 = [x + barWidth for x in r2]
r4 = [x + barWidth for x in r3]
r5 = [x + barWidth for x in r4]
print(group)
# Make the plot
plt.bar(r1, bars1, color='#7f6d5f', width=barWidth, edgecolor='white', label='opportunistic')
plt.bar(r2, bars2, color='#557f2d', width=barWidth, edgecolor='white', label='neighbor aware')
plt.bar(r3, bars3, color='#2d7f5e', width=barWidth, edgecolor='white', label='oppo and neighbor aware')
plt.bar(r4, bars4, color='#1B4D03', width=barWidth, edgecolor='white', label='baseline')
plt.bar(r5, bars5, color='#349B05', width=barWidth, edgecolor='white', label='baseline headway')
# Add xticks on the middle of the group bars
plt.xlabel('Regime')
plt.xticks([r + barWidth for r in range(len(bars1))], ['Low Density', 'Critical Density', 'High Density'])
plt.title("Average Survival "+key+ " for different Models and Regimes")
plt.ylabel("Average Survival " +key)
# Create legend & Show graphic
plt.legend()
plt.savefig("draft_2/experiment_1/figures/" + fig)
plt.show()
def plotter1(fig, t, group,key):
# set width of bar
barWidth = 0.15
# set height of bar
bars1 = [t[0],t[5]]
bars2 = [t[1],t[6]]
bars3 = [t[2],t[7]]
bars4 = [t[3],t[8]]
bars5 = [t[4],t[9]]
# Set position of bar on X axis
r1 = np.arange(len(bars1))
r2 = [x + barWidth for x in r1]
r3 = [x + barWidth for x in r2]
r4 = [x + barWidth for x in r3]
r5 = [x + barWidth for x in r4]
print(group)
# Make the plot
plt.bar(r1, bars1, color='#7f6d5f', width=barWidth, edgecolor='white', label='opportunistic')
plt.bar(r2, bars2, color='#557f2d', width=barWidth, edgecolor='white', label='neighbor aware')
plt.bar(r3, bars3, color='#2d7f5e', width=barWidth, edgecolor='white', label='oppo and neighbor aware')
plt.bar(r4, bars4, color='#1B4D03', width=barWidth, edgecolor='white', label='baseline')
plt.bar(r5, bars5, color='#349B05', width=barWidth, edgecolor='white', label='baseline headway')
# Add xticks on the middle of the group bars
plt.xlabel('Regime')
plt.xticks([r + barWidth for r in range(len(bars1))], ['Low Density', 'Critical Density', 'High Density'])
plt.title("Average Survival "+key+ " for different Models and Regimes")
plt.ylabel("Average Survival " +key)
# Create legend & Show graphic
plt.legend()
plt.savefig("draft_2/experiment_1/figures/" + fig)
plt.show()
def ana_clusterability():
f = open("draft_2/experiment_1/data_files/clusterability.txt","r")
titles = []
clval = []
for x in f:
x = x.split(",")
titles.append(x[0])
y = float((x[1].split("\n")[0]))
clval.append(y)
return titles, clval
tit = ana_clusterability()[0]
c1 = ana_clusterability()[1]
#print(tit)
plotter1("time_report_exp1.png", time, group, "Time")
plotter("cluster_size_report_exp1.png", clsize, grp, "Cluster Size")
plotter("cluster_num_report_exp1.png", clnum, grp, "Cluster Number")
plotter("clusterability_report.png", c1, tit, "Clusterability")
# plot and think about comparing other values for analysis