plot_SER_K.py

import numpy as np
import matplotlib.pyplot as plt
from constants import *

time_epochs = 7

msg_files = 1
puser_files = 1

# arrays for broadcast
Epidemic_opt_PQ = np.zeros(shape=(time_epochs, msg_files, puser_files))
Epidemic_pes_PQ = np.zeros(shape=(time_epochs, msg_files, puser_files))
Epidemic_TV = np.zeros(shape=(time_epochs, msg_files, puser_files))
Epidemic_LTE = np.zeros(shape=(time_epochs, msg_files, puser_files))
Epidemic_CBRS = np.zeros(shape=(time_epochs, msg_files, puser_files))
Epidemic_ISM = np.zeros(shape=(time_epochs, msg_files, puser_files))


num_mules = 48
num_channels = 10
num_Pusers = 100
msgMean = 15
T = 360
startTime = 1
days = "50"
dataset = "Lexington"
buffer_type = "PQ"
protocols = ["optimistic", "pessimistic", "TV", "LTE", "CBRS", "ISM"]
# protocols = ["Epidemic_Smart_optimistic"]
# fwd_strat = ["geo_3"]
fwd_strat = [1, 2, 3, 4, 5, 6, 7]
metrics_file = "metrics.txt"

p_id = 1 # p_id = 1 for PDR, = 2 for latency, and 3 for Energy, and 4 for overhead

for i in range(msg_files):
    for j in range(puser_files):
        for strat in fwd_strat:
            for protocol in protocols:
                if strat == 1:
                    t = 0
                elif strat == 2:
                    t = 1
                elif strat == 3:
                    t = 2
                elif strat == 4:
                    t = 3
                elif strat == 5:
                    t = 4
                elif strat == 6:
                    t = 5
                elif strat == 7:
                    t = 6
                elif strat == 15:
                    t = 7
                else:
                    t = 8


                if strat > 0:
                    path = "DataMules/" + dataset + "/" + days + "/3/Link_Exists/LE_" + str(startTime) + \
                           "_" + str(T) + "/Epidemic_Smart_" + protocol + "/" + buffer_type + "/geo_" + str(strat) + "/mules_" + \
                           str(num_mules) + "/channels_" + str(num_channels) + "/P_users_" + str(num_Pusers) + \
                           "/msgfile" + str(i) + "_" + str(msgMean) + "/puserfile" + str(j) + "/"
                else:
                    path = "DataMules/" + dataset + "/" + days + "/3/Link_Exists/LE_" + str(startTime) + \
                           "_" + str(T) + "/Epidemic_Smart_" + protocol + "/" + buffer_type + "/broadcast/mules_" + \
                           str(num_mules) + "/channels_" + str(num_channels) + "/P_users_" + str(num_Pusers) + \
                           "/msgfile" + str(i) + "_" + str(msgMean) + "/puserfile" + str(j) + "/"

                with open(path + metrics_file, "r") as f:
                    lines = f.readlines()[1:]

                for line in lines:
                    line_arr = line.strip().split()
                    if int(line_arr[0]) == 360:
                        if "optimistic" in protocol:
                            Epidemic_opt_PQ[t, i, j] = float(line_arr[p_id])
                        elif "pessimistic" in protocol:
                             Epidemic_pes_PQ[t, i, j] = float(line_arr[p_id])
                        elif "TV" in protocol:
                            Epidemic_TV[t, i, j] = float(line_arr[p_id])
                        elif "LTE" in protocol:
                            Epidemic_LTE[t, i, j] = float(line_arr[p_id])
                        elif "CBRS" in protocol:
                            Epidemic_CBRS[t, i, j] = float(line_arr[p_id])
                        elif "ISM" in protocol:
                            Epidemic_ISM[t, i, j] = float(line_arr[p_id])





optB_mean = []
optB_sd = []
pesB_mean = []
pesB_sd = []

TV_mean = []
TV_sd = []
LTE_mean = []
LTE_sd = []
CBRS_mean = []
CBRS_sd = []
ISM_mean = []
ISM_sd = []



optB_temp = []
pesB_temp = []

TV_temp = []
LTE_temp = []
CBRS_temp = []
ISM_temp = []

for t in range(len(Epidemic_opt_PQ)):

    t_arr_optB = []
    t_arr_pesB = []

    t_arr_tv = []
    t_arr_lte = []
    t_arr_cbrs = []
    t_arr_ism = []
    for i in range(len(Epidemic_opt_PQ[t])):
        for j in range(len(Epidemic_opt_PQ[t][i])):
            t_arr_optB.append(Epidemic_opt_PQ[t,i,j])
            t_arr_pesB.append(Epidemic_pes_PQ[t,i,j])
            t_arr_tv.append(Epidemic_TV[t, i, j])
            t_arr_lte.append(Epidemic_LTE[t, i, j])
            t_arr_cbrs.append(Epidemic_CBRS[t, i, j])
            t_arr_ism.append(Epidemic_ISM[t, i, j])


    optB_temp.append(t_arr_optB)
    pesB_temp.append(t_arr_pesB)
    TV_temp.append(t_arr_tv)
    LTE_temp.append(t_arr_lte)
    CBRS_temp.append(t_arr_cbrs)
    ISM_temp.append(t_arr_ism)

for i in range(len(optB_temp)):
    optB_mean.append(np.mean(optB_temp[i]))
    pesB_mean.append(np.mean(pesB_temp[i]))
    optB_sd.append(np.std(optB_temp[i]))
    pesB_sd.append(np.std(pesB_temp[i]))
    TV_mean.append(np.mean(TV_temp[i]))
    TV_sd.append(np.std(TV_temp[i]))
    LTE_mean.append(np.mean(LTE_temp[i]))
    LTE_sd.append(np.std(LTE_temp[i]))
    CBRS_mean.append(np.mean(CBRS_temp[i]))
    CBRS_sd.append(np.std(CBRS_temp[i]))
    ISM_mean.append(np.mean(ISM_temp[i]))
    ISM_sd.append(np.std(ISM_temp[i]))

print(len(optB_mean))
x = [1, 2, 3, 4, 5, 6, 7]
# x.append(0)
plt.xticks(fontsize=10)
plt.yticks(fontsize=25)
plt.xticks(x)
title_str = "Channels: " + str(num_channels) + "    Primary Users: " + str(num_Pusers)
# title_str = "Broadcast to everyone in range"
# plt.title(title_str)

# plt.xlim(0,12)
fig_name = "dummy.eps"

if p_id == 1:
    plt.ylabel('Message delivery ratio', fontsize=25)
    plt.xlabel('# mules to forward to', fontsize=25)
    plt.ylim(-0.1,1)

    fig_name = "Plots/pdr_K_SER.png"

if p_id == 2:
    plt.ylabel('Network delay (min)', fontsize=25)
    plt.xlabel('# mules to forward to', fontsize=25)

    fig_name = "Plots/latency_K_SER.png"

if p_id == 3:
    plt.ylabel('Energy per packet (J)', fontsize=25)
    plt.xlabel('# mules to forward to', fontsize=25)
    fig_name = "Plots/energy_K_SER.png"

if p_id == 4:
    plt.ylabel('Message overhead', fontsize=25)
    plt.xlabel('# mules to forward to', fontsize=25)
    # plt.ylim(-1, 20)
    fig_name = "Plots/overhead_K_SER.png"


plt.errorbar(x, optB_mean, optB_sd, marker='o', markersize=5, linestyle='-', linewidth=1, color="red")
plt.errorbar(x, pesB_mean, pesB_sd, marker='o', markersize=5, linestyle='-', linewidth=1, color="blue")

plt.errorbar(x, TV_mean, 0, marker='x', markersize=5, linestyle='--', linewidth=1, color="green")
plt.errorbar(x, LTE_mean, 0, marker='x', markersize=5, linestyle='--', linewidth=1, color="black")
plt.errorbar(x, CBRS_mean, 0, marker='x', markersize=5, linestyle='--', linewidth=1, color="brown")
plt.errorbar(x, ISM_mean, 0, marker='x', markersize=5, linestyle='--', linewidth=1, color="gray")


if p_id == 1:
    plt.legend(["Optimistic", "Pessimistic", "TV", "LTE", "CBRS", "ISM"], loc="lower right", fontsize=12, ncol = 2, frameon=False)
elif p_id == 2:
    plt.legend(["Optimistic", "Pessimistic", "TV", "LTE", "CBRS", "ISM"], loc="upper center", fontsize=12, ncol = 3, frameon=False)
elif p_id ==3:
    plt.legend(["Optimistic", "Pessimistic", "TV", "LTE", "CBRS", "ISM"], loc="upper right", fontsize=12, ncol = 2, frameon=False)
elif p_id ==4:
    plt.legend(["Optimistic", "Pessimistic", "TV", "LTE", "CBRS", "ISM"], loc="lower right", fontsize=12, ncol = 2, frameon=False)


plt.tight_layout()
plt.savefig(fig_name)

plt.show()