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plot_ssr.py
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plot_ssr.py
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import matplotlib as mpl
mpl.rcParams['figure.dpi'] = 300
import matplotlib.pyplot as plt
import numpy as np
import cmath
from scipy.io import loadmat, savemat
import pandas as pd
import os
import copy
import math
######################################################
# new for energy
# energy related parameters of rotary-wing UAV
# based on Energy Minimization in Internet-of-Things System Based on Rotary-Wing UAV
P_i = 790.6715
P_0 = 580.65
U2_tip = (200) ** 2
s = 0.05
d_0 = 0.3
p = 1.225
A = 0.79
delta_time = 0.1 #0.1/1000 #0.1ms
# add ons hover veloctiy
# based on https://www.intechopen.com/chapters/57483
m = 1.3 # mass: assume 1.3kg https://www.droneblog.com/average-weights-of-common-types-of-drones/#:~:text=In%20most%20cases%2C%20toy%20drones,What%20is%20this%3F
g = 9.81 # gravity
T = m * g # thrust
v_0 = (T / (A * 2 * p)) ** 0.5
def get_energy_consumption(v_t):
'''
arg
1) v_t = displacement per time slot
'''
energy_1 = P_0 \
+ 3 * P_0 * (abs(v_t)) ** 2 / U2_tip \
+ 0.5 * d_0 * p * s * A * (abs(v_t))**3
energy_2 = P_i * ((
(1 + (abs(v_t) ** 4) / (4 * (v_0 ** 4))) ** 0.5 \
- (abs(v_t) ** 2) / (2 * (v_0 **2)) \
) ** 0.5)
energy = delta_time * (energy_1 + energy_2)
return energy
ENERGY_MIN = get_energy_consumption(0.25)
ENERGY_MAX = get_energy_consumption(0)
######################################################
# modified from data_manager.py
init_data_file = 'data/init_location.xlsx'
def read_init_location(entity_type = 'user', index = 0):
if entity_type == 'user' or 'attacker' or 'RIS' or 'RIS_norm_vec' or 'UAV':
return np.array([\
pd.read_excel(init_data_file, sheet_name=entity_type)['x'][index],\
pd.read_excel(init_data_file, sheet_name=entity_type)['y'][index],\
pd.read_excel(init_data_file, sheet_name=entity_type)['z'][index]])
else:
return None
# load and plot everything
class LoadAndPlot(object):
"""
load date and plot 2022-07-22 16_16_26
"""
def __init__(self, store_paths, \
user_num = 2, attacker_num = 1, RIS_ant_num = 4, \
ep_num = 300, step_num = 100): # RIS_ant_num = 16 (not true)
self.store_paths = store_paths
self.color_list = ['b', 'c', 'g', 'k', 'm', 'r', 'y']
# self.store_path = store_path + '//'
self.user_num = user_num
self.attacker_num = attacker_num
self.RIS_ant_num = RIS_ant_num
self.ep_num = ep_num
self.step_num = step_num
def load_one_ep(self, file_name):
m = loadmat(self.store_path + file_name)
return m
def load_all_steps(self):
result_dic = {}
result_dic.update({'reward':[]})
result_dic.update({'user_capacity':[]})
for i in range(self.user_num):
result_dic['user_capacity'].append([])
result_dic.update({'secure_capacity':[]})
for i in range(self.user_num):
result_dic['secure_capacity'].append([])
result_dic.update({'attaker_capacity':[]})
for i in range(self.attacker_num):
result_dic['attaker_capacity'].append([])
result_dic.update({'RIS_elements':[]})
for i in range(self.RIS_ant_num):
result_dic['RIS_elements'].append([])
for ep_cnt in range(self.ep_num):
mat_ep = self.load_one_ep("simulation_result_ep_" + str(ep_cnt) + ".mat")
one_ep_reward = mat_ep["result_" + str(ep_cnt)]["reward"][0][0]
result_dic['reward'] += list(one_ep_reward[:, 0])
one_ep_user_capacity = mat_ep["result_" + str(ep_cnt)]["user_capacity"][0][0]
for i in range(self.user_num):
result_dic['user_capacity'][i] += list(one_ep_user_capacity[:, i])
one_ep_secure_capacity = mat_ep["result_" + str(ep_cnt)]["secure_capacity"][0][0]
for i in range(self.user_num):
result_dic['secure_capacity'][i] += list(one_ep_secure_capacity[:, i])
one_ep_attaker_capacity = mat_ep["result_" + str(ep_cnt)]["attaker_capacity"][0][0]
for i in range(self.attacker_num):
result_dic['attaker_capacity'][i] += list(one_ep_attaker_capacity[:, i])
one_ep_RIS_first_element = mat_ep["result_" + str(ep_cnt)]["reflecting_coefficient"][0][0]
for i in range(self.RIS_ant_num):
result_dic['RIS_elements'][i] += list(one_ep_RIS_first_element[:, i])
return result_dic
def plot(self):
"""
plot result
b--blue c--cyan(青色） g--green k--black m--magenta（紫红色） r--red w--white y--yellow
"""
###############################
# plot average sum secrecy rate of each episode
###############################
fig = plt.figure('average_sum_secrecy_rate')
# store_paths = ['data/storage/ddpg 4', 'data/storage/td3 3', 'data/storage/ddpg seem 6', 'data/storage/td3 seem 2']
# store_paths = r'data/storage/scratch/td3_ssr'
legends = ['TDDRL', 'TTD3', 'TDDRL (Energy Penalty)', 'TTD3 (Energy Penalty)']
all_average_sum_secrecy_rate = []
for store_path, legend in zip(self.store_paths, legends):
self.store_path = store_path + '//'
self.all_steps = self.load_all_steps()
sum_secrecy_rate = np.array(self.all_steps['secure_capacity'])
sum_secrecy_rate = np.sum(sum_secrecy_rate, axis = 0)
average_sum_secrecy_rate = []
ssr = []
for i in range(0, self.ep_num * self.step_num, self.step_num):
ssr_one_episode = sum_secrecy_rate[i:i+self.step_num] # ssr means Sum Secrecy Rate
ssr.append(ssr_one_episode)
try:
_ = sum(ssr_one_episode) / len(ssr_one_episode)
except:
_ = 0
average_sum_secrecy_rate.append(_)
all_average_sum_secrecy_rate.append(average_sum_secrecy_rate)
plt.plot(range(len(average_sum_secrecy_rate)), average_sum_secrecy_rate, label=legend)
plt.xlabel("Episodes (Ep)")
plt.ylabel("Average Sum Secrecy Rate")
plt.legend()
plt.savefig('data/average_sum_secrecy_rate.png')
# transpose
'''
all_average_sum_secrecy_rate = np.array(all_average_sum_secrecy_rate)
all_average_sum_secrecy_rate = np.transpose(all_average_sum_secrecy_rate)
all_average_sum_secrecy_rate = list(all_average_sum_secrecy_rate)
'''
# dictionary of lists
dict = {legend: average_sum_secrecy_rate for legend, average_sum_secrecy_rate in zip(legends, all_average_sum_secrecy_rate)}
df = pd.DataFrame(dict)
df.to_excel('data/average_sum_secrecy_rate.xlsx', index=False)
if __name__ == '__main__':
LoadPlotObject = LoadAndPlot(
store_paths = ['data/storage/scratch/ddpg_ssr', 'data/storage/scratch/td3_ssr', 'data/storage/scratch/ddpg_see', 'data/storage/scratch/td3_see'],
ep_num = 300,
)
LoadPlotObject.plot()