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_paths = store_paths + '//' 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 secrecy energy efficient of each episode ############################### fig = plt.figure('average_secrecy_energy_efficiency') # get init location init_uav_coord = read_init_location(entity_type = 'UAV') init_user_coord_0 = read_init_location(entity_type = 'user', index=0) init_user_coord_1 = read_init_location(entity_type = 'user', index=1) # paths legends = ['TDDRL', 'TTD3', 'TDDRL (Energy Penalty)', 'TTD3 (Energy Penalty)'] all_average_see = [] all_energies = [] # energies for store_path in self.store_paths: energies = [] for i in range(self.ep_num): # read the mat file filename = f'simulation_result_ep_{i}.mat' filename = os.path.join(store_path, filename) data = loadmat(filename) # v_ts energies_one_episode = [] # loop all uav movt uav_movt = data[f'result_{i}'][0][0][-1] for j in range(uav_movt.shape[0]): move_x = uav_movt[j][0] move_y = uav_movt[j][1] v_t = (move_x ** 2 + move_y ** 2) ** 0.5 energy = get_energy_consumption(v_t / delta_time) energies_one_episode.append(energy) energies.append(energies_one_episode) all_energies.append(energies) # see for store_path, legend in zip(self.store_paths, legends): average_see = [] # ssr 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) # energy energies = all_energies.pop(0) 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 energies_one_episode = energies.pop(0) ssr_one_episode = ssr_one_episode[:len(energies_one_episode)] energies_one_episode = energies_one_episode[:len(ssr_one_episode)] try: see = np.array(ssr_one_episode) / np.array(energies_one_episode) average_see.append(sum(see)/len(see)) except: average_see.append(0) # change from /J to /kJ average_see = np.array(average_see) * 1000 average_see = list(average_see) all_average_see.append(average_see) plt.plot(range(len(average_see)), average_see, label=legend) plt.xlabel("Episodes (Ep)") plt.ylabel("Average Secrecy Energy Efficiency") plt.legend() plt.savefig('data/average_secrecy_energy_efficiency.png') # dictionary of lists dict = {legend: average_see for legend, average_see in zip(legends, all_average_see)} df = pd.DataFrame(dict) df.to_excel('data/average_secrecy_energy_efficiency.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()