if UAV_mode not in res[network_type]: res[network_type][UAV_mode] = [] if UAV_mode not in UAV_modes: UAV_modes[UAV_mode] = True if network_type not in network_types: network_types[network_type] = True res[network_type][UAV_mode].append(lifetime) input_file.close() # generate average lifetime figures nodes = [] for network_type in network_types: group = float(network_type.split('_')[5]) # transfer_rate for UAV_mode in res[network_type].keys(): node = {} node['label'] = UAV_mode node['value'] = np.mean(res[network_type][UAV_mode]) node['error'] = np.std(res[network_type][UAV_mode]) node['group'] = group print node['label'], node['value'], node['group'] nodes.append(node) fm.draw_bar_err_group_figure(nodes, 'Charging Efficiency Rate', 'Lifetime (day)', 'Lifetime by Charging Efficiency Rate and Algorithms') plt.show() fm.draw_normalized_bar_err_group_figure(nodes, 'Charging Efficiency Rate', 'Normalized Lifetime', 'Normalized Lifetime by Charging Efficiency Rate and Algorithms') plt.show()
if network_type not in res: res[network_type] = {} if UAV_mode not in res[network_type]: res[network_type][UAV_mode] = [] if UAV_mode not in UAV_modes: UAV_modes[UAV_mode] = True if network_type not in network_types: network_types[network_type] = True res[network_type][UAV_mode].append(lifetime) input_file.close() # generate average lifetime figures nodes = [] for network_type in network_types: group = int(network_type.split('_')[8]) # charging energy consumption rate for UAV_mode in res[network_type].keys(): node = {} node['label'] = UAV_mode node['value'] = np.mean(res[network_type][UAV_mode]) node['error'] = np.std(res[network_type][UAV_mode]) node['group'] = group nodes.append(node) fm.draw_bar_err_group_figure(nodes, 'Energy Consumption Rate of Transfer (W)', 'Lifetime (day)', 'Lifetime by Energy Consumption Rate of Transfer and Algorithms') plt.show() fm.draw_normalized_bar_err_group_figure(nodes, 'Energy Consumption Rate of Transfer (W)', 'Normalized Lifetime', 'Normalized Lifetime by Energy Consumption Rate of Transfer and Algorithms') plt.show()
if UAV_mode not in UAV_modes: UAV_modes[UAV_mode] = True if network_type not in network_types: network_types[network_type] = True res[network_type][UAV_mode].append(lifetime) input_file.close() # generate average lifetime figures nodes = [] for network_type in network_types: group = int(network_type.split('_')[9]) # base distance for UAV_mode in res[network_type].keys(): node = {} node['label'] = UAV_mode node['group'] = group node['value'] = np.mean(res[network_type][UAV_mode]) node['error'] = np.std(res[network_type][UAV_mode]) nodes.append(node) fm.draw_bar_err_group_figure(nodes, 'Distance of UAV Base Station (m)', 'Lifetime (day)', 'Lifetime by Distance of UAV Base Station and Algorithms') plt.show() fm.draw_normalized_bar_err_group_figure(nodes, 'Distance of UAV Base Station (m)', 'Normalized Lifetime', 'Normalized Distance of UAV Base Station and Algorithms') plt.show()
if UAV_mode not in UAV_modes: UAV_modes[UAV_mode] = True if network_type not in network_types: network_types[network_type] = True res[network_type][UAV_mode].append(lifetime) input_file.close() # generate average lifetime figures nodes = [] for network_type in network_types: group = int(float(network_type.split('_')[10])) / (3600)# UAV power capacity (WH) for UAV_mode in res[network_type].keys(): node = {} node['label'] = UAV_mode node['group'] = group node['value'] = np.mean(res[network_type][UAV_mode]) node['error'] = np.std(res[network_type][UAV_mode]) nodes.append(node) fm.draw_bar_err_group_figure(nodes, 'UAV Energy Capacity (WH)', 'Lifetime (day)', 'Lifetime by UAV Energy Capacity and Algorithms') plt.show() fm.draw_normalized_bar_err_group_figure(nodes, 'UAV Energy Capacity (WH)', 'Normalized Lifetime', 'Normalized Lifetime by UAV Energy Capacity and Algorithms') plt.show()