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ax1.plot(ds, delays_1, color='r', label=r"$N_1=$"+str(N1)) - ax1.plot(ds, delays_2, color='g', label=r"$N_2=$"+str(N2)) - ax1.plot(ds, delays_3, color='b', label=r"$N_3=$"+str(N3)) - ax2.plot(ds, ds, color='y', label="Bandwidth ampl.") - print(delays_1) - print(ds) + ax1.plot(ds, delays_1, color='r', label=str(N1)+" nodes") + ax1.plot(ds, delays_2, color='g', label=str(N2)+" nodes") + ax1.plot(ds, delays_3, color='b', label=str(N3)+" nodes") + ax2.plot(ds, ds, color='y', label="Bandwidth\namplification") ax1.autoscale(tight=True) ax2.autoscale(tight=True) - ax1.legend(loc=0) - ax2.legend(loc=0) - ax1.set(title='Worst case maximum hops vs bandwidth') - ax1.set(**dict(xlabel='Outbound degree (D)', ylabel='Amount of hops')) - ax2.set(**dict(ylabel='Bandwidth amplification')) - #ax2.set_xlim([2, 14]) - fig.savefig('message_hops.svg', dpi=300) + ax1.legend(loc=(0.08, 0.7)) + ax2.legend(loc=(0.5, 0.76)) + ax1.set(title='$h_{max}$ and bandwidth amplification depending on node degree $D$\n') + ax1.set(**dict(xlabel='Node degree (D)', ylabel='Maximum number of hops ($h_{max}$)\n')) + ax2.set(**dict(ylabel='\nBandwidth amplification')) + # Set the limits of the x-axis and y-axis + ax1.set_xlim(2, 14) + ax1.set_ylim(2, ax1.get_ylim()[1]) # Ensure that the lower limit is 2 + + # Set the limits of the second y-axis to match the first y-axis + ax2.set_ylim(ax1.get_ylim()) + + fig.savefig('message_hops.svg', dpi=600) plt.close() - 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ax[0][0].set(ylabel='Cumulative message share') + ax[0][0].set(ylabel='Message share') ax[1][0].set(xlabel='Latency (ms)', ylabel='Cumulative message share') ax[1][1].set(xlabel='Latency (ms)')