#!/usr/bin/env python3importnumpyasnpimportmatplotlibimportmatplotlib.pyplotaspltimportmatplotlib.tickerastickerimportcollectionsdefplot_figure(distribution):percentile= [95,99]percentile_color='yg'mean_color='c'bins=100legend_handle= []legend_title= []fig, (ax0,ax1)=plt.subplots(2,sharex=True)fig.subplots_adjust(hspace=0)mu,sigma=100,15xs_downstream=mu+sigma*np.random.randn(10000)xs_upstream=mu+sigma*np.random.randn(10000)xmin=min(np.min(xs_downstream),np.min(xs_upstream))xmax=max(np.max(xs_downstream),np.max(xs_upstream))hist0=ax0.hist(xs_downstream,bins=bins,range=(xmin,xmax),color='red',label='{} workload with Proxy'.format(distribution.capitalize()))handles,labels=ax0.get_legend_handles_labels()line=ax0.axvline(x=np.mean(xs_downstream),color=mean_color,linewidth=1)legend_title.append('Mean')legend_handle.append(line)forpercent,colorinzip(percentile,percentile_color):line=ax0.axvline(x=xs_downstream[int(percent/100*len(xs_downstream))],color=color,linewidth=1)legend_title.append('{}th percentile'.format(percent))legend_handle.append(line)ax0.set_yscale('log')legend_title+=labelslegend_handle+=handleshist1=ax1.hist(xs_upstream,bins=bins,range=(xmin,xmax),color='blue',label='{} workload without Proxy'.format(distribution.capitalize()))handles,labels=ax1.get_legend_handles_labels()legend_title+=labelslegend_handle+=handlesax1.axvline(x=np.mean(xs_upstream),color=mean_color,linewidth=1)forpercent,colorinzip(percentile,percentile_color):ax1.axvline(x=xs_upstream[int(percent/100*len(xs_upstream))],color=color,linewidth=1)ax1.set_yscale('log')ax0.legend(legend_handle,legend_title)ax1.set_xlabel('latency (ms)')fig.savefig('latency-{}.pdf'.format(distribution))defmain():plot_figure('zipf')plot_figure('uniform')if__name__=='__main__':main()