def drawpic(trig): timedata=np.fromstring(''.join(open('%s.time'%trig,'r').read().splitlines()),sep=' ') fig,axes=plt.subplots(2,1,figsize=(9,6)) axes[0].hist(timedata/1000000,bins=64,color='blue',histtype='step',label='64ms') axes[0].set_title('%s'%trig) axes[0].set_xlabel('Time/second') axes[0].set_ylabel('Count') axes[0].legend(loc='best') axes[1].hist(timedata/1000000,bins=1000,color='blue',histtype='step',normed='True',label='bins=1s') bbhist(timedata/1000000,bins='blocks',color='red',histtype='step',normed='True',label='bayesian') axes[1].legend(loc='best') axes[1].set_title('Bayesian block&1s') axes[1].set_xlabel('Time/second') axes[1].set_ylabel('Count Rate($s^{-1}$)') axes[1].set_ylim([0,4]) fig.tight_layout() fig.savefig('%s.png'%trig,dpi=200)
# total data # <codecell> #We use standard histogram as background, then Knuth bins & Bayesian block fig,axes = plt.subplots(2,1,figsize=(12,6)) axes[0].hist(timedata/1000,bins=64,color='blue',histtype='step',label='64ms') axes[0].set_title('bins=64') axes[0].set_xlabel('Time/second') axes[0].set_ylabel('Count') axes[0].legend(loc='best') axes[1].hist(timedata/1000,bins=1000,color='blue',histtype='step',normed='True',label='bins=1s') bbhist(timedata/1000,bins='blocks',color='red',histtype='step',normed='True',label='bayesian') axes[1].legend(loc='best') axes[1].set_title('Bayesian block') axes[1].set_xlabel('Time/second') axes[1].set_ylabel('Count Rate($s^{-1}$)') #axes[1].set_ylim([0,0.001]) fig.tight_layout() fig.savefig('%s.png'%trig,dpi=200) # <headingcell level=2> # Channel data # <headingcell level=3>