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>
