Process and Analyse SSPALS

In [1]:

    

# import packages
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import sspals


    

In [2]:

    

# simulate data
dt = 1e-9
t  = np.arange(-1.5E-7, 7.0E-7, dt)
sim = -sspals.sim(t, amp=0.5, eff=0.28)

h_noise = 0.002         # scale of noise to add to hi-gain channel
h_min = -0.05           # range of the hi-gain channel
h_max = 0.03
l_noise = 0.006         # scale of noise to add to low-gain channel
offset = 0.003          # add a random offset to both channels
repeats = 1000

# data
high = []
low = []
for i in range(repeats):
    high.append(np.clip(np.add(sim, 
            np.random.normal(np.random.rand() * offset, h_noise, len(sim))), h_min, h_max))
    low.append(np.add(sim, 
            np.random.normal(np.random.rand() * offset, l_noise, len(sim))))
high = np.array(high)
low = np.array(low)
print(np.shape(high))
print(np.shape(low))


    

    




(1000, 850)
(1000, 850)

In [3]:

    

xdata = np.arange(len(high[0])) * dt

# plot
fig, ax = plt.subplots()
ax.plot(xdata * 1e9, low[0], alpha=0.8)
ax.plot(xdata * 1e9, high[0], alpha=0.8, c='r')

ax.set_xlabel('Time (ns)')
ax.set_ylabel('PMT (V)')

plt.show()


    

In [4]:

    

# combine high and low gain data
chmx = sspals.chmx(high, low)

# analyse
limits=[-1.0E-8, 3.5E-8, 6.5E-7]
df = sspals.sspals(chmx, dt, limits=limits, cfd_threshold=0.04)

# trigger
t0 = np.nanmean(df.t0)

# plot average
fig, ax = plt.subplots()
reps = df.DF.count()
ax.plot((xdata - t0) * 1e9, np.mean(chmx, 0), 
        label='$f_d$ = %.4f $\pm$ %.4f'%(100 * df.DF.mean(), 100 * df.DF.std()/ np.sqrt(reps)))

# show interation limits ABC
for lim in limits:
    ax.axvline(lim*1e9, c='k', ls='--', alpha=0.5)
ax.set_yscale('log')
ax.set_xlim(-100, 700)
ax.set_ylim(1e-5, 1)
ax.set_xlabel('Time (ns)')
ax.set_ylabel('Signal (V)')
ax.legend(loc=1)

plt.show()


    

In [5]:

    

# analysis
df.head()


    

    
Out[5]:







  

    

      

      
t0
      
AC
      
BC
      
DF
    
  
  

    

      
0
      
1.488986e-07
      
9.950969e-09
      
2.494471e-09
      
0.250676
    
    

      
1
      
1.489070e-07
      
1.009521e-08
      
2.626897e-09
      
0.260212
    
    

      
2
      
1.488213e-07
      
9.847591e-09
      
2.427056e-09
      
0.246462
    
    

      
3
      
1.489059e-07
      
9.978136e-09
      
2.654667e-09
      
0.266048
    
    

      
4
      
1.490014e-07
      
9.973022e-09
      
2.606485e-09
      
0.261354
    
  

In [6]:

    

df.hist()
plt.tight_layout()
plt.show()


    

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