Assorbimento Pb¶
marco base "didattica" Imparare a leggere un file di testo, vedere la distribuzione di una certa variabile, customizzare la grafica, fare un fit, fare un fit su un sottorange e usare i risultati
File structure: epoch time, adc1, adc2, adc3, i5
...
001524235805 1778 80 64 112
001524235805 2236 81 66 112
001524235805 1651 81 65 112
001524235805 1688 81 66 113
001524235805 1768 81 65 112
...Usual imports:
In [1]:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
In case I want to give the run number as an argument
In [2]:
#import sys
#nrun = int(sys.argv[1]) if len(sys.argv)>1 else 20275 # ternary operator
The read the data file using numpy loadtxt and show histos
In [3]:
datadir = './dati/'
nrun=20275
datafile = datadir + 'run%6.6d.dat' % nrun
print('Opening file: %s' % datafile)
In [4]:
time, adc1, adc2, adc3, i5 = np.loadtxt(datafile, unpack=True)
plt.hist(adc1)
plt.show()
Plot customizing: increase the number of bins
In [5]:
plt.hist(adc1, 500)
plt.show()
Classic line
In [6]:
plt.hist(adc1, 500, histtype='step')
plt.show()
Zooming
In [7]:
plt.hist(adc1, 500, histtype='step', range=(1000,2500))
plt.show()
axis and title (property of the plot)
In [8]:
plt.hist(adc1, 500, histtype='step', range=(1000,2500), edgecolor='black', linewidth=1.2)
plt.title('Titolo', fontsize=18)
plt.xlabel('ADC', fontsize=16)
plt.ylabel('number of events', fontsize=16)
plt.show()
Computing the mean (numpy library), the mean in the same range of the histo, and plot a vertical line
In [9]:
hrange=(1000, 2500) # <-- it's a tuple
plt.hist(adc1, 500, histtype='step', range=hrange, edgecolor='black', linewidth=1.2)
plt.title('Titolo', fontsize=18)
plt.xlabel('ADC', fontsize=16)
plt.ylabel('number of events', fontsize=16)
histomean = np.mean([v for v in adc1 if hrange[0]<v<hrange[1]])
# vertical line
plt.axvline(x=histomean, color='b')
plt.show()
Fit with a gaussian
In [10]:
import scipy.optimize as opt
def gaus(x, A, mu, sigma):
return A*np.exp(-(x-mu)**2/(2*sigma**2))
hrange=(1000, 2500) # <-- it's a tuple
h, bins, pathes = plt.hist(adc1, 500, histtype='step', range=hrange, edgecolor='black', linewidth=1.2, label='data')
# fit
# find subrange for fitting
subrange=(1650, 1900)
newb =[b for b,bb in zip(bins,h) if subrange[0]<b<subrange[1]]
newh =[bb for b,bb in zip(bins,h) if subrange[0]<b<subrange[1]]
optimizedParams, pCov = opt.curve_fit(gaus, newb,newh, p0=[100, 1700, 100])
print(optimizedParams)
plt.plot(newb,gaus(newb,*optimizedParams),'r--',label='fit', linewidth=2)
plt.title('Titolo', fontsize=18)
plt.xlabel('ADC', fontsize=16)
plt.ylabel('number of events', fontsize=16)
histomean = np.mean([v for v in adc1 if hrange[0]<v<hrange[1]])
# vertical line
plt.axvline(x=histomean, color='b', label='mean')
plt.legend()
plt.show()
