Cartelle di dati

• cartella punto3_old: files che avevano la saturazione negli altri 2
• cartella punto3_new: i punti a 56 e 56.5 ripresi, ma ci sono solo 3 dataset
• cartella punto3: non usare, non ricordo esattamente cosa ci sia

import numpy as np
from matplotlib import pyplot as plt

from scipy.optimize import curve_fit
from scipy.signal import find_peaks
import os

def gauss(x,a,mu,sigma):
    return a * np.exp(-((x-mu)/sigma)**2)

def miaFunzione(fileName):
    print(fileName)
    x,y = np.loadtxt(fileName, unpack=True)
    
    # Scelgo i dati da fittare
    cond = (x > -100) & (x < 100) & (y > 0)
    xData = x[cond]
    yData = y[cond]
    
    
    # Fitto
    popt, pcov = curve_fit(gauss, xData, yData, 
                           sigma = np.sqrt(yData), absolute_sigma = True,
                          p0 = (np.max(yData), xData[np.argmax(yData)], 50) )
    
    
    # Plotto
    fig, ax = plt.subplots()
    ax.set_title(f"{fileName}", fontsize = 16)
    
    ax.plot(x,y, c = "tab:green")
    ax.plot(xData, gauss(xData, *popt), ":k")
    
    ax.set_xlim(-500, 1000)
    ax.set_yscale("log")
    ax.grid()
    
    ax.set_xlabel("Energia [ADC]", fontsize = 14)
    ax.set_ylabel("Entries", fontsize = 14)
    
    
    #plt.show()
    
    
    
    # Calcolo le aree
    myMu = popt[1]
    mySigma = np.abs(popt[2])
    
    inizio = myMu - 3 * mySigma
    fine = myMu + 3 * mySigma
    
    ax.axvline(x = inizio, c = "grey", ls = "--")
    ax.axvline(x = fine, c = "grey", ls = "--")
    

    tmpCond = (x >= inizio) & (x <= fine)
    
    # Integrale manuale
    tmpInt = np.sum(y[tmpCond])   
    
    # Probabilità picco in zero
    A0 = tmpInt
    A = np.sum(y)
    prob0 = A0/A
    #prob0 = tmpInt/np.sum(y)
    lstProb0.append(prob0)
    print(prob0)
    
    
    # Calcolo il DCR con la formula (tau gate = 152 ns)
    mu0 = -np.log(prob0)
    errMu0 = np.sqrt( 1/A0 + 1/A)
    myGate = 152e-9
    errMyGate = 8e-9 # avevamo risoluzione a 8 nani dice giò
    #myDCR = -np.log(prob0) / 152e-9
    #myDCR = mu0 / 152e-9
    myDCR = mu0 / myGate
    lstDCR.append(myDCR)
    print(myDCR)
    
    errDCR = np.sqrt((errMu0/myGate)**2 + (mu0*errMyGate/(myGate**2))**2)
    lstErrDCR.append(errDCR)
    print(errDCR)
    print(mu0, errMu0)

    
    
    plt.show()
    
    

Far girare sia con punto3_old che punto3_new ed osservare che siamo disabili a settare il bias

lstProb0 = []
lstDCR = []
lstErrDCR = []
lstBias = []

for e in os.scandir(r".\punto3_new"):
    if e.name.split(".")[-1] != "txt":  continue
    #if e.name.split("_")[-1] == "old.txt":  continue
    if e.name.split("_")[-2] in ("4", "5"):  continue
    
    miaFunzione(e.path)
    
    lstBias.append(e.name.split("_")[2])
                   

lstProb0 = np.array(lstProb0)
lstDCR = np.array(lstDCR)
lstBias = np.array(lstBias)
                   
    

.\punto3_new\MPH_n_54.5_1_histo.txt
0.9764402648308574
156853.3109525504
20709.29444521686
0.023841703264787666 0.00288689030459137

.\punto3_new\MPH_n_54.5_2_histo.txt
0.976294095974947
157838.224447222
23945.48470251787
0.023991410115977743 0.003413662213549343

.\punto3_new\MPH_n_54.5_3_histo.txt
0.9765987300530333
155785.708757777
24249.9874914809
0.023679427731182104 0.00346891251147318

.\punto3_new\MPH_n_55.5_1_histo.txt
0.9653627816214299
231916.50517518757
26594.707726711396
0.035251308786628514 0.0035914767153030244

.\punto3_new\MPH_n_55.5_2_histo.txt
0.9662816972131559
225657.07406736826
26341.53928034441
0.03429987525823998 0.0035738460681959193

.\punto3_new\MPH_n_55.5_3_histo.txt
0.9661748830514574
226384.36052328045
25888.536612205273
0.03441042279953863 0.0034935205458345247

.\punto3_new\MPH_n_55_1_histo.txt
0.9713299721482446
191375.2716040176
23407.42548631121
0.029089041283810676 0.0032116800580818195

.\punto3_new\MPH_n_55_2_histo.txt
0.9728526924788607
181069.75953187665
99279.32683543464
0.027522603448845252 0.015020772032787993

.\punto3_new\MPH_n_55_3_histo.txt
0.9710229875764681
193454.8476639085
26373.936019838682
0.029405136844914095 0.0036980533416664583

.\punto3_new\MPH_n_56.5_1_histo.txt
0.9566010963801952
291900.01062310743
28096.208347569704
0.04436880161471233 0.003575621216496904

.\punto3_new\MPH_n_56.5_2_histo.txt
0.9552573019050874
301148.3467859819
29482.559693133702
0.04577454871146925 0.0037786649234707293

.\punto3_new\MPH_n_56.5_3_histo.txt
0.9561637232717876
294908.6969824161
28874.223340407174
0.04482612194132725 0.0037008285315100987

.\punto3_new\MPH_n_56_1_histo.txt
0.9602880093799504
266592.30094345973
21734.63436446914
0.04052202974340589 0.0025230191254537264

.\punto3_new\MPH_n_56_2_histo.txt
0.961175284802992
260516.37043564438
27813.87302021651
0.03959848830621795 0.0036783038171498555

.\punto3_new\MPH_n_56_3_histo.txt
0.9610941988068642
261071.40239729753
27034.303672427603
0.03968285316438923 0.003538857338244225

perm = np.argsort(lstBias)
lstBias = lstBias[perm]
lstDCR = lstDCR[perm]
lstProb0 = lstProb0[perm]

matDCR = np.reshape(lstDCR, (int(len(lstDCR)/5), 5)) matBias = np.reshape(lstBias, (int(len(lstDCR)/5), 5))

matDCR =  np.reshape(lstDCR,  (int(len(lstDCR)/3), 3))
matBias = np.reshape(lstBias, (int(len(lstDCR)/3), 3))

matBias

array([['54.5', '54.5', '54.5'],
       ['55', '55', '55'],
       ['55.5', '55.5', '55.5'],
       ['56', '56', '56'],
       ['56.5', '56.5', '56.5']], dtype='<U4')

vectBias = matBias[:,0]
meanDCR = np.mean(matDCR, axis = 1)
stdDCR = np.std(matDCR, axis = 1)

print (meanDCR)
print(matDCR)

[156825.74805252 188633.29293327 227985.97992195 262726.6912588
 295985.68479717]
[[156853.31095255 157838.22444722 155785.70875778]
 [191375.27160402 181069.75953188 193454.84766391]
 [231916.50517519 225657.07406737 226384.36052328]
 [266592.30094346 260516.37043564 261071.4023973 ]
 [291900.01062311 301148.34678598 294908.69698242]]

fig, ax = plt.subplots()

ax.plot(vectBias, meanDCR*1e-3, marker = ".", c = "tab:green", ms = 10, ls = "--", zorder=1, label = "Data")
ax.errorbar(vectBias, meanDCR*1e-3, yerr = stdDCR*1e-3, marker = "", c = "k", ms = 10, ls = "", zorder=2, label = "Errorbar")

ax.set_title("DCR vs Bias", fontsize = 16)
ax.set_xlabel("V bias [V]", fontsize = 14)
ax.set_ylabel("DCR [kHz]", fontsize = 14)


ax.grid()
ax.legend(fontsize = 14)

plt.show()

Copio le celle sopra, con la directory sbagliata.

Avevamo sbagliato l'ultimo bias

lstProb0 = []
lstDCR = []
lstErrDCR = []
lstBias = []

for e in os.scandir(r".\punto3_old"):
    if e.name.split(".")[-1] != "txt":  continue
    #if e.name.split("_")[-1] == "old.txt":  continue
    if e.name.split("_")[-2] in ("4", "5"):  continue
    
    miaFunzione(e.path)
    
    lstBias.append(e.name.split("_")[2])
                   

lstProb0 = np.array(lstProb0)
lstDCR = np.array(lstDCR)
lstBias = np.array(lstBias)
                   
    

.\punto3_old\MPH_n_54.5_1_histo.txt
0.9764402648308574
156853.3109525504
20709.29444521686
0.023841703264787666 0.00288689030459137

.\punto3_old\MPH_n_54.5_2_histo.txt
0.976294095974947
157838.224447222
23945.48470251787
0.023991410115977743 0.003413662213549343

.\punto3_old\MPH_n_54.5_3_histo.txt
0.9765987300530333
155785.708757777
24249.9874914809
0.023679427731182104 0.00346891251147318

.\punto3_old\MPH_n_55.5_1_histo.txt
0.9653627816214299
231916.50517518757
26594.707726711396
0.035251308786628514 0.0035914767153030244

.\punto3_old\MPH_n_55.5_2_histo.txt
0.9662816972131559
225657.07406736826
26341.53928034441
0.03429987525823998 0.0035738460681959193

.\punto3_old\MPH_n_55.5_3_histo.txt
0.9661748830514574
226384.36052328045
25888.536612205273
0.03441042279953863 0.0034935205458345247

.\punto3_old\MPH_n_55_1_histo.txt
0.9713299721482446
191375.2716040176
23407.42548631121
0.029089041283810676 0.0032116800580818195

.\punto3_old\MPH_n_55_2_histo.txt
0.9728526924788607
181069.75953187665
99279.32683543464
0.027522603448845252 0.015020772032787993

.\punto3_old\MPH_n_55_3_histo.txt
0.9710229875764681
193454.8476639085
26373.936019838682
0.029405136844914095 0.0036980533416664583

.\punto3_old\MPH_n_56.5_1_histo.txt
0.9615038115038115
258268.09104362433
24936.857289809675
0.0392567498386309 0.003177765821830574

.\punto3_old\MPH_n_56.5_2_histo.txt
0.9622225124053277
253352.32359174572
27460.758701039813
0.03850955318594535 0.003648914534806858

.\punto3_old\MPH_n_56.5_3_histo.txt
0.9618264439337998
256060.8967355776
27413.011281266037
0.0389212563038078 0.003628462046458216

.\punto3_old\MPH_n_56_1_histo.txt
0.961904325753016
255528.20224637567
27539.409156406666
0.038840286741449105 0.0036528968652916583

.\punto3_old\MPH_n_56_2_histo.txt
0.9613141285395025
259566.096660209
26996.401977985508
0.03945404669235177 0.0035392591884458786

.\punto3_old\MPH_n_56_3_histo.txt
0.96222810111699
253314.11233329633
26800.504170990265
0.03850374507466105 0.003533848721982756

perm = np.argsort(lstBias)
lstBias = lstBias[perm]
lstDCR = lstDCR[perm]
lstProb0 = lstProb0[perm]

matDCR = np.reshape(lstDCR, (int(len(lstDCR)/5), 5)) matBias = np.reshape(lstBias, (int(len(lstDCR)/5), 5))

matDCR =  np.reshape(lstDCR,  (int(len(lstDCR)/3), 3))
matBias = np.reshape(lstBias, (int(len(lstDCR)/3), 3))

matBias

array([['54.5', '54.5', '54.5'],
       ['55', '55', '55'],
       ['55.5', '55.5', '55.5'],
       ['56', '56', '56'],
       ['56.5', '56.5', '56.5']], dtype='<U4')

vectBias = matBias[:,0]
meanDCR = np.mean(matDCR, axis = 1)
stdDCR = np.std(matDCR, axis = 1)

print (meanDCR)
print(matDCR)

[156825.74805252 188633.29293327 227985.97992195 256136.13707996
 255893.77045698]
[[156853.31095255 157838.22444722 155785.70875778]
 [191375.27160402 181069.75953188 193454.84766391]
 [231916.50517519 225657.07406737 226384.36052328]
 [255528.20224638 259566.09666021 253314.1123333 ]
 [258268.09104362 253352.32359175 256060.89673558]]

fig, ax = plt.subplots()

ax.plot(vectBias, meanDCR*1e-3, marker = ".", c = "tab:green", ms = 10, ls = "--", zorder=1, label = "Data")
ax.errorbar(vectBias, meanDCR*1e-3, yerr = stdDCR*1e-3, marker = "", c = "k", ms = 10, ls = "", zorder=2, label = "Errorbar")

ax.set_title("DCR vs Bias", fontsize = 16)
ax.set_xlabel("V bias [V]", fontsize = 14)
ax.set_ylabel("DCR [kHz]", fontsize = 14)


ax.grid()
ax.legend(fontsize = 14)

plt.show()