def wf_data(wf_path, res_path, n_wf, threshold):
all_amplitude = np.array([])
all_delay = np.array([])
areas = np.array([])
trc = Trc()
allMyData = list(os.listdir(wf_path))
for input_file in allMyData[:n_wf]:
print("Sto facendo il file {}\n".format(input_file))
my_wf = '{}/{}.'.format(wf_path, input_file)
time, ampl, _ = trc.open(my_wf)
ampl = -ampl
_, amplDLED = DLED(time, ampl, 50)
amplDLED = wf_correction(time, amplDLED, threshold)
peaks, _ = find_peaks(amplDLED, height=threshold, prominence=0.01)
peak_timestamp = time[peaks]
peak_amplitude = amplDLED[peaks]
time_distance = peak_timestamp[1:] - peak_timestamp[:-1]
all_delay = np.concatenate((all_delay, time_distance))
all_amplitude = np.concatenate((all_amplitude, peak_amplitude[1:]))
dt = np.abs(time[1:] - time[:-1])
dt = dt.mean()
for i in range(2, len(peaks) - 2):
if peak_timestamp[i] - peak_timestamp[
i - 1] > 100e-9 and peak_timestamp[
i + 1] - peak_timestamp[i] > 100e-9:
#if time_distance[i] > 100e-9 and time_distance[i-1] > 100e-9:
area = compute_area(ampl, peaks[i], dt)
area = np.array([area])
areas = np.concatenate((areas, area))
fampl = open(f'{res_path}/all_ampl.txt', 'w')
fareas = open(f'{res_path}/all_areas.txt', 'w')
fdelay = open(f'{res_path}/all_delay.txt', 'w')
#fdcr = open(f'{res_path}/dark_count_rate.txt', 'w')
for i in areas:
fareas.write(f'{i} \n')
fareas.close()
for i in range(len(all_amplitude)):
fampl.write(f'{all_amplitude[i]} \n')
fdelay.write(f'{all_delay[i]} \n')
fdelay.close()
fampl.close()
return areas, all_amplitude, all_delay
def compute_area(inputfile):
trc = Trc()
time, ampl, _ = trc.open(inputfile)
ampl = -ampl
baseline = ampl[:150].mean()
dt = (np.abs(time[1:] - time[:-1])).mean()
area = ((ampl - baseline) * dt).sum()
return area
def wf_data(wf_path, n_wf):
all_amplitude = np.array([])
all_delay = np.array([])
areas = np.array([])
trc = Trc()
#allMyData = list = os.listdir('C:/Users/39348/Desktop/Thesis_Software/test-gpib/A6_56V')
allMyData = list(os.listdir(wf_path))
for input_file in allMyData[:n_wf]:
print("Sto facendo il file {}\n".format(input_file))
my_wf = '{}/{}.'.format(wf_path, input_file)
time, ampl, d = trc.open(my_wf)
amplDLED = wf_correction(time, ampl)
peaks, _ = find_peaks(-amplDLED, height=0.0085, prominence=0.02)
peak_timestamp = time[peaks]
peak_amplitude = amplDLED[peaks]
time_distance = peak_timestamp[1:] - peak_timestamp[:-1]
all_delay = np.concatenate((all_delay, time_distance))
all_amplitude = np.concatenate((all_amplitude, -peak_amplitude[1:]))
dt = np.abs(time[1:] - time[:-1])
dt = dt.mean()
for i in range(2, len(peaks) - 2):
if peak_timestamp[i] - peak_timestamp[i - 1] > 500e-9:
area = compute_area(ampl, peaks[i], dt)
area = np.array([area])
areas = np.concatenate((areas, area))
return areas, all_amplitude, all_delay
timeDLED = time_delay
amplDLED = ampl_delay - ampl
return timeDLED, amplDLED
def gaus(x, a, mu, sigma):
return a * norm.pdf(x, mu, sigma)
if __name__ == '__main__':
allMyData = list = os.listdir(
'C:/Users/39348/Desktop/Thesis_Software/test-gpib/A6_56V')
trc = Trc()
areas = np.array([])
for input_file in allMyData[:20]:
print("Sto facendo il file {}\n".format(input_file))
wf_path = 'C:/Users/39348/Desktop/Thesis_Software/test-gpib/A6_56V/{}.'.format(
input_file)
time, ampl, d = trc.open(wf_path)
#print(d)
timeDLED, amplDLED = DLED(time, ampl, 20)
peaks, _ = find_peaks(-amplDLED, height=0.0085, prominence=0.02)
peak_timestamp = time[peaks]
peak_amplitude = amplDLED[peaks]
import numpy as np
import os
from matplotlib import pyplot as plt
from readTrc import Trc
trc = Trc()
time, _, _ = trc.open('C:/Users/Marco/Desktop/C1--trigger--00000.trc')
dt = (time[-1] - time[0]) * 40
print(time[-1] - time[0])
#plt.style.use('thesis')
def compute_area(ampl, picco, dt):
baseline = ampl[picco - 200:picco - 100].mean()
ampl[picco - 200:picco + 900] = ampl[picco - 200:picco + 900] - baseline
area = (ampl[picco - 200:picco + 900] * dt).sum()
#plt.figure()
#plt.plot(ampl[picco-200:picco+900])
#plt.plot(ampl[picco-200:picco-100])
#plt.show()
return area
time, a, _ = Trc().open(
'C:/Users/Marco/Desktop/Saturazione/9.5/C1--trigger--00000.trc')
a = -a
'''
timeDLED, amplDLED = DLED(time, ampl, 50)
#plt.figure()
#plt.plot(ampl)
#plt.figure()
#plt.plot(amplDLED)
peaks, _ = find_peaks(amplDLED, height=0.016, prominence=0.005)
amplDLED = wf_correction(timeDLED, amplDLED, 0.016)
peaks, _ = find_peaks(amplDLED, height=0.016, prominence=0.005)
peak_timestamp = time[peaks]
time_distance = peak_timestamp[1:]-peak_timestamp[:-1]
import numpy as np
import os
import argparse
import matplotlib.pyplot as plt
from readTrc import Trc
filepath = 'C:/Users/Marco/Desktop/Saturazione/foot/6'
allMyData = list(os.listdir(filepath))
print(allMyData[0])
trc = Trc()
time, ampl, _ = trc.open(f'{filepath}/{allMyData[0]}')
ampl = -ampl
baseline = ampl[:150].mean()
f = 0.3
a = np.where((ampl-baseline)>f*max(ampl-baseline))
plt.figure()
plt.plot(time, ampl-baseline)
plt.plot(time, (ampl-baseline)[a[0]])
plt.plot(time, np.zeros(len(time))+f*max(ampl-baseline))
plt.show()