<html><head><meta name="color-scheme" content="light dark"></head><body><pre style="word-wrap: break-word; white-space: pre-wrap;">from matplotlib import pyplot
import numpy

def running_mean(x, N):
    # From https://stackoverflow.com/questions/13728392
    cumsum = numpy.cumsum(numpy.insert(x, 0, 0))
    return (cumsum[N:] - cumsum[:-N]) / float(N)

#newFile = open("Scint_adc.txt", "rb")
#newFile = open("NaI_adc.txt", "rb")
newFile = open("pulses.txt", "rb")
line = newFile.read()
newFile.close()
print(len(line),"bytes read in")

time_division_size = 2 # microseconds
frame_time_divisions = 14
frame_points    = 14000    # Scint
frame_points    = 7000    # NaI
frame_points    = 700    # NaI

# Convert to signed integer
data = []
for l in range(len(line)) :
    data.append(int(line[l]))
    if data[l] &gt; 127 :
        data[l] = data[l]-256
adc =[]
N_events = int(len(line)/frame_points)
i_event = 0
for i_event in range(N_events) :
    adc.append([])
    for j_point in range(i_event*frame_points,(i_event+1)*frame_points) :
        adc[i_event].append(data[j_point])
point_to_time = time_division_size*frame_time_divisions/frame_points
times = numpy.arange(0,time_division_size*frame_time_divisions,point_to_time)
print("Number of events: ", len(adc))
pyplot.title("Typical Trigger from NaI data")
pyplot.xlabel("Time (Âµs) ")
pyplot.plot(times, running_mean(adc[1], 1), linestyle="-", marker="None")
# Save plot as a graphic file, if desired.
pyplot.savefig('scope_trace',dpi=300)
pyplot.show()</pre></body></html>