Data = np.reshape(Data, (PMT_num + 4, time_samples + 2)).T
trig = np.argmin(Data[2:1002, 0])
H = np.zeros(200)
for i, pmt in enumerate(pmts):
wf = Data[2:1002, chns[i]]
wf = wf - np.median(wf[:Init])
blw = np.sqrt(np.mean(wf[:Init]**2))
wf -= BL[i]
for k in range(np.argmin(wf)):
if np.all(wf[k:k + 20] < -blw):
rec[j]['init_wf'][i] = k
break
#if pmts[i]<14:
wf = np.roll(wf, int(np.round(delays[i] * 5)))
waveform = WaveForm(blw)
h, recon_wf = Recon_wf(waveform, wf, height_cuts[i], rise_time_cuts[i],
spes[i], Init)
H += h
chi2 = np.sqrt(np.sum((wf[Init:] - recon_wf[Init:])**2))
if blw < 10 and rec[j]['init_wf'][i] > 20 and chi2 < 500:
WFs[i] += wf
recon_WFs[i] += recon_wf
rec[j]['area'] = -np.sum(wf[Init:])
rec[j]['blw'][i] = blw
rec[j]['id'] = id
rec[j]['chi2'][i] = chi2
rec[j]['h'][:, i] = h
if len(np.nonzero(H > 0)[0]) == 0:
init = -1
else:
print(path, pmts)
start_time = time.time()
Data = np.fromfile(file, np.float32, (PMT_num + 4) * (time_samples + 2))
if len(Data) < (PMT_num + 4) * (time_samples + 2):
break
Data = np.reshape(Data, (PMT_num + 4, time_samples + 2)).T
trig = find_trig(Data[2:1002, PMT_num + 2])
H = np.zeros(1000)
for i, pmt in enumerate(pmts):
wf = Data[2:1002, chns[np.nonzero(pmts == pmt)[0][0]]]
wf = np.roll(wf, 100 - trig)
wf = wf - np.median(wf[:Init])
blw = np.sqrt(np.mean(wf[:Init]**2))
wf += BL[i]
waveform = WaveForm(blw)
h, recon_wf = Recon_wf(waveform, wf, height_cuts[i], dh3_cuts[i],
spk_cuts[i], spes[i], 3)
rec[j]['chi2'][i] = np.sqrt(np.sum((wf - recon_wf)**2))
rec[j]['blw'][i] = blw
rec[j]['id'] = id
rec[j]['h'][:, i] = h
if len(np.nonzero(wf[:np.argmin(wf)] > 0.1 * np.amin(wf))[0]) > 0:
rec[j]['init10'][i] = np.amax(
np.nonzero(wf[:np.argmin(wf)] > 0.1 * np.amin(wf))[0])
else:
rec[j]['init10'][i] = -1
# x=np.arange(1000)
# plt.figure()
# plt.title(pmts[i])
# plt.plot(x, wf, 'k.')
# plt.plot(x, recon_wf, 'r--')
break
Data = np.reshape(Data, (PMT_num + 4, time_samples + 2)).T
trig = np.argmin(Data[2:1002, 0])
H = np.zeros(1000)
for i, pmt in enumerate(pmts):
wf = Data[2:1002, chns[i]]
wf = wf - np.median(wf[:left])
blw = np.sqrt(np.mean(wf[:left]**2))
wf -= BL[i]
for k in range(np.argmin(wf)):
if np.all(wf[k:k + 20] < -blw):
rec[j]['init_wf'][i] = k
break
waveform = WaveForm(blw)
h, recon_wf, Areas, DCAreas, Abins = Recon_wf(
waveform, wf, height_cuts[i], dh3_cut[i],
spes[i] * SPEcorrection[i], Init, left, right, th[i])
H += h
chi2 = np.sqrt(np.sum((wf[Init:] - recon_wf[Init:])**2))
if blw < 10 and rec[j]['init_wf'][i] > 20 and chi2 < 500:
WFs[i] += wf
recon_WFs[i] += recon_wf
rec[j]['Areas'][i] = Areas
rec[j]['DCAreas'][i] = DCAreas
rec[j]['blw'][i] = blw
rec[j]['id'] = id
rec[j]['chi2'][i] = chi2
rec[j]['h'][:, i] = h
if len(np.nonzero(H > 0)[0]) == 0:
start_time = time.time()
path = '/home/gerak/Desktop/DireXeno/190803/pulser/'
file = open(path + 'out.DXD', 'rb')
Data = np.fromfile(file, np.float32, (PMT_num + 4) * (time_samples + 2) * id)
j = 0
Data = np.fromfile(file, np.float32, (PMT_num + 4) * (time_samples + 2))
Data = np.reshape(Data, (PMT_num + 4, time_samples + 2)).T
trig = np.argmin(Data[2:1002, 0])
for i, pmt in enumerate(pmts):
wf = Data[2:1002, chns[i]]
wf = wf - np.median(wf[:left])
blw = np.sqrt(np.mean(wf[:left]**2))
wf -= BL[i]
waveform = WaveForm(blw)
h, recon_wf, Areas, DCAreas, Abins = Recon_wf(waveform, wf, height_cuts[i],
dh3_cut[i], spes[i], Init,
left, right, 0.5)
t = np.arange(1000)
fig, ax = plt.subplots(1)
fig.suptitle('..', fontsize=25)
np.ravel(ax)[0].plot(t, wf, 'ko', label='PMT{}'.format(pmts[i]))
np.ravel(ax)[0].plot(t, spes[i], 'b.-', label='PMT{}'.format(pmts[i]))
np.ravel(ax)[0].plot(t, recon_wf, 'r.-')
np.ravel(ax)[0].plot(t, wf - recon_wf, 'g--')
np.ravel(ax)[0].legend(fontsize=15)
plt.show()