def Recon_WF(wfs, spe, dn, up, h_init):
for wf in wfs:
Recon_wf=np.zeros(len(wf))
Real_Recon_wf=np.zeros(len(wf))
t=[]
blw=np.sqrt(np.mean(wf[:150]**2))
WF=WaveForm(100, blw)
find_hits(WF, wf)
init=sorted(filter(lambda hit: hit.height>h_init, WF.hits), key=lambda hit: hit.init)[0].init
while len(WF.hits)>0:
if len(WF.hits[0].groups)==0:
Recon_wf[WF.hits[0].init:WF.hits[0].fin]+=(wf-Recon_wf)[WF.hits[0].init:WF.hits[0].fin]
else:
i=WF.hits[0].groups[0].maxi
if i<init or (wf-Recon_wf)[i]>0.5*np.amin(spe):
Recon_wf[WF.hits[0].groups[0].left:WF.hits[0].groups[0].right]+=np.array(wf[WF.hits[0].groups[0].left:WF.hits[0].groups[0].right]-
Recon_wf[WF.hits[0].groups[0].left:WF.hits[0].groups[0].right])
N=0
J=-1
else:
recon_wf=np.zeros(1000)
Chi2=1e17
J=i
N=1
for j in range(np.amax((init, WF.hits[0].groups[0].left)), np.amin((WF.hits[0].groups[0].maxi+5,999))):
if (wf-Recon_wf)[j]>0.5*np.amin(spe):
temp=1
else:
if j>np.argmin(spe):
recon_wf[j-np.argmin(spe):]=spe[:len(spe)-(j-np.argmin(spe))]
else:
recon_wf[:len(recon_wf)-(np.argmin(spe)-j)]=spe[np.argmin(spe)-j:]
n=1
chi2=np.mean((recon_wf[j-dn:j-up]-(wf-Recon_wf)[j-dn:j-up])**2)
if chi2<Chi2:
Chi2=chi2
J=j
N=n
if J>np.argmin(spe):
Real_Recon_wf[J-np.argmin(spe):]+=spe[:len(spe)-(J-np.argmin(spe))]
Recon_wf[J-np.argmin(spe):]+=spe[:len(spe)-(J-np.argmin(spe))]
else:
Real_Recon_wf[:len(recon_wf)-(np.argmin(spe)-J)]+=spe[np.argmin(spe)-J:]
Recon_wf[:len(recon_wf)-(np.argmin(spe)-J)]+=spe[np.argmin(spe)-J:]
for i in range(N):
t.append(J)
WF.hits=[]
find_hits(WF, wf-Recon_wf)
yield [Real_Recon_wf, np.sum(((Real_Recon_wf-wf)[init:])**2), np.histogram(t, bins=1000, range=[-0.5, 999.5])[0]]
def hits_from_data(data):
blw = np.sqrt(np.mean(data[:40]**2, axis=0))
for i, wf in enumerate(data.T):
WF = WaveForm(pmts[i], blw[i])
find_hits(WF, wf)
if len(WF.hits):
yield WF.hits[0], pmts[i], blw[i]
def make_event(events, SPEs, N, tau, St):
id=0
while events>0:
Rec=[]
print(events)
wf=np.zeros(1000)
events-=1
t=np.round(np.random.normal(200+np.random.exponential(tau*5, N), St*5, N)).astype(int)
h, bins=np.histogram(t, bins=1000, range=[-0.5, 999.5])
for pe in shoot_pes(SPEs[np.random.randint(0,len(SPEs),N)], t):
wf+=pe
wf=wf-np.median(wf[:150])
blw=np.sqrt(np.mean(wf[:150]**2))
WF=WaveForm(100, blw)
find_hits(WF, wf)
rec=np.recarray(len(WF.hits), dtype=[
('id', 'i8'),
('blw', 'f8'),
('init', 'i8'),
('height', 'f8')])
for i, hit in enumerate(WF.hits):
rec[i]=id, blw, hit.init, hit.height
yield [h, wf, rec]
id+=1
break
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:
def Show_Recon_WF(wf, spe, dn, up, h_init, p):
Recon_wf = np.zeros(len(wf))
Real_Recon_wf = np.zeros(len(wf))
t = []
blw = np.sqrt(np.mean(wf[:150]**2))
WF = WaveForm(100, blw)
find_hits(WF, wf)
if len(list(filter(lambda hit: hit.height > h_init, WF.hits))) == 0:
return Real_Recon_wf, np.sum(((Real_Recon_wf - wf))**2), np.zeros(1000)
init = sorted(filter(lambda hit: hit.height > h_init, WF.hits),
key=lambda hit: hit.init)[0].init
while len(WF.hits) > 0:
if len(WF.hits[0].groups) == 0:
Recon_wf[WF.hits[0].init:WF.hits[0].fin] += (
wf - Recon_wf)[WF.hits[0].init:WF.hits[0].fin]
else:
i = WF.hits[0].groups[0].maxi
if i < init or (wf - Recon_wf)[i] > 0.5 * np.amin(spe):
Recon_wf[WF.hits[0].groups[0].left:WF.hits[0].groups[0].
right] += np.array(
wf[WF.hits[0].groups[0].left:WF.hits[0].groups[0].
right] - Recon_wf[WF.hits[0].groups[0].left:WF.
hits[0].groups[0].right])
N = 0
J = -1
else:
recon_wf = np.zeros(1000)
Chi2 = 1e17
J = i
N = 1
for j in range(np.amax((init, WF.hits[0].groups[0].left)),
np.amin((WF.hits[0].groups[0].maxi + 5, 999))):
if (wf - Recon_wf)[j] > 0.5 * np.amin(spe):
temp = 1
else:
if j > np.argmin(spe):
recon_wf[j -
np.argmin(spe):] = spe[:len(spe) -
(j -
np.argmin(spe))]
else:
recon_wf[:len(recon_wf) -
(np.argmin(spe) -
j)] = spe[np.argmin(spe) - j:]
n = 1
chi2 = np.mean((recon_wf[j - dn:j - up] -
(wf - Recon_wf)[j - dn:j - up])**2)
if chi2 < Chi2:
Chi2 = chi2
J = j
N = n
x = np.arange(1000)
peak = np.zeros(1000)
if J > np.argmin(spe):
Real_Recon_wf[J -
np.argmin(spe):] += spe[:len(spe) -
(J - np.argmin(spe))]
Recon_wf[J - np.argmin(spe):] += spe[:len(spe) -
(J - np.argmin(spe))]
peak[J - np.argmin(spe):] += spe[:len(spe) -
(J - np.argmin(spe))]
if p == 1:
plt.figure()
plt.plot(x, wf, 'k.-', label='wf')
plt.plot(x,
Real_Recon_wf,
'r.-',
label='Real_Recon_wf')
plt.plot(x, Recon_wf, 'g.-', label='Recon_wf')
plt.plot(x, wf - Recon_wf, 'b.-', label='fit')
plt.plot(x, peak, 'c.-', label='peak')
plt.legend()
plt.show()
else:
Real_Recon_wf[:len(recon_wf) -
(np.argmin(spe) - J)] += spe[np.argmin(spe) -
J:]
Recon_wf[:len(recon_wf) -
(np.argmin(spe) - J)] += spe[np.argmin(spe) - J:]
peak[:len(recon_wf) -
(np.argmin(spe) - J)] += spe[np.argmin(spe) - J:]
if p == 1:
plt.figure()
plt.plot(x, wf, 'k.-', label='wf')
plt.plot(x,
Real_Recon_wf,
'r.-',
label='Real_Recon_wf')
plt.plot(x, Recon_wf, 'g.-', label='Recon_wf')
plt.plot(x, wf - Recon_wf, 'b.-', label='fit')
plt.plot(x, peak, 'c.-', label='peak')
plt.legend()
plt.show()
for i in range(N):
t.append(J)
WF.hits = []
find_hits(WF, wf - Recon_wf)
return Real_Recon_wf, np.sqrt(np.mean(
(Real_Recon_wf - wf)[init:]**2)), np.histogram(t,
bins=1000,
range=[-0.5, 999.5])[0]
j = 0
start_time = time.time()
while j < len(rec_hits):
if id % 10 == 0:
print(
source + type +
' In PMT {}, Event number {} ({} sec for event). {} Events were saved'
.format(pmt, id, (time.time() - start_time) / 10, j))
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
data = np.array(Data[2:1002, 1:21])
bl, blw = find_bl(wf)
wf = np.array(wf - bl)
WF = WaveForm(pmt, blw)
find_hits(WF, wf)
for hit in WF.hits:
rec[j] = id, blw, hit.init, hit.height
j += 1
if j == len(rec):
np.savez(path + 'PMT{}/WFs{}to{}'.format(pmt, id0, id - 1),
rec=rec)
id0 = id
j = 0
id += 1
np.savez(path + 'PMT{}/WFs{}to{}'.format(pmt, id0, id), rec=rec[:j - 1])
first_id = 13
id = first_id
start_time = time.time()
N = 100
spectrum = np.zeros((len(pmts), 1000, N))
mean_WF = np.zeros((len(pmts), 1000))
Recon_wf = np.zeros((len(pmts), 1000))
Chi2 = np.zeros(N)
ID = np.zeros(N)
first_pmt = np.zeros(len(pmts))
BLW = np.zeros((len(pmts), N))
x = np.arange(1000)
plt.figure()
for i, [recon_wfs, chi2, recon_Hs, wfs, blw] in enumerate(
Recon_WFs(file, spe, delay, dn, up, 100, height_cut, pmts, first_id)):
if (first_id + i) % 10 == 0:
print('in', path, '. ID=', first_id + i)
WF = WaveForm(100, np.sqrt(np.mean(np.sum(wfs, axis=0)[:40]**2)))
find_hits(WF, np.sum(wfs, axis=0))
init = sorted(WF.hits, key=lambda hit: hit.area)[-1].init
wfs = np.roll(wfs, 100 - init, axis=1)
plt.plot(x, np.sum(wfs, axis=0), '.-')
# plt.plot(x, np.sum(recon_wfs, axis=0), 'r.-')
if i == 2:
break
plt.show()