26.01514535, 24.01144984, 23.92032753, 25.36948057, 24.36123342,
22.11293807
], [1.20445654, 1.17307301, 1.21097483, 0.98557953, 1.03284394, 1.16066175], [
0.06602095, 0.06831482, 0.10287761, 0.09569007, 0.09342742, 0.09662029
], 1.07043855e-08, 16.1565145, 32.96391684)
p = minimize(L,
rec_to_p(rec),
method='Nelder-Mead',
options={
'disp': True,
'maxfev': 100000
})
rec = p_to_rec(p.x)
m = Model(rec['NQ'][0], rec['T'][0], rec['R'][0], rec['F'][0], rec['Tf'][0],
rec['Ts'][0], rec['St'][0])
s = Sim(rec['NQ'][0], rec['T'][0], 5, rec['R'][0], rec['F'][0], rec['Tf'][0],
rec['Ts'][0], rec['St'][0])
x = np.arange(1000) / 5
fig, ax = plt.subplots(2, 3)
for i in range(len(pmts)):
np.ravel(ax)[i].plot(x,
np.sum(H[:, :, i].T * np.arange(np.shape(H)[0]),
axis=1),
'ko',
label='Data - PMT{}'.format(pmts[i]))
np.ravel(ax)[i].plot(
x,
np.sum(H[:, 0, i]) *
np.sum(m[:, :, i].T * np.arange(np.shape(H)[0]), axis=1),
def L(p):
rec = p_to_rec(p)
global counter
counter += 1
names = ['NQ', 'F', 'Tf', 'Ts', 'T', 'a_spectra', 'R']
for name in names:
if np.any(rec[name] < 0):
return 1e10 * (1 - np.amin(rec[name]))
names = ['F', 'R']
for name in names:
if np.any(rec[name] > 1):
return 1e10 * (np.amax(rec[name]))
if rec['Ts'][0] > 100:
return 1e10 * rec['Ts'][0]
if rec['Ts'][0] < rec['Tf'][0]:
return 1e10 * (rec['Tf'][0] - rec['Ts'][0])
if np.any(rec['St'][0] < 0.5):
return 1e10 * (1 + np.abs(np.amin(rec['St'][0])))
# if np.any(rec['Tf'][0]<1):
# return 1e10*(1+np.abs(np.amin(rec['Tf'][0])))
l = 0
m = Model(rec['NQ'][0], rec['T'][0], rec['R'][0], rec['F'][0],
rec['Tf'][0], rec['Ts'][0], rec['St'][0])
for i in range(len(pmts)):
model = np.sum(H[:, 0, i]) * np.ravel(m[:, :500, i])
if np.any(np.isnan(model)) or np.any(np.isinf(model)):
print('model is nan or inf')
print('NQ=', rec['NQ'][0, i], 'T=', rec['T'][0, i], 'R=',
rec['R'][0], 'F=', rec['F'][0], 'Tf=', rec['Tf'][0], 'Ts=',
rec['Ts'][0], 'St=', rec['St'][0, i])
plt.figure()
plt.plot(np.mean(t.T * np.arange(np.shape(t)[0])), 'k.')
plt.show()
sys.exit()
data = np.ravel(H[:, :500, i])
L = len(model)
for j in range(L):
if model[j] > 0 and data[j] <= 0:
l -= model[j] - data[j]
elif model[j] <= 0 and data[j] > 0:
return 1e10 * (data[j] - model[j])
elif model[j] == 0 and data[j] == 0:
l += 1
else:
l += (data[j] * np.log(model[j]) - data[j] * np.log(data[j]) +
data[j] - model[j])
spectra_rng = np.nonzero(
np.logical_and(PEs > PEs[np.argmax(spectra[i])] - 10,
PEs < PEs[np.argmax(spectra[i])] + 10))[0]
model = rec['a_spectra'][0, i] * poisson.pmf(
PEs, np.sum(m[:, :, i].T * np.arange(np.shape(H)[0])))[spectra_rng]
data = spectra[i][spectra_rng]
L = len(model)
for j in range(L):
if model[j] > 0 and data[j] <= 0:
l -= model[j] - data[j]
elif model[j] <= 0 and data[j] > 0:
return 1e10 * (data[j] - model[j])
elif model[j] == 0 and data[j] == 0:
l += 1
else:
l += (data[j] * np.log(model[j]) - data[j] * np.log(data[j]) +
data[j] - model[j])
# for i in range(len(pmts)-1):
# for j in range(i+1, len(pmts)):
# x=delays[names=='{}_{}'.format(pmts[i], pmts[j])]
# rng=np.nonzero(np.logical_and(x>x[np.argmax(data)]-5, x<x[np.argmax(data)]+5))[0]
# data=delay_hs[names=='{}_{}'.format(pmts[i], pmts[j])]
# model=(x[1]-x[0])*np.sum(data)*np.exp(-0.5*(x[rng]-rec['T'][0,j]+rec['T'][0,i])**2/(rec['St'][0,i]**2+rec['St'][0,j]**2))/np.sqrt(2*np.pi*(rec['St'][0,i]**2+rec['St'][0,j]**2))
# data=data[rng]
# L=len(model)
# for j in range(L):
# if model[j]>0 and data[j]<=0:
# l-=model[j]-data[j]
# elif model[j]<=0 and data[j]>0:
# return 1e10*(data[j]-model[j])
# elif model[j]==0 and data[j]==0:
# l+=1
# else:
# l+=(data[j]*np.log(model[j])-data[j]*np.log(data[j])+data[j]-model[j])
if counter % (len(p) + 1) == 0:
print('!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
print('iteration=', int(counter / (len(p) + 1)), 'fanc=', -l)
print('--------------------------------')
print(rec)
return -l
# rec[0]=([44.10656898, 44.16785627], [2.40187361, 2.05004474], [0.88833944, 1.11088245], [0.04048149, 0.04177063], [5.64966695e-05, 2.44157508e-05], 0.66410346, 31.87824534, 367.91330633, 460.48414159)
rec[0] = ([34.27791569, 34.18452497], [37.55977339, 37.43415619],
[0.60967213, 0.87370292], 0.03910226, 1.59669644, 36.68629443)
p = minimize(L,
rec_to_p(rec),
method='Nelder-Mead',
options={
'disp': True,
'maxfev': 100000
})
rec = p_to_rec(p.x)
P = np.dstack((np.identity(100), np.identity(100)))
m = Model(rec['NQ'][0], rec['T'][0], [0, 0], rec['F'][0], rec['Tf'][0],
rec['Ts'][0], rec['St'][0], [0, 0], P)
s = Sim(rec['NQ'][0], rec['T'][0], 5, [0, 0], rec['F'][0], rec['Tf'][0],
rec['Ts'][0], rec['St'][0], [0, 0], [0, 0], [1e-6, 1e-6], [1e-6, 1e-6],
[0, 0])
x = np.arange(1000) / 5
fig, ((ax1, ax3), (ax2, ax4)) = plt.subplots(2, 2)
ax1.plot(x[:30 * 5],
np.sum(H[:, :, 0].T * np.arange(np.shape(H)[0]), axis=1)[:30 * 5],
'ko',
label='Data - PMT7')
ax1.plot(x[:30 * 5],
np.sum(H[:, 0, 0]) *
np.sum(m[:, :, 0].T * np.arange(np.shape(H)[0]), axis=1)[:30 * 5],
def L(p):
rec = p_to_rec(p)
global counter
counter += 1
names = ['NQ', 'F', 'Tf', 'Ts', 'T']
for name in names:
if np.any(rec[name] < 0):
return 1e10 * (1 - np.amin(rec[name]))
names = ['F']
for name in names:
if np.any(rec[name] > 1):
return 1e10 * (np.amax(rec[name]))
if rec['Ts'][0] > 100:
return 1e10 * rec['Ts'][0]
if rec['Ts'][0] < rec['Tf'][0]:
return 1e10 * (rec['Tf'][0] - rec['Ts'][0])
if np.any(rec['St'][0] < 0.2):
return 1e10 * (1 + np.abs(np.amin(rec['St'][0])))
l = 0
# P=make_P(rec['a0'][0], rec['Spad'][0], rec['Spe'][0], rec['m_pad'][0])
P = np.dstack((np.identity(100), np.identity(100)))
m = Model(rec['NQ'][0], rec['T'][0], [0, 0], rec['F'][0], rec['Tf'][0],
rec['Ts'][0], rec['St'][0], [0, 0], P)
#m_area=model_area(areas, rec['m_pad'][0], rec['a_pad'][0], rec['a_spe'][0], rec['a_dpe'][0], rec['a_trpe'][0], rec['Spad'][0], rec['Spe'][0])
for i in range(len(pmts)):
model = np.sum(H[:, 0, i]) * np.ravel(m[:, :500, i])
if np.any(np.isnan(model)) or np.any(np.isinf(model)):
print('model is nan or inf')
print('NQ=', rec['NQ'][0, i], 'T=', rec['T'][0, i], 'F=',
rec['F'][0, i], 'Tf=', rec['Tf'][0, i], 'Ts=',
rec['Ts'][0, i], 'St=', rec['St'][0, i])
plt.figure()
plt.plot(np.mean(t.T * np.arange(np.shape(t)[0])), 'k.')
plt.show()
sys.exit()
data = np.ravel(H[:, :500, i])
L = len(model)
for j in range(L):
if model[j] > 0 and data[j] <= 0:
l -= model[j] - data[j]
elif model[j] <= 0 and data[j] > 0:
return 1e10 * (data[j] - model[j])
elif model[j] == 0 and data[j] == 0:
l += 1
else:
l += data[j] * np.log(model[j]) - data[j] * np.log(
data[j]) + data[j] - model[j]
# model=np.sum(h_q0[i])*q0_model(n_q0, rec['q0'][0,i])
# data=h_q0[i]
# L=len(model)
# for j in range(L):
# if model[j]>0 and data[j]<=0:
# l-=model[j]-data[j]
# elif model[j]<=0 and data[j]>0:
# return 1e10*(data[j]-model[j])
# elif model[j]==0 and data[j]==0:
# l+=1
# else:
# l+=data[j]*np.log(model[j])-data[j]*np.log(data[j])+data[j]-model[j]
#
# model=rec['a_spec'][0,i]*poisson.pmf(spec_PEs, np.sum(m[:,:,i].T*np.arange(np.shape(H)[0])))
# data=spectrum[spec_PEs, i]
# L=len(model)
# for j in range(L):
# if model[j]>0 and data[j]<=0:
# l-=model[j]-data[j]
# elif model[j]<=0 and data[j]>0:
# return 1e10*(data[j]-model[j])
# elif model[j]==0 and data[j]==0:
# l+=1
# else:
# l+=data[j]*np.log(model[j])-data[j]*np.log(data[j])+data[j]-model[j]
#
# model=m_area[i]
# data=H_areas[i]
# L=len(model)
# for j in range(L):
# if model[j]>0 and data[j]<=0:
# l-=model[j]-data[j]
# elif model[j]<=0 and data[j]>0:
# return 1e10*(data[j]-model[j])
# elif model[j]==0 and data[j]==0:
# l+=1
# else:
# l+=data[j]*np.log(model[j])-data[j]*np.log(data[j])+data[j]-model[j]
#
# model=rec['a_delay'][0]*np.exp(-0.5*(delays[rng_delay]-rec['T'][0,1]+rec['T'][0,0])**2/(rec['St'][0,0]**2+rec['St'][0,1]**2))/np.sqrt(2*np.pi*(rec['St'][0,0]**2+rec['St'][0,1]**2))
# data=delay_h[rng_delay]
#
# L=len(model)
# for j in range(L):
# if model[j]>0 and data[j]<=0:
# l-=model[j]-data[j]
# elif model[j]<=0 and data[j]>0:
# return 1e10*(data[j]-model[j])
# elif model[j]==0 and data[j]==0:
# l+=1
# else:
# l+=data[j]*np.log(model[j])-data[j]*np.log(data[j])+data[j]-model[j]
if counter % (len(p) + 1) == 0:
print('!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
print('iteration=', int(counter / (len(p) + 1)), 'fanc=', -l)
print('--------------------------------')
print(rec)
return -l
l+=(data[j]*np.log(model[j])-data[j]*np.log(data[j])+data[j]-model[j])
if counter%(len(p)+1)==0:
print('!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
print('iteration=', int(counter/(len(p)+1)), 'fanc=',-l)
print('--------------------------------')
print(rec)
return -l
rec[0]=([36.65361232, 27.0674887 , 21.58765364, 35.26543114, 34.68101963, 56.55811891], [20318.89804123, 29398.22812292, 54050.97911597, 37386.39722335, 16245.51120957, 25676.63138817],
[23.90532686, 23.97120507, 23.72761649, 23.46118973, 23.4272549 , 23.73023182], [1.25507068, 1.4145993 , 1.24708723, 0.55778022, 0.86542921, 1.21696164], 0.11440806, 4.51577519, 35.28739035)
print(L(rec_to_p(rec)))
# p=minimize(L, rec_to_p(rec), method='Nelder-Mead', options={'disp':True, 'maxfev':100000})
# rec=p_to_rec(p.x)
m=Model(rec['NQ'][0], rec['T'][0], np.zeros(len(pmts)), rec['F'][0], rec['Tf'][0], rec['Ts'][0], rec['St'][0])
s=Sim(rec['NQ'][0], rec['T'][0], 5, np.zeros(len(pmts)), rec['F'][0], rec['Tf'][0], rec['Ts'][0], rec['St'][0])
x=np.arange(1000)/5
fig, ax=plt.subplots(2,3)
for i in range(len(pmts)):
np.ravel(ax)[i].plot(x, np.sum(H[:,:,i].T*np.arange(np.shape(H)[0]), axis=1), 'ko', label='Data - PMT{}'.format(pmts[i]))
np.ravel(ax)[i].plot(x, np.sum(H[:,0,i])*np.sum(m[:,:,i].T*np.arange(np.shape(H)[0]), axis=1), 'r.-', label='2 exp model', linewidth=3)
np.ravel(ax)[i].plot(x, np.sum(H[:,0,i])*np.sum(s[:,:,i].T*np.arange(np.shape(H)[0]), axis=1), 'g.-', label='2 exp model', linewidth=3)
np.ravel(ax)[i].legend(fontsize=15)
np.ravel(ax)[i].set_xlabel('Time [ns]', fontsize='15')
fig.text(0.04, 0.5, r'$N_{events}\sum_n nH_{ni}$', va='center', rotation='vertical', fontsize=15)
fig, ax=plt.subplots(2,3)
for i in range(len(pmts)):
spectra_rng=np.nonzero(np.logical_and(PEs>PEs[np.argmax(spectra[i])]-10, PEs<PEs[np.argmax(spectra[i])]+10))[0]
model=rec['a_spectra'][0,i]*poisson.pmf(PEs, np.sum(m[:,:,i].T*np.arange(np.shape(H)[0])))[spectra_rng]
rec[0] = ([
35.49302268, 26.17497766, 21.34892126, 34.11857516, 34.25089177,
55.35067421
], [
24.18066808, 24.14013932, 24.37615609, 24.31612566, 24.51001521,
24.57748248
], [1.0884017, 0.88510012, 0.99856744, 1.05147662, 1.09080665, 1.14670428
], [0.06125483, 0.05278206, 0.0909597, 0.09726535, 0.09490963,
0.08954461], 32.79469072)
# print(L(rec_to_p(rec)))
# p=minimize(L, rec_to_p(rec), method='Nelder-Mead', options={'disp':True, 'maxfev':100000})
# rec=p_to_rec(p.x)
m = Model(rec['NQ'][0], rec['T'][0], rec['R'][0], 0, 1000, rec['Ts'][0],
rec['St'][0])
s, GS, G_spec = Sim(rec['NQ'][0], rec['T'][0], 1e-6, rec['R'][0], 0, 1000,
rec['Ts'][0], rec['St'][0])
x = np.arange(200)
fig, ax = plt.subplots(2, 3)
for i in range(len(pmts)):
np.ravel(ax)[i].plot(x,
np.sum(H[:, :, i].T * np.arange(np.shape(H)[0]),
axis=1),
'ko',
label='Data - PMT{}'.format(pmts[i]))
np.ravel(ax)[i].plot(
x,
np.sum(H[:, 0, i]) *
np.sum(m[:, :, i].T * np.arange(np.shape(m)[0]), axis=1),
def L(p):
rec = p_to_rec(p)
global counter
counter += 1
nams = ['NQ', 'Ts', 'T', 'F', 'Tf', 'Ts']
for name in nams:
if np.any(rec[name] < 0):
return 1e10 * (1 - np.amin(rec[name]))
nams = ['F']
for name in nams:
if np.any(rec[name] > 1):
return 1e10 * (np.amax(rec[name]))
if rec['Ts'][0] > 100:
return 1e10 * rec['Ts'][0]
if np.any(rec['St'][0] < 0.5):
return 1e10 * (1 + np.abs(np.amin(rec['St'][0])))
# if np.any(rec['Tf'][0]<1):
# return 1e10*(1+np.abs(np.amin(rec['Tf'][0])))
l = 0
m = Model(rec['NQ'][0], rec['T'][0], np.zeros(len(pmts)), rec['F'][0],
rec['Tf'][0], rec['Ts'][0], rec['St'][0])
for i in range(len(pmts)):
model = np.sum(H[:, 0, i]) * np.ravel(m[:, :100, i])
if np.any(np.isnan(model)) or np.any(np.isinf(model)):
print('model is nan or inf')
print('NQ=', rec['NQ'][0, i], 'T=', rec['T'][0, i], 'R=', 0, 'Ts=',
'F=', rec['F'][0], 'Tf=', rec['Tf'][0], 'Ts=', rec['Ts'][0],
'St=', rec['St'][0, i])
data = np.ravel(H[:, :100, i])
L = len(model)
for j in range(L):
if model[j] > 0 and data[j] <= 0:
l -= model[j] - data[j]
elif model[j] <= 0 and data[j] > 0:
return 1e10 * (data[j] - model[j])
elif model[j] == 0 and data[j] == 0:
l += 1
else:
l += (data[j] * np.log(model[j]) - data[j] * np.log(data[j]) +
data[j] - model[j])
spectra_rng = np.nonzero(
np.logical_and(PEs > PEs[np.argmax(spectra[i])] - 20,
PEs < PEs[np.argmax(spectra[i])] + 20))[0]
model = poisson.pmf(PEs,
np.sum(m[:, :, i].T *
np.arange(np.shape(m)[0])))[spectra_rng]
data = spectra[i][spectra_rng]
model = model / np.amax(model) * np.amax(data)
L = len(model)
for j in range(L):
if model[j] > 0 and data[j] <= 0:
l -= model[j] - data[j]
elif model[j] <= 0 and data[j] > 0:
return 1e10 * (data[j] - model[j])
elif model[j] == 0 and data[j] == 0:
l += 1
else:
l += (data[j] * np.log(model[j]) - data[j] * np.log(data[j]) +
data[j] - model[j])
for i in range(len(pmts) - 1):
for j in range(i + 1, len(pmts)):
x = delays[names == '{}_{}'.format(pmts[i], pmts[j])]
rng = np.nonzero(
np.logical_and(x > x[np.argmax(data)] - 3,
x < x[np.argmax(data)] + 3))[0]
data = delay_hs[names == '{}_{}'.format(pmts[i], pmts[j])]
model = (x[1] - x[0]) * np.exp(
-0.5 * (x[rng] - rec['T'][0, j] + rec['T'][0, i])**2 /
(rec['St'][0, i]**2 + rec['St'][0, j]**2)) / np.sqrt(
2 * np.pi * (rec['St'][0, i]**2 + rec['St'][0, j]**2))
model = model / np.amax(model) * np.amax(data)
data = data[rng]
L = len(model)
for j in range(L):
if model[j] > 0 and data[j] <= 0:
l -= model[j] - data[j]
elif model[j] <= 0 and data[j] > 0:
return 1e10 * (data[j] - model[j])
elif model[j] == 0 and data[j] == 0:
l += 1
else:
l += (data[j] * np.log(model[j]) -
data[j] * np.log(data[j]) + data[j] - model[j])
if counter % (len(p) + 1) == 0:
print('!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
print('iteration=', int(counter / (len(p) + 1)), 'fanc=', -l)
print('--------------------------------')
print(rec)
return -l
rec[0] = ([33.95208843, 34.56622498], [37.81719022,
37.52927457], [0.41393811, 0.61785414],
[4.54280123e-05, 4.63532744e-05], [1.41784125e-03, 2.48391983e-06],
[76503.34258707,
80957.52515861], [14998.18651635,
11345.76354315], [1324.48379597, 1032.34423508],
[407.41079156, 30.88461516], [-0.06053189,
-0.00021648], [0.25647794, 0.22882734],
[4.58697010e-03, 2.72860268e-08], 34.04362439, 354.02590438)
# p=minimize(L, rec_to_p(rec), method='Nelder-Mead', options={'disp':True, 'maxfev':100000})
# rec=p_to_rec(p.x)
print(L(rec_to_p(rec)))
P = make_P(rec['a0'][0], rec['Spad'][0], rec['Spe'][0], rec['m_pad'][0])
m = Model(rec['NQ'][0], rec['T'][0], [0, 0], 0, 1, rec['Ts'][0], rec['St'][0],
rec['q0'][0], P)
m_area = model_area(areas, rec['m_pad'][0], rec['a_pad'][0], rec['a_spe'][0],
rec['a_dpe'][0], rec['a_trpe'][0], rec['Spad'][0],
rec['Spe'][0])
# s=Sim(rec['NQ'][0], rec['T'][0], 5, [0,0], rec['F'][0], rec['Tf'][0], rec['Ts'][0], rec['St'][0], rec['q0'][0], rec['a0'][0], rec['Spad'][0], rec['Spe'][0], rec['m_pad'][0])
s = m
x = np.arange(1000) / 5
fig, ((ax1, ax3), (ax2, ax4)) = plt.subplots(2, 2)
ax1.plot(x[:30 * 5],
np.sum(H[:, :, 0].T * np.arange(np.shape(H)[0]), axis=1)[:30 * 5],
'ko',
label='Data - PMT7')
ax1.plot(x[:30 * 5],
np.sum(H[:, 0, 0]) *
else:
print('f**k')
sys.exit()
return rec
rec1[0] = ([34.17255625, 35.09148282], [39.65578361,
39.30607808], [0.98964095, 1.08379849],
[0, 0], 0.07063164, 0.21911959, 36.64258893, 463.23111438)
rec2[0] = ([34.1231953, 35.08659966], [38.19114816,
37.94501817], [0.97305945, 1.11166723],
[0.0443506,
0.05271929], 0.02677325, 0.21487918, 36.61184292, 466.39540394)
P = np.dstack((np.identity(100), np.identity(100)))
m1 = Model(rec1['NQ'][0], rec1['T'][0], rec1['R'][0], rec1['F'][0],
rec1['Tf'][0], rec1['Ts'][0], rec1['St'][0], [0, 0], P)
s1 = Sim(rec1['NQ'][0], rec1['T'][0], 5, rec1['R'][0], rec1['F'][0],
rec1['Tf'][0], rec1['Ts'][0], rec1['St'][0], [0, 0], [0, 0],
[1e-6, 1e-6], [1e-6, 1e-6], [0, 0])
m2 = Model(rec2['NQ'][0], rec2['T'][0], rec2['R'][0], rec2['F'][0],
rec2['Tf'][0], rec2['Ts'][0], rec2['St'][0], [0, 0], P)
s2 = Sim(rec2['NQ'][0], rec2['T'][0], 5, rec2['R'][0], rec2['F'][0],
rec2['Tf'][0], rec2['Ts'][0], rec2['St'][0], [0, 0], [0, 0],
[1e-6, 1e-6], [1e-6, 1e-6], [0, 0])
x = np.arange(200) / 5
fig, ((ax1, ax3), (ax2, ax4)) = plt.subplots(2, 2, sharex=True)
ax1.title.set_text('Double Exp')