def L(p):
rec = p_to_rec(p)
global counter
counter += 1
names = [
'NQ', 'Ts', 'T', 'a_delay', 'q0', 'a0', 'a_pad', 'a_spe', 'a_dpe',
'a_trpe', 'Spad', 'Spe'
]
for name in names:
if np.any(rec[name] < 0):
return 1e10 * (1 - np.amin(rec[name]))
names = ['q0', 'a0', 'Spad']
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 np.any(rec['St'][0] < 0.2):
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
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])
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]
spectra_rng = np.nonzero(
np.logical_and(PEs > PEs[np.argmax(spectra[i]) - 5],
PEs < PEs[np.argmax(spectra[i]) + 5]))[0]
model = np.sum(H[:, 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]
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
fig, ((ax1, ax3), (ax2, ax4)) = plt.subplots(2, 2)
ax3.plot(delays, delay_h, 'ko')
ax3.plot(delays[rng_delay],
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)),
'r.-',
linewidth=5)
ax3.set_xlabel('Delay [ns]', fontsize='15')
ax4.plot(n_q0, h_q0[0], 'o', label='PMT{} - data'.format(pmts[0]))
ax4.plot(n_q0, h_q0[1], 'o', label='PMT{} - data'.format(pmts[1]))
ax4.plot(n_q0,
np.sum(h_q0[0]) * q0_model(n_q0, rec['q0'][0, 0]),
'+',
label='PMT{} - model'.format(pmts[0]))
ax4.plot(n_q0,
np.sum(h_q0[1]) * q0_model(n_q0, rec['q0'][0, 1]),
'+',
label='PMT{} - model'.format(pmts[1]))
ax4.set_xlabel('Dark PEs', fontsize='15')
ax3.legend(fontsize=15)
ax4.legend(fontsize=15)
ax4.set_yscale('log')
# plt.subplots_adjust(hspace=0)
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],