def make_l(i, l, G, H, Spectrum, Spectra, Bins, bins, Abins, left, right, x1,
x2, gamma, Q, T, St, W, std, Nbg, nLXe, sigma_smr, mu, R, a, F, Tf,
Ts):
np.random.seed(int(i * time.time() % 2**32))
spectrum, spectra, g, h = Sim_fit(x1, x2, left, right, gamma, Q, T, St, W,
std, nLXe, sigma_smr, mu, R, a, F, Tf,
Ts, binsSpec, bins)
if np.any(spectrum < 0):
l.value = 1e20 * (1 - np.amin(spectrum))
else:
data = Spectrum
model = Nbg * BGspectrum + (np.sum(Spectrum) -
Nbg * np.sum(BGspectrum)) * spectrum
l.value = -np.sum(data * np.log((model + 1e-10) /
(data + 1e-10)) + data - model)
data = np.ravel(Spectra)
model = np.ravel(Nbg * BGspectra +
(np.sum(Spectra, axis=0) -
Nbg * np.sum(BGspectra, axis=0)) * spectra)
l.value = -np.sum(data * np.log((model + 1e-10) /
(data + 1e-10)) + data - model)
data = np.ravel(G)
model = np.ravel(Nbg * BGG +
(np.sum(G[:, 0]) - Nbg * np.sum(BGG[:, 0])) * g)
l.value = -np.sum(data * np.log((model + 1e-10) /
(data + 1e-10)) + data - model)
data = np.ravel(H)
model = np.ravel(
Nbg * BGH +
(np.sum(H[:, 0], axis=0) - Nbg * np.sum(BGH[:, 0], axis=0)) * h)
l.value = -np.sum(data * np.log((model + 1e-10) /
(data + 1e-10)) + data - model)
def make_l(l, H, spectra, cov, left, right, Xcov, x1, x2, gamma, Q, T, St, mu, W, F, Tf, Ts, R, a):
S, Sspectra, Scov=Sim_fit(x1, x2, left, right, gamma, Q, T, St, mu, W, F, Tf, Ts, R, a, np.arange(np.shape(spectra)[0]))
if np.any(S<0):
print('S<0', S[S<0])
print(Q, T, St, mu, W, F, Tf, Ts, R, a)
l.value=-1e10*(1-np.amin(S))
else:
# model=np.sum(H[:,0,0])*np.ravel(S)
# data=np.ravel(H[:,:100,:])
model=np.sum(H[:,0,0])*np.ravel(np.sum(S, axis=-1))
data=np.ravel(np.sum(H[:,:100,:], axis=-1))
l.value+=np.sum(data*np.log((model+1e-10)/(data+1e-10))+data-model)
def make_l(i, l, H, spectrum, spectra, T, Bins, bins, cov, left, right, x1, x2, gamma, Q, W, nLXe, mu):
np.random.seed(int(i*time.time()%2**32))
Sspectrum, Sspectra, Scov=Sim_fit(x1, x2, left, right, gamma, Q, W, nLXe, mu, Bins, bins)
if np.any(Sspectrum<0):
l.value=1e10*(1-np.amin(Sspectrum))
else:
data=spectrum
model=T/TBG*BGspectrum+(np.sum(spectrum)-T/TBG*np.sum(BGspectrum))*Sspectrum
l.value=-np.sum(data*np.log((model+1e-10)/(data+1e-10))+data-model)
data=np.ravel(spectra)
model=np.ravel(T/TBG*BGspectra+(np.sum(spectra, axis=0)-T/TBG*np.sum(BGspectra, axis=0))*Sspectra)
l.value=-np.sum(data*np.log((model+1e-10)/(data+1e-10))+data-model)
def make_l(i, l, SAreas, H, spectrum, spectra, T, Bins, bins, Abins, left, right, x1, x2, gamma, Q, Sa, W, std, Nbg):
np.random.seed(int(i*time.time()%2**32))
Sspectrum, Sspectra, Sareas=Sim_fit(x1, x2, left, right, gamma, Q, Sa, W, std, binsSpec, bins, Abins)
if np.any(Sspectrum<0):
l.value=1e20*(1-np.amin(Sspectrum))
else:
data=spectrum
model=Nbg*BGspectrum+(np.sum(spectrum)-Nbg*np.sum(BGspectrum))*Sspectrum
l.value=-np.sum(data*np.log((model+1e-10)/(data+1e-10))+data-model)
data=np.ravel(spectra)
model=np.ravel(Nbg*BGspectra+(np.sum(spectra, axis=0)-Nbg*np.sum(BGspectra, axis=0))*Sspectra)
l.value=-np.sum(data*np.log((model+1e-10)/(data+1e-10))+data-model)
SAreas+=np.ravel(Sareas.T)
def make_l(H, spectra, bins, cov, left, right, Xcov, x1, x2, gamma, Q, w, mu,
N):
Sspectra = Sim_fit(x1, x2, left, right, gamma, Q, w, mu, bins)
if np.any(Sspectra < 0):
return 1e10 * (1 - np.amin(Sspectra))
else:
# model=np.sum(H[:,0,0])*np.ravel(S)
# data=np.ravel(H[:,:100,:])
# model=np.sum(H[:,0,0])*np.ravel(np.sum(S, axis=-1))
# data=np.ravel(np.sum(H[:,:100,:], axis=-1))
# l.value+=np.sum(data*np.log((model+1e-10)/(data+1e-10))+data-model)
data = np.ravel(spectra)
model = N * np.ravel(Sspectra)
return -np.sum(data * np.log((model + 1e-10) /
(data + 1e-10)) + data - model)
def make_l(l, H, spectra, cov, left, right, Xcov, x1, x2, gamma, Q, N):
Sspectra = Sim_fit(x1, x2, left, right, gamma, Q, 13.7,
np.arange(np.shape(spectra)[0]))
if np.any(Sspectra < 0):
l.value = -1e10 * (1 - np.amin(Sspectra))
else:
# model=np.sum(H[:,0,0])*np.ravel(S)
# data=np.ravel(H[:,:100,:])
# model=np.sum(H[:,0,0])*np.ravel(np.sum(S, axis=-1))
# data=np.ravel(np.sum(H[:,:100,:], axis=-1))
# l.value+=np.sum(data*np.log((model+1e-10)/(data+1e-10))+data-model)
spectra = rebin_spectra(spectra)[1]
Sspectra = rebin_spectra(Sspectra)[1]
data = np.ravel(spectra)
model = N * np.ravel(Sspectra)
l.value += np.sum(data * np.log((model + 1e-10) / (data + 1e-10)) +
data - model)
def make_l(l, H, spectra, cov, left, right, Xcov, x1, x2, gamma, Q, T, St, mu,
W, F, Tf, Ts, R, a, dl):
S, Sspectra, Scov = Sim_fit(x1, x2, left, right, gamma, Q, T, St, mu, W, F,
Tf, Ts, R, a, dl,
np.arange(np.shape(spectra)[0]))
if np.any(S < 0):
l.value = -1e10 * (1 - np.amin(S))
else:
model = np.sum(H[:, 0, 0]) * np.ravel(S)
data = np.ravel(H[:, :100, :])
l.value += np.sum(data * np.log((model + 1e-10) / (data + 1e-10)) +
data - model)
data = np.ravel(spectra)
model = np.sum(H[:, 0, 0]) * np.ravel(Sspectra)
l.value += np.sum(data * np.log((model + 1e-10) / (data + 1e-10)) +
data - model)
model = np.sum(H[:, 0, 0]) * np.ravel(Scov)
data = np.ravel(cov)
l.value += np.sum(data * np.log((model + 1e-10) / (data + 1e-10)) +
data - model)
def make_l(l, H, spectra, cov, left, right, Xcov, PEbins, x1, x2, gamma, Q, T,
St, mu, W, F, Tf, Ts, R, a):
S, Sspectra, Scov = Sim_fit(x1, x2, left, right, gamma, Q, T, St, mu, W, F,
Tf, Ts, R, a, PEbins)
if np.any(S < 0):
print('S<0')
l.value = -1e10 * (1 - np.amin(S))
else:
model = np.sum(H[:, 0, 0]) * np.ravel(S)
data = np.ravel(H[:, :, :])
l.value += np.sum(data * np.log((model + 1e-10) / (data + 1e-10)) +
data - model)
data = np.ravel(spectra)
model = np.sum(H[:, 0, 0]) * np.ravel(Sspectra)
l.value += np.sum(data * np.log((model + 1e-10) / (data + 1e-10)) +
data - model)
model = np.sum(H[:, 0, 0]) * np.ravel(Scov)
data = np.ravel(cov)
l.value += np.sum(data * np.log((model + 1e-10) / (data + 1e-10)) +
data - model)
x2 = 1
T = 1564823506349 - 1564820774226
p = ([
0.1025416, 0.22955419, 0.12410873, 0.13288353, 0.1176724, 0.20605295,
17.63976215, 0.1237789, 1.73823515, 0.88490154
])
Q, W, mu, nLXe, sigma_smr = make_glob_array(p)
Sspectrum = np.zeros((10, len(binsSpec) - 1))
Sspectra = np.zeros((10, len(bins) - 1, 6))
fig, ax = plt.subplots(3, 5)
for i in range(10):
print(i)
s, ss, Scov = Sim_fit(x1, x2, left, right, gamma, Q, W, mu, nLXe,
sigma_smr, binsSpec, bins)
Sspectrum[i] = T / TBG * BGspectrum + (np.sum(spectrum) -
T / TBG * np.sum(BGspectrum)) * s
Sspectra[i] = T / TBG * BGspectra + (
np.sum(spectra, axis=0) - T / TBG * np.sum(BGspectra, axis=0)) * ss
for j in range(15):
np.ravel(ax)[j].plot(i, Scov[j], 'ko')
j = 0
for i in range(5):
for k in range(i + 1, 6):
np.ravel(ax)[j].axhline(cov[j],
0,
1,
color='r',
label='Cov\n PMT{}-PMT{}'.format(i, k),
gamma = 122
elif source == 'Cs137':
gamma = 662
Rec = np.recarray(1, dtype=[
('Q', 'f8', len(pmts)),
])
p = [
0.23744822, 0.19335455, 0.27713303, 0.2391398, 0.2424144, 0.27420061,
14.68521394, 0.93384985
]
Q, W, mu = make_glob_array(p)
Sspectra = Sim_fit(x1, x2, left, right, gamma, Q[:], W[0], mu[0], bins)
# PEs, spectra=rebin_spectra(spectra)
# PEs, Sspectra=rebin_spectra(Sspectra)
fig, ax = plt.subplots(2, 3)
for i in range(len(pmts)):
np.ravel(ax)[i].bar(0.5 * (bins[1:] + bins[:-1]),
spectra[:, i],
linewidth=5,
width=bins[1:] - bins[:-1],
align='center',
label='spectrum - PMT{}'.format(pmts[i]),
alpha=0.5)
np.ravel(ax)[i].plot(0.5 * (bins[1:] + bins[:-1]),
N * Sspectra[:, i],
if source=='Co57':
gamma=122
elif source=='Cs137':
gamma=662
p=([0.19838883, 0.13568442, 0.10828627, 0.17653182, 0.17463287, 0.28635258,
1.39186269, 1.30583897, 14.15082216])
Q, nLXe, mu, W=make_glob_array(p)
Sspectrum=np.zeros((10, len(binsSpec)-1))
Sspectra=np.zeros((10, len(bins)-1, 6))
fig, ax=plt.subplots(3,5)
for i in range(10):
print(i)
s, ss, Scov=Sim_fit(x1, x2, left, right, gamma, Q, 13.7, nLXe, mu, binsSpec, bins)
Sspectrum[i]=T/TBG*BGspectrum+(np.sum(spectrum)-T/TBG*np.sum(BGspectrum))*s
Sspectra[i]=T/TBG*BGspectra+(np.sum(spectra, axis=0)-T/TBG*np.sum(BGspectra, axis=0))*ss
for j in range(15):
np.ravel(ax)[j].plot(i, Scov[j], 'ko')
j=0
for i in range(5):
for k in range(i+1,6):
np.ravel(ax)[j].axhline(cov[j], 0,1, color='r', label='Cov\n PMT{}-PMT{}'.format(i, k), linewidth=5)
np.ravel(ax)[j].legend(fontsize=15)
# np.ravel(ax)[j].set_ylim(-70, 40)
j+=1
plt.figure()
x1 = 1
x2 = 0
elif type == '':
x1 = 0
x2 = 1
if source == 'Co57':
gamma = 122
elif source == 'Cs137':
gamma = 662
Rec = np.recarray(1, dtype=[
('Q', 'f8', len(pmts)),
])
p = np.array([
0.22863552, 0.26201005, 0.26584989, 0.20042843, 0.19058325, 0.25078971
]) * 0.9
Q = make_glob_array(p)
Sspectrum = Sim_fit(x1, x2, left, right, gamma, Q, 13.7, bins)
plt.figure()
plt.bar(0.5 * (bins[1:] + bins[:-1]), spectrum, width=bins[1:] - bins[:-1])
plt.plot(0.5 * (bins[1:] + bins[:-1]), Sspectrum * N, 'k.')
plt.errorbar(0.5 * (bins[1:] + bins[:-1]),
Sspectrum * N,
N * np.sqrt(Sspectrum / 1000),
fmt='k.')
plt.show()
x1 = 0
x2 = 1
if source == 'Co57':
gamma = 122
elif source == 'Cs137':
gamma = 662
Rec = np.recarray(1, dtype=[
('Q', 'f8', len(pmts)),
])
p = [0.22, 0.19041186, 0.22063317, 0.20403208, 0.20959739, 0.24661499]
Q = make_glob_array(p)
Sspectra = Sim_fit(x1, x2, left, right, gamma, Q[:], 13.7,
np.arange(np.shape(spectra)[0]))
PEs, spectra = rebin_spectra(spectra)
PEs, Sspectra = rebin_spectra(Sspectra)
fig, ax = plt.subplots(2, 3)
for i in range(len(pmts)):
np.ravel(ax)[i].bar(PEs,
spectra[:, i],
linewidth=5,
width=PEs[1] - PEs[0],
align='edge',
label='spectrum - PMT{}'.format(pmts[i]))
np.ravel(ax)[i].plot(PEs + 0.5 * (PEs[1] - PEs[0]),
N * Sspectra[:, i],
'g.',
0.19672198, 0.13481802, 0.10158334, 0.16812452, 0.14911151, 0.25114549,
0.38766503, 0.6670629, 0.83024831, 0.62834315, 1.22890051, 0.79467679,
32.39085721, 0.10672545
])
N = 10
Q, Sa, W, g = make_glob_array(p)
Sspectrum = np.zeros((N, len(binsSpec) - 1))
Sspectra = np.zeros((N, len(bins) - 1, 6))
# fig, ax=plt.subplots(3,5)
for i in range(N):
l = 0
#l=L(np.array(p), -100, -100, np.zeros((1, len(p))), [], 1000)
print(i, l)
s, ss, Scov = Sim_fit(x1, x2, left, right, gamma, Q, W, Sa, g, binsSpec,
bins)
Sspectrum[i] = T / TBG * BGspectrum + (np.sum(spectrum) -
T / TBG * np.sum(BGspectrum)) * s
Sspectra[i] = T / TBG * BGspectra + (
np.sum(spectra, axis=0) - T / TBG * np.sum(BGspectra, axis=0)) * ss
# for j in range(15):
# np.ravel(ax)[j].plot(i, Scov[j], 'ko')
# j=0
# for i in range(5):
# for k in range(i+1,6):
# np.ravel(ax)[j].axhline(cov[j], 0,1, color='r', label='Cov\n PMT{}-PMT{}'.format(i, k), linewidth=5)
# np.ravel(ax)[j].legend(fontsize=15)
# j+=1
plt.figure()
1.71385413e-01, 1.75494553e-01, 1.10843989e+00, 7.60654309e-01,
4.98808286e-01, 6.27130735e-01, 9.39779196e-01, 8.96174105e-01,
4.77687664e-01, 2.80100432e-01, 3.90831974e+01, 6.87363235e+02
])
N = 10
Q, Sa, W, std, Nbg = make_glob_array(p)
Sspectrum = np.zeros((N, len(binsSpec) - 1))
Sspectra = np.zeros((N, len(bins) - 1, 6))
SAreas = np.zeros((N, len(Q), len(Abins[0]) - 1))
# fig, ax=plt.subplots(3,5)
for i in range(N):
l = 0
# l=L(np.array(p), -100, -100, np.zeros((1, len(p))), [], 1000)
print(i, l)
s, ss, sa = Sim_fit(x1, x2, left, right, gamma, Q, Sa, W, std, binsSpec,
bins, Abins)
Sspectrum[i] = Nbg[I] * BGspectrum + (np.sum(spectrum) -
Nbg[I] * np.sum(BGspectrum)) * s
Sspectra[i] = Nbg[I] * BGspectra + (
np.sum(spectra, axis=0) - Nbg[I] * np.sum(BGspectra, axis=0)) * ss
SAreas[i] = (np.sum(Areas, axis=1) * sa).T
plt.figure()
plt.bar(0.5 * (binsSpec[1:] + binsSpec[:-1]),
spectrum,
width=binsSpec[1:] - binsSpec[:-1],
label='data')
plt.bar(0.5 * (binsSpec[1:] + binsSpec[:-1]),
BGspectrum * Nbg[I],
width=binsSpec[1:] - binsSpec[:-1],
label='Background',
