def Sim_fit(x1, x2, left, right, gamma, Q, T, St, mu, W, F, Tf, Ts, R, a, eta,
PEs):
f = np.sum(make_recomb(np.arange(1000 * 20) / 100, a,
eta).reshape(1000, 20),
axis=1)
i = 2
while 1 - np.sum(f) > 1e-4:
f = np.sum(make_recomb(np.arange(i * 1000 * 20) / 100, a,
eta).reshape(i * 1000, 20),
axis=1)
i += 1
if np.sum(f) - 1 > 1e-4:
print('In recombenation model:')
print('sum(f):', np.sum(f))
sys.exit()
else:
f = f / np.sum(f)
N_events = 10000
d = np.zeros((N_events, 100, len(Q)))
H = np.zeros((50, 100, len(Q)))
spectra = np.zeros((len(PEs), len(Q)))
cov = np.zeros((11, 15))
v = make_v(N_events, mu, x1, x2)
N = np.random.poisson(1000 * gamma / W, N_events)
p = multiprocessing.Pool(processes=2)
d = p.map(make_d, make_iter(N, Q, T, St, F, Tf, Ts, R, a, eta, f, v))
p.close()
p.join()
# d=make_d_loop(N, Q, T, St, F, Tf, Ts, R, a, eta, f, v)
s = np.sum(np.sum(d, axis=1), axis=1)
ind = np.nonzero(np.logical_and(s > left, s < right))[0]
if len(ind) == 0:
w = np.abs(np.mean(s) - (left + right) / 2)
return H - w, spectra - w, cov - w
d = np.array(d)[ind]
for i in range(len(Q)):
spectra[:, i] = np.histogram(np.sum(d[:, :, i], axis=1),
bins=np.arange(len(PEs) + 1) - 0.5)[0]
k = 0
for i in range(5):
Si = np.sum(d[:, :, i], axis=1)
Mi = np.mean(Si)
for j in range(i + 1, 6):
Sj = np.sum(d[:, :, j], axis=1)
Mj = np.mean(Sj)
cov[:, k] = np.histogram((Si - Mi) * (Sj - Mj) / (Mi * Mj),
bins=11,
range=[-0.1, 0.1])[0]
k += 1
for k in range(100):
for j in range(len(Q)):
H[:, k,
j] = np.histogram(d[:, k, j],
bins=np.arange(np.shape(H)[0] + 1) - 0.5)[0]
return H / len(ind), spectra / len(ind), cov / len(ind)
def Sim_fit(x1, x2, left, right, gamma, Q, W, nLXe, mu, Bins, bins):
spectra=np.zeros((len(bins)-1, len(Q)))
cov=np.zeros(15)
N_events=1000
N=np.random.poisson(1000*122/W, N_events)
v=make_v(N_events, mu, x1, x2)
p=multiprocessing.Pool(processes=2)
s=p.map(make_d, make_iter(N, Q, v, nLXe))
p.close()
p.join()
s=np.array(s)
up=s[:,0]+s[:,1]
dn=s[:,-1]+s[:,-2]+s[:,-3]
ind=np.nonzero(np.logical_and(np.logical_and(np.sum(s, axis=1)>left, np.sum(s, axis=1)<right), dn<3*up+18))[0]
if len(ind)==0:
w=np.abs(np.mean(np.sum(s, axis=1))-(left+right)/2)
return np.zeros(len(Bins)-1)-w, spectra-w, cov-w
s=np.array(s)[ind]
spectrum=np.histogram(np.sum(s, axis=1), bins=Bins)[0]
for i in range(len(Q)):
spectra[:,i]=np.histogram(s[:,i], bins=bins)[0]
M=np.mean(s, axis=0)
k=0
for i in range(5):
for j in range(i+1,6):
cov[k]=np.mean((s[:,i]-M[i])*(s[:,j]-M[j]))
k+=1
return spectrum/len(ind), spectra/len(ind), cov
def Sim_fit(x1, x2, left, right, gamma, Q, T, St, W, std, nLXe, sigma_smr, mu,
R, a, F, Tf, Ts, Bins, bins):
spectra = np.zeros((len(bins) - 1, len(Q)))
N_events = 1000
H = np.zeros((15, 100, len(Q)))
G = np.zeros((5, 50, 100))
N = np.array([]).astype(int)
while len(N) < N_events:
n = np.round(np.random.normal(1000 * gamma / W, std,
N_events - len(N))).astype(int)
N = np.append(N, n[n > 0])
v = make_v(N_events, mu, x1, x2)
p = multiprocessing.Pool(processes=2)
ret = p.map(make_d,
make_iter(N, Q, T, St, nLXe, sigma_smr, R, a, F, Tf, Ts, v))
p.close()
p.join()
Ret = np.array(ret)
s = np.sum(Ret[:, 0], axis=1)
up = s[:, 0] + s[:, 1]
dn = s[:, -1] + s[:, -2] + s[:, -3]
ind = np.nonzero(
np.logical_and(
np.logical_and(
np.sum(s, axis=1) >= left,
np.sum(s, axis=1) <= right), dn < 3 * up + 18))[0]
if len(ind) == 0:
print('len in<0')
w = np.abs(np.mean(np.sum(s, axis=1)) - (left + right) / 2)
return np.zeros(len(Bins) - 1) - w, spectra - w, G, H
Ret = Ret[ind]
for i in range(np.shape(Ret)[2]):
G[0, :, i] = np.histogram(np.sum(Ret[:, 0, i, :], axis=1),
bins=np.arange(
(np.shape(G)[1] + 1)) - 0.5)[0]
G[1, :, i] = np.histogram(np.sum(Ret[:, 1, i, :], axis=1),
bins=np.arange(
(np.shape(G)[1] + 1)) - 0.5)[0]
G[2, :, i] = np.histogram(np.sum(Ret[:, 2, i, :], axis=1),
bins=np.arange(
(np.shape(G)[1] + 1)) - 0.5)[0]
G[3, :, i] = np.histogram(np.sum(Ret[:, 3, i, :], axis=1),
bins=np.arange(
(np.shape(G)[1] + 1)) - 0.5)[0]
G[4, :, i] = np.histogram(np.sum(Ret[:, 4, i, :], axis=1),
bins=np.arange(
(np.shape(G)[1] + 1)) - 0.5)[0]
for j in range(len(Q)):
H[:, i, j] = np.histogram(Ret[:, 0, i, j],
bins=np.arange(
(np.shape(H)[0] + 1)) - 0.5)[0]
spectrum = np.histogram(np.sum(np.sum(Ret[:, 0], axis=1), axis=1),
bins=Bins)[0]
for i in range(len(Q)):
spectra[:, i] = np.histogram(np.sum(Ret[:, 0, :, i], axis=1),
bins=bins)[0]
return spectrum / len(ind), spectra / len(ind), G / len(ind), H / len(ind)
def Sim_show(x1, x2, left, right, gamma, Q, T, St, Sa, mu, W, F, Tf, Ts, R, a, PEs):
N_events=10000
H=np.zeros((50, 200, len(Q)))
G=np.zeros((250,200))
Gtrp=np.zeros((250,200))
Gsng=np.zeros((250,200))
GRtrp=np.zeros((250,200))
GRsng=np.zeros((250,200))
spectra=np.zeros((len(PEs),len(Q)))
cov=np.zeros((11, 15))
v=make_v(N_events, mu, x1, x2)
N=np.random.poisson(1000*gamma/W, N_events)
p=multiprocessing.Pool(processes=2)
ds=p.map(make_d_show, make_iter(N, Q, T, St, Sa, F, Tf, Ts, R, a, v))
p.close()
p.join()
ds=np.array(ds)
s=np.sum(np.sum(ds[:,0,:100],axis=1), axis=1)
ind=np.nonzero(np.logical_and(s>left, s<right))[0]
if len(ind)==0:
w=np.abs(np.mean(s)-(left+right)/2)
print('No events in the left-right band')
return H-w, spectra-w, cov-w, G-w, G-w, G-w, G-w, G-w, 0
ds=ds[ind]
d=ds[:,0]
trp=ds[:,1]
sng=ds[:,2]
Rtrp=ds[:,3]
Rsng=ds[:,4]
for i in range(len(Q)):
spectra[:,i]=np.histogram(np.sum(d[:,:100,i], axis=1), bins=np.arange(len(PEs)+1)-0.5)[0]
# k=0
# for i in range(5):
# Si=np.sum(d[:,:,i], axis=1)
# Mi=np.mean(Si)
# for j in range(i+1,6):
# Sj=np.sum(d[:,:,j], axis=1)
# Mj=np.mean(Sj)
# cov[:,k]=np.histogram((Si-Mi)*(Sj-Mj)/(Mi*Mj), bins=11, range=[-0.5,0.5])[0]
# k+=1
for k in range(200):
G[:,k]=np.histogram(np.sum(d[:,k,:], axis=1), bins=np.arange(np.shape(G)[0]+1)-0.5)[0]
Gtrp[:,k]=np.histogram(np.sum(trp[:,k,:], axis=1), bins=np.arange(np.shape(G)[0]+1)-0.5)[0]
Gsng[:,k]=np.histogram(np.sum(sng[:,k,:], axis=1), bins=np.arange(np.shape(G)[0]+1)-0.5)[0]
GRtrp[:,k]=np.histogram(np.sum(Rtrp[:,k,:], axis=1), bins=np.arange(np.shape(G)[0]+1)-0.5)[0]
GRsng[:,k]=np.histogram(np.sum(Rsng[:,k,:], axis=1), bins=np.arange(np.shape(G)[0]+1)-0.5)[0]
for j in range(len(Q)):
H[:,k,j]=np.histogram(d[:,k,j], bins=np.arange(np.shape(H)[0]+1)-0.5)[0]
return H/N_events, spectra/N_events, cov/N_events, G/N_events, Gtrp/N_events, Gsng/N_events, GRtrp/N_events, GRsng/N_events, N_events
def Sim_fit(x1, x2, left, right, gamma, Q, T, St, mu, W, g, F, Tf, Ts, R, a,
PEs):
N_events = 10000
d = np.zeros((N_events, 100, len(Q)))
H = np.zeros((50, 100, len(Q)))
spectra = np.zeros((len(PEs), len(Q)))
cov = np.zeros((11, 15))
v = make_v(N_events, mu, x1, x2)
gamma = np.random.choice([gamma1, gamma2], size=1, p=[g, 1 - g])
N = np.random.poisson(1000 * gamma / W, N_events)
p = multiprocessing.Pool(processes=2)
d = p.map(make_d, make_iter(N, Q, T, St, F, Tf, Ts, R, a, v))
p.close()
p.join()
s = np.sum(np.sum(d, axis=1), axis=1)
ind = np.nonzero(np.logical_and(s > left, s < right))[0]
if len(ind) == 0:
w = np.abs(np.mean(s) - (left + right) / 2)
return H - w, spectra - w, cov - w
d = np.array(d)[ind]
for i in range(len(Q)):
spectra[:, i] = np.histogram(np.sum(d[:, :, i], axis=1),
bins=np.arange(len(PEs) + 1) - 0.5)[0]
k = 0
for i in range(5):
Si = np.sum(d[:, :, i], axis=1)
Mi = np.mean(Si)
for j in range(i + 1, 6):
Sj = np.sum(d[:, :, j], axis=1)
Mj = np.mean(Sj)
cov[:, k] = np.histogram((Si - Mi) * (Sj - Mj) / (Mi * Mj),
bins=11,
range=[-0.1, 0.1])[0]
k += 1
for k in range(100):
for j in range(len(Q)):
H[:, k,
j] = np.histogram(d[:, k, j],
bins=np.arange(np.shape(H)[0] + 1) - 0.5)[0]
return H / len(ind), spectra / len(ind), cov / len(ind)
def Sim_fit(x1, x2, left, right, gamma, Q, W, Sa, g, Bins, bins):
spectra = np.zeros((len(bins) - 1, len(Q)))
cov = np.zeros(15)
N_events = 10000
N122 = np.random.binomial(N_events, g)
N136 = N_events - N122
N = np.append(np.random.poisson(1000 * 122 / W, N122),
np.random.poisson(1000 * 136 / W, N136))
# N=np.random.poisson(1000*gamma/W, N_events)
p = multiprocessing.Pool(processes=2)
s = p.map(make_d, make_iter(N, Q, Sa, x1, x2))
p.close()
p.join()
s = np.array(s)
up = s[:, 0] + s[:, 1]
dn = s[:, -1] + s[:, -2] + s[:, -3]
ind = np.nonzero(
np.logical_and(
np.logical_and(
np.sum(s, axis=1) > left,
np.sum(s, axis=1) < right), True))[0]
if len(ind) == 0:
print('len in<0')
w = np.abs(np.mean(np.sum(s, axis=1)) - (left + right) / 2)
return np.zeros(len(Bins) - 1) - w, spectra - w, cov - w
s = np.array(s)[ind]
spectrum = np.histogram(np.sum(s, axis=1), bins=Bins)[0]
for i in range(len(Q)):
spectra[:, i] = np.histogram(s[:, i], bins=bins)[0]
M = np.mean(s, axis=0)
k = 0
# fig, ax=plt.subplots(3,5)
for i in range(5):
for j in range(i + 1, 6):
cov[k] = np.mean((s[:, i] - M[i]) * (s[:, j] - M[j]))
# np.ravel(ax)[k].hist2d(s[:,i]-M[i], s[:,j]-M[j] ,bins=[50,50], range=[[-50, 50], [-50,50]], norm=mcolors.PowerNorm(0.3))
k += 1
# plt.show()
return spectrum / len(ind), spectra / len(ind), cov
def Sim_fit(x1, x2, left, right, gamma, Q, W, PEs):
spectra = np.zeros((len(PEs), len(Q)))
N_events = 10000
N = np.random.poisson(1000 * gamma / W, N_events)
v = make_v(N_events)
p = multiprocessing.Pool(processes=2)
s = p.map(make_d, make_iter(N, Q, v))
p.close()
p.join()
ind = np.nonzero(
np.logical_and(np.sum(s, axis=1) > left,
np.sum(s, axis=1) < right))[0]
if len(ind) == 0:
w = np.abs(np.mean(np.sum(s, axis=1)) - (left + right) / 2)
return spectra - w
s = np.array(s)[ind]
for i in range(len(Q)):
spectra[:, i] = np.histogram(s[:, i],
bins=np.arange(len(PEs) + 1) - 0.5)[0]
return spectra / N_events
def Sim_fit(x1, x2, left, right, gamma, Q, Sa, W, std, Bins, bins, Abins):
spectra = np.zeros((len(bins) - 1, len(Q)))
N_events = 1000
# N=np.random.poisson(1000*gamma/W, N_events)
N = np.array([]).astype(int)
while len(N) < N_events:
n = np.round(np.random.normal(1000 * gamma / W, std,
N_events - len(N))).astype(int)
N = np.append(N, n[n > 0])
v = make_v(N_events)
p = multiprocessing.Pool(processes=2)
ret = p.map(make_d, make_iter(N, Q, Sa, v, Abins))
p.close()
p.join()
s = np.zeros((N_events, len(Q)))
Areas = np.zeros((len(Q), len(Abins[0]) - 1))
for i in range(N_events):
s[i] = ret[i][0]
Areas += ret[i][1]
up = s[:, 0] + s[:, 1]
dn = s[:, -1] + s[:, -2] + s[:, -3]
ind = np.nonzero(
np.logical_and(
np.logical_and(
np.sum(s, axis=1) >= left,
np.sum(s, axis=1) <= right), dn < 3 * up + 18))[0]
if len(ind) == 0:
print('len in<0')
w = np.abs(np.mean(np.sum(s, axis=1)) - (left + right) / 2)
return np.zeros(len(Bins) - 1) - w, spectra - w, Areas * 0
s = np.array(s)[ind]
spectrum = np.histogram(np.sum(s, axis=1), bins=Bins)[0]
for i in range(len(Q)):
spectra[:, i] = np.histogram(s[:, i], bins=bins)[0]
return spectrum / len(ind), spectra / len(ind), (Areas.T /
np.sum(Areas, axis=1))
def make_3D(x1, x2, gamma, Q, T, St, mu, W, F, Tf, Ts, R, a, eta, PEs, Xcov):
v = make_v(10000, mu, x1, x2)
V_mash, dS = make_dS(v)
t = np.arange(200)
dt = 1
r = np.arange(t[0], t[-1] + dt, dt / 100)
dr = r[1] - r[0]
N = gamma * 1000 / W
n = np.arange(np.floor(N - 3 * np.sqrt(N)), np.ceil(N + 3 * np.sqrt(N)))
pois = poisson.pmf(n, N)
nu = np.arange(20)
model = np.sum(((1 - R) * Promt(r, F, Tf, Ts, T, St) + R *
(1 - eta) * Recomb(r, F, Tf, Ts, T, St, a, eta)).reshape(
len(t), 100, len(T)),
axis=1)
frac = np.sum(model[:int(np.mean(T) + 100)], axis=0)
s = np.zeros((len(PEs), len(Q), len(n)))
p = multiprocessing.Pool(processes=2)
Bs = p.map(make_B, make_iter(V_mash, dS, nu, n, model, Q))
p.close()
p.join()
B = np.sum(np.array(Bs), axis=0)
B[np.logical_and(B > 1, B < 1 + 1e-6)] = 1
I = np.arange(len(PEs) * len(Q) * len(n) * len(V_mash))
s = binom.pmf(
PEs[I // (len(Q) * len(n) * len(V_mash))],
(n[(I // len(V_mash)) % len(n)]).astype(int),
dS[I % len(V_mash), (I // (len(n) * len(V_mash))) % len(Q)] *
Q[(I // (len(n) * len(V_mash))) % len(Q)] *
frac[(I // (len(n) * len(V_mash))) % len(Q)]).reshape(
len(PEs), len(Q), len(n), len(V_mash))
S = np.sum(np.sum(s * V_mash, axis=-1) * pois, axis=-1)
M = np.sum(S.T * PEs, axis=1)
Mcov = np.zeros((len(Xcov), 15))
k = 0
for i in range(5):
for j in range(i + 1, 6):
for p1, pe1 in enumerate(PEs):
for p2, pe2 in enumerate(PEs):
c = (pe1 - M[i]) * (pe2 - M[j]) / (M[i] * M[j])
if np.abs(c) <= 0.1:
Mcov[np.argmin(np.abs(c - Xcov)), k] += np.sum(
np.sum(s[p1, i] * s[p2, j] * V_mash, axis=-1) *
pois,
axis=-1)
k += 1
P0 = np.vstack((np.ones(len(n)), np.cumprod(np.prod(B[0], axis=0),
axis=0)[:-1]))
P1 = (P0 * (1 - np.prod(B[0], axis=0)))
P = np.zeros((100, len(nu), len(Q)))
for i in range(len(Q)):
P2 = P0 * (1 - np.prod(B[0, np.delete(np.arange(len(Q)), i)], axis=0))
P[0, 0, i] = np.sum(np.sum(B[0, i] * P2 * pois, axis=1), axis=0)
P[0, 1:] = np.sum(np.sum(B[1:] * P0 * pois, axis=3), axis=2)
for i in range(1, 100):
P[i] = np.sum(np.sum(B[:, :, i:, :] * P1[:len(t) - i, :] * pois,
axis=3),
axis=2)
return np.transpose(P, (1, 0, 2)), S, Mcov
def Sim_show(x1, x2, left, right, gamma, Q, T, St, mu, W, F, Tf, Ts, R, a, eta,
PEs):
f = np.sum(make_recomb(np.arange(1000 * 20) / 100, a,
eta).reshape(1000, 20),
axis=1)
i = 2
while 1 - np.sum(f) > 1e-4:
f = np.sum(make_recomb(np.arange(i * 1000 * 20) / 100, a,
eta).reshape(i * 1000, 20),
axis=1)
i += 1
if np.sum(f) - 1 > 1e-4:
print('In recombenation model:')
print('sum(f):', np.sum(f))
sys.exit()
else:
f = f / np.sum(f)
N_events = 10000
H = np.zeros((50, 200, len(Q)))
G = np.zeros((250, 200))
Gtrp = np.zeros((250, 200))
Gsng = np.zeros((250, 200))
GRtrp = np.zeros((250, 200))
GRsng = np.zeros((250, 200))
spectra = np.zeros((len(PEs), len(Q)))
cov = np.zeros((11, 15))
v = make_v(N_events, mu, x1, x2)
N = np.random.poisson(1000 * gamma / W, N_events)
p = multiprocessing.Pool(processes=2)
ds = p.map(make_d_show, make_iter(N, Q, T, St, F, Tf, Ts, R, a, eta, f, v))
p.close()
p.join()
ds = np.array(ds)
s = np.sum(np.sum(ds[:, 0, :100], axis=1), axis=1)
ind = np.nonzero(np.logical_and(s > left, s < right))[0]
if len(ind) == 0:
w = np.abs(np.mean(s) - (left + right) / 2)
return H - w, spectra - w, cov - w, G - w, G - w, G - w, G - w, G - w, 0
ds = ds[ind]
d = ds[:, 0]
trp = ds[:, 1]
sng = ds[:, 2]
Rtrp = ds[:, 3]
Rsng = ds[:, 4]
for i in range(len(Q)):
spectra[:, i] = np.histogram(np.sum(d[:, :100, i], axis=1),
bins=np.arange(len(PEs) + 1) - 0.5)[0]
k = 0
for i in range(5):
Si = np.sum(d[:, :, i], axis=1)
Mi = np.mean(Si)
for j in range(i + 1, 6):
Sj = np.sum(d[:, :, j], axis=1)
Mj = np.mean(Sj)
cov[:, k] = np.histogram((Si - Mi) * (Sj - Mj) / (Mi * Mj),
bins=11,
range=[-0.5, 0.5])[0]
k += 1
for k in range(200):
G[:, k] = np.histogram(np.sum(d[:, k, :], axis=1),
bins=np.arange(np.shape(G)[0] + 1) - 0.5)[0]
Gtrp[:, k] = np.histogram(np.sum(trp[:, k, :], axis=1),
bins=np.arange(np.shape(G)[0] + 1) - 0.5)[0]
Gsng[:, k] = np.histogram(np.sum(sng[:, k, :], axis=1),
bins=np.arange(np.shape(G)[0] + 1) - 0.5)[0]
GRtrp[:, k] = np.histogram(np.sum(Rtrp[:, k, :], axis=1),
bins=np.arange(np.shape(G)[0] + 1) - 0.5)[0]
GRsng[:, k] = np.histogram(np.sum(Rsng[:, k, :], axis=1),
bins=np.arange(np.shape(G)[0] + 1) - 0.5)[0]
for j in range(len(Q)):
H[:, k,
j] = np.histogram(d[:, k, j],
bins=np.arange(np.shape(H)[0] + 1) - 0.5)[0]
return H / len(ind), spectra / len(ind), cov / len(ind), G / len(
ind), Gtrp / len(ind), Gsng / len(ind), GRtrp / len(ind), GRsng / len(
ind), len(ind)