def make_d(iter_array):
[Q, T, St, N, F, Tf, Ts, R, a, v] = iter_array
Strig = 2
d = np.zeros((200, len(Q)))
t0 = np.zeros(len(Q))
trig = np.random.normal(0, Strig, 1)
ex = np.random.binomial(N, 1 - R)
recomb = N - ex
t = np.zeros(recomb + ex)
u = np.random.uniform(size=recomb)
t[:recomb] += 1 / a * (u / (1 - u))
ch = np.random.choice(2, size=recomb + ex, replace=True, p=[F, 1 - F])
t[ch == 0] += np.random.exponential(Tf, len(t[ch == 0]))
t[ch == 1] += np.random.exponential(Ts, len(t[ch == 1]))
slow_i = np.nonzero(ch == 1)[0]
fast_i = np.nonzero(ch == 0)[0]
costheta = np.random.uniform(-1, 1, len(t))
phi = np.random.uniform(0, 2 * np.pi, len(t))
us = np.vstack(
(np.vstack((np.sin(np.arccos(costheta)) * np.cos(phi),
np.sin(np.arccos(costheta)) * np.sin(phi))), costheta))
pmt_hit = whichPMT(v, us)
for j in range(len(Q)):
hits = np.nonzero(pmt_hit == j)[0]
ind = np.nonzero(1 == np.random.choice(
2, size=len(hits), replace=True, p=[1 - Q[j], Q[j]]))[0]
tj = np.random.normal(trig + T[j] + t[hits[ind]], St[j], len(ind))
h, bins = np.histogram(tj, bins=np.arange(201))
d[:, j] = h
if np.any(h > 0):
t0[j] = np.amin(np.nonzero(h > 0)[0])
for j in range(len(Q)):
d[:, j] = np.roll(d[:, j], -int(np.amin(t0)))
return d[:100]
def make_d_show(iter_array):
[Q, T, St, N, F, Tf, Ts, R, a, eta, v, f] = iter_array
Strig = 2
d = np.zeros((5, 200, len(Q)))
# trp=np.zeros((N_events, 200, len(Q)))
# sng=np.zeros((N_events, 200, len(Q)))
# Rtrp=np.zeros((N_events, 200, len(Q)))
# Rsng=np.zeros((N_events, 200, len(Q)))
t0 = np.zeros(len(Q))
trig = np.random.normal(0, Strig, 1)
ex = np.random.binomial(N, 1 - R)
recomb = np.random.binomial(N - ex, 1 - eta)
t = np.zeros(recomb + ex)
t[:recomb] += np.random.choice(np.arange(len(f[:])) / 5,
size=recomb,
replace=True,
p=f[:])
ch = np.random.choice(2, size=recomb + ex, replace=True, p=[F, 1 - F])
t[ch == 0] += np.random.exponential(Tf, len(t[ch == 0]))
t[ch == 1] += np.random.exponential(Ts, len(t[ch == 1]))
slow_i = np.nonzero(ch == 1)[0]
fast_i = np.nonzero(ch == 0)[0]
costheta = np.random.uniform(-1, 1, len(t))
phi = np.random.uniform(0, 2 * np.pi, len(t))
us = np.vstack(
(np.vstack((np.sin(np.arccos(costheta)) * np.cos(phi),
np.sin(np.arccos(costheta)) * np.sin(phi))), costheta))
pmt_hit = whichPMT(v, us)
for j in range(len(Q)):
hits = np.nonzero(pmt_hit == j)[0]
ind = np.nonzero(1 == np.random.choice(
2, size=len(hits), replace=True, p=[1 - Q[j], Q[j]]))[0]
tj = np.random.normal(trig + T[j] + t[hits[ind]], St[j], len(ind))
h, bins = np.histogram(tj, bins=np.arange(201))
d[0, :, j] = h
d[1, :, j] = np.histogram(tj[np.nonzero(
np.logical_and(hits[ind] >= recomb, np.isin(hits[ind],
slow_i)))[0]],
bins=np.arange(201))[0]
d[2, :, j] = np.histogram(tj[np.nonzero(
np.logical_and(hits[ind] >= recomb, np.isin(hits[ind],
fast_i)))[0]],
bins=np.arange(201))[0]
d[3, :, j] = np.histogram(tj[np.nonzero(
np.logical_and(hits[ind] < recomb, np.isin(hits[ind],
slow_i)))[0]],
bins=np.arange(201))[0]
d[4, :, j] = np.histogram(tj[np.nonzero(
np.logical_and(hits[ind] < recomb, np.isin(hits[ind],
fast_i)))[0]],
bins=np.arange(201))[0]
if np.any(h > 0):
t0[j] = np.amin(np.nonzero(h > 0)[0])
for i in range(5):
for j in range(len(Q)):
d[i, :, j] = np.roll(d[i, :, j], -int(np.amin(t0)))
return d
def make_d(N,Q,v):
s=np.zeros((len(N), len(Q)))
for i in range(len(N)):
costheta=np.random.uniform(-1,1, N[i])
phi=np.random.uniform(0,2*np.pi, N[i])
us=np.vstack((np.vstack((np.sin(np.arccos(costheta))*np.cos(phi), np.sin(np.arccos(costheta))*np.sin(phi))), costheta))
pmt_hit=whichPMT(v[i], us)
for j in range(len(Q)):
s[i, j]=np.random.binomial(len(np.nonzero(pmt_hit==j)[0]), Q[j])
return s
def make_d_show(iter_array):
[Q, T, St, N, F, Tf, Ts, R, a, v, dl] = iter_array
Strig = 2
d = np.zeros((6, 200, len(Q)))
t0 = np.zeros(len(Q))
trig = np.random.normal(0, Strig, 1)
ex = np.random.binomial(N, 1 - R)
sr = np.random.binomial(ex, dl)
recomb = N - ex
t = np.zeros(recomb + ex)
u = np.random.uniform(size=recomb)
t[sr:sr + recomb] += 1 / a * (u / (1 - u))
ch = np.random.choice(2, size=recomb + ex - sr, replace=True, p=[F, 1 - F])
slow_i = sr + np.nonzero(ch == 1)[0]
fast_i = sr + np.nonzero(ch == 0)[0]
t[fast_i] += np.random.exponential(Tf, len(fast_i))
t[slow_i] += np.random.exponential(Ts, len(slow_i))
costheta = np.random.uniform(-1, 1, len(t))
phi = np.random.uniform(0, 2 * np.pi, len(t))
us = np.vstack(
(np.vstack((np.sin(np.arccos(costheta)) * np.cos(phi),
np.sin(np.arccos(costheta)) * np.sin(phi))), costheta))
pmt_hit = whichPMT(v, us)
for j in range(len(Q)):
hits = np.nonzero(pmt_hit == j)[0]
ind = np.nonzero(1 == np.random.choice(
2, size=len(hits), replace=True, p=[1 - Q[j], Q[j]]))[0]
tj = np.random.normal(trig + T[j] + t[hits[ind]], St[j], len(ind))
h = np.histogram(tj, bins=np.arange(201))[0]
d[0, :, j] = h
d[1, :, j] = np.histogram(tj[hits[ind] < sr], bins=np.arange(201))[0]
d[2, :, j] = np.histogram(tj[np.logical_and(hits[ind] >= sr + recomb,
np.isin(hits[ind],
slow_i))],
bins=np.arange(201))[0]
d[3, :, j] = np.histogram(tj[np.logical_and(hits[ind] >= sr + recomb,
np.isin(hits[ind],
fast_i))],
bins=np.arange(201))[0]
d[4, :, j] = np.histogram(tj[np.logical_and(
np.logical_and(hits[ind] >= sr, hits[ind] < sr + recomb),
np.isin(hits[ind], slow_i))],
bins=np.arange(201))[0]
d[5, :, j] = np.histogram(tj[np.logical_and(
np.logical_and(hits[ind] >= sr, hits[ind] < sr + recomb),
np.isin(hits[ind], fast_i))],
bins=np.arange(201))[0]
if np.any(h > 0):
t0[j] = np.amin(np.nonzero(h > 0)[0])
for i in range(np.shape(d)[0]):
for j in range(len(Q)):
d[i, :, j] = np.roll(d[i, :, j], -int(np.amin(t0)))
return d
def make_d(iter_array):
[Q, N, v] = iter_array
costheta = np.random.uniform(-1, 1, N)
phi = np.random.uniform(0, 2 * np.pi, N)
us = np.vstack(
(np.vstack((np.sin(np.arccos(costheta)) * np.cos(phi),
np.sin(np.arccos(costheta)) * np.sin(phi))), costheta))
pmt_hit = whichPMT(v, us)
s = np.zeros(len(Q))
for j in range(len(Q)):
s[j] = np.random.binomial(len(np.nonzero(pmt_hit == j)[0]), Q[j])
return s
def make_d_show(iter_array):
[Q, T, St, Sa, N, F, Tf, Ts, R, a, v]=iter_array
Strig=2
d=np.zeros((5, 200, len(Q)))
t0=np.zeros(len(Q))
trig=np.random.normal(0, Strig, 1)
ex=np.random.binomial(N, 1-R)
recomb=N-ex
t=np.zeros(recomb+ex)
u=np.random.uniform(size=recomb)
t[:recomb]+=1/a*(u/(1-u))
ch=np.random.choice(2, size=recomb+ex, replace=True, p=[F, 1-F])
t[ch==0]+=np.random.exponential(Tf, len(t[ch==0]))
t[ch==1]+=np.random.exponential(Ts, len(t[ch==1]))
slow_i=np.nonzero(ch==1)[0]
fast_i=np.nonzero(ch==0)[0]
costheta=np.random.uniform(-1,1, len(t))
phi=np.random.uniform(0,2*np.pi, len(t))
us=np.vstack((np.vstack((np.sin(np.arccos(costheta))*np.cos(phi), np.sin(np.arccos(costheta))*np.sin(phi))), costheta))
pmt_hit=whichPMT(v, us)
for j in range(len(Q)):
hits=np.nonzero(pmt_hit==j)[0]
ind=np.nonzero(1==np.random.choice(2, size=len(hits), replace=True, p=[1-Q[j], Q[j]]))[0]
tj=np.random.normal(trig+T[j]+t[hits[ind]], St[j], len(ind))
h0, bins=np.histogram(tj, bins=np.arange(201))
As=np.random.normal(loc=h0[h0>0], scale=np.sqrt(h0[h0>0]*Sa[j]))
d[0,h0>0,j]+=np.round(As).astype(int)
h=np.histogram(tj[np.nonzero(np.logical_and(hits[ind]>=recomb, np.isin(hits[ind], slow_i)))[0]], bins=np.arange(201))[0]
As=np.random.normal(loc=h[h>0], scale=np.sqrt(h[h>0]*Sa[j]))
d[1,h>0,j]+=np.round(As).astype(int)
h=np.histogram(tj[np.nonzero(np.logical_and(hits[ind]>=recomb, np.isin(hits[ind], fast_i)))[0]], bins=np.arange(201))[0]
As=np.random.normal(loc=h[h>0], scale=np.sqrt(h[h>0]*Sa[j]))
d[2,h>0,j]+=np.round(As).astype(int)
h=np.histogram(tj[np.nonzero(np.logical_and(hits[ind]<recomb, np.isin(hits[ind], slow_i)))[0]], bins=np.arange(201))[0]
As=np.random.normal(loc=h[h>0], scale=np.sqrt(h[h>0]*Sa[j]))
d[3,h>0,j]+=np.round(As).astype(int)
h=np.histogram(tj[np.nonzero(np.logical_and(hits[ind]<recomb, np.isin(hits[ind], fast_i)))[0]], bins=np.arange(201))[0]
As=np.random.normal(loc=h[h>0], scale=np.sqrt(h[h>0]*Sa[j]))
d[4,h>0,j]+=np.round(As).astype(int)
if np.any(h0>0):
t0[j]=np.amin(np.nonzero(h0>0)[0])
for i in range(5):
for j in range(len(Q)):
d[i,:,j]=np.roll(d[i,:,j], -int(np.amin(t0)))
return d
def make_d(iter):
x1, x2, Q, Sa, N, i = iter
np.random.seed(int(i * time.time() % (2**32)))
s = np.zeros(len(Q))
costheta = np.random.uniform(-1, 1, N)
phi = np.random.uniform(0, 2 * np.pi, N)
us = np.vstack(
(np.vstack((np.sin(np.arccos(costheta)) * np.cos(phi),
np.sin(np.arccos(costheta)) * np.sin(phi))), costheta))
vs = np.zeros((3, N))
count = 0
while count < N:
#d=np.random.exponential(mu, N-count)
d = np.random.uniform(0, 0.5, N - count)
ind = np.nonzero(d < 0.5)[0]
if len(ind) == 1:
X1 = d[ind] - 0.25
r = np.sqrt(np.random.uniform(0, 0.25**2 - X1**2))
phi = np.random.uniform(0, 2 * np.pi)
v = np.zeros((3, 1))
v[x1] = X1
v[x2] = r * np.cos(phi)
v[2] = r * np.sin(phi)
vs[:, count:count + 1] = v
count += 1
else:
X1 = d[ind] - 0.25
r = np.sqrt(np.random.uniform(0, 0.25**2 - X1**2))
phi = np.random.uniform(0, 2 * np.pi, len(r))
v = np.zeros((3, len(r)))
v[x1] = X1
v[x2] = r * np.cos(phi)
v[2] = r * np.sin(phi)
vs[:, count:count + len(r)] = v
count += len(r)
pmt_hit = whichPMT(vs, us)
for j in range(len(Q)):
area = np.sum(
np.random.normal(
1, Sa[j],
np.random.binomial(len(np.nonzero(pmt_hit == j)[0]), Q[j])))
#s[j]=np.random.binomial(len(np.nonzero(pmt_hit==j)[0]), Q[j])
if area > 0:
s[j] = int(np.round(area))
return s
def make_d(iter):
np.random.seed(int(iter * time.time() % (2 * 32)))
Strig = 2
d = np.zeros((200, len(Q)))
t0 = np.zeros(len(Q))
trig = np.random.normal(0, Strig, 1)
N_glob = np.random.poisson(N.value)
ex = np.random.binomial(N_glob, 1 - R.value)
recomb = np.random.binomial(N_glob - ex, 1 - eta.value)
t = np.zeros(recomb + ex)
t[:recomb] += np.random.choice(np.arange(len(f)) / 5,
size=recomb,
replace=True,
p=f)
ch = np.random.choice(2,
size=recomb + ex,
replace=True,
p=[F.value, 1 - F.value])
t[ch == 0] += np.random.exponential(Tf.value, len(t[ch == 0]))
t[ch == 1] += np.random.exponential(Ts.value, len(t[ch == 1]))
slow_i = np.nonzero(ch == 1)[0]
fast_i = np.nonzero(ch == 0)[0]
costheta = np.random.uniform(-1, 1, len(t))
phi = np.random.uniform(0, 2 * np.pi, len(t))
us = np.vstack(
(np.vstack((np.sin(np.arccos(costheta)) * np.cos(phi),
np.sin(np.arccos(costheta)) * np.sin(phi))), costheta))
pmt_hit = whichPMT(v[iter], us)
for j in range(len(Q)):
hits = np.nonzero(pmt_hit == j)[0]
ind = np.nonzero(1 == np.random.choice(
2, size=len(hits), replace=True, p=[1 - Q[j], Q[j]]))[0]
tj = np.random.normal(trig + T[j] + t[hits[ind]], St[j], len(ind))
h, bins = np.histogram(tj, bins=np.arange(201))
d[:, j] = h
if np.any(h > 0):
t0[j] = np.amin(np.nonzero(h > 0)[0])
for j in range(len(Q)):
d[:, j] = np.roll(d[:, j], -int(np.amin(t0)))
return d
def make_d(iter):
N, Q, Sa, v, i, Abins = iter
np.random.seed(int(i * time.time() % (2**32)))
s = np.zeros(len(Q))
costheta = np.random.uniform(-1, 1, N)
phi = np.random.uniform(0, 2 * np.pi, N)
us = np.vstack(
(np.vstack((np.sin(np.arccos(costheta)) * np.cos(phi),
np.sin(np.arccos(costheta)) * np.sin(phi))), costheta))
pmt_hit = whichPMT(v.reshape(3, 1), us)
# pmt_hit=whichPMT(vs, us)
Areas = np.zeros((len(Q), len(Abins[0]) - 1))
for j in range(len(Q)):
areas = np.random.normal(
1, Sa[j], np.random.binomial(len(np.nonzero(pmt_hit == j)[0]),
Q[j]))
Areas[j] = np.histogram(areas, bins=Abins[j])[0]
#s[j]=np.random.binomial(len(np.nonzero(pmt_hit==j)[0]), Q[j])
A = np.sum(areas)
if A > 0:
s[j] = int(np.round(A))
return [s, Areas]
def make_d(iter):
Q, N, v, i = iter
np.random.seed(int(i * time.time() % (2**32)))
s = np.zeros(len(Q))
costheta = np.random.uniform(-1, 1, N)
phi = np.random.uniform(0, 2 * np.pi, N)
us = np.vstack(
(np.vstack((np.sin(np.arccos(costheta)) * np.cos(phi),
np.sin(np.arccos(costheta)) * np.sin(phi))), costheta))
vs = np.repeat(v, N).reshape(3, N)
# count=np.zeros(N)
# while np.any(np.sqrt(np.sum(vs**2, axis=0))<0.75):
#
# absorb=np.nonzero(count>10)[0]
# vs[0,absorb]=2
# us[:,absorb]=vs[:,absorb]
#
# ind_LXe=np.nonzero(np.sqrt(np.sum(vs**2, axis=0))<=0.25)[0]
# ind_toLXe=np.nonzero(np.logical_and(np.sqrt(np.sum(vs**2, axis=0))>0.25, np.sum(vs*us, axis=0)<=0))[0]
# ind_toVac=np.nonzero(np.logical_and(np.logical_and(np.sqrt(np.sum(vs**2, axis=0))<0.75, np.sqrt(np.sum(vs**2, axis=0))>0.25), np.sum(vs*us, axis=0)>0))[0]
#
# count[ind_LXe]+=1
# count[ind_toLXe]+=1
# count[ind_toVac]+=1
#
# if len(ind_LXe)>0:
# vs[:,ind_LXe], us[:,ind_LXe]=traceLXe(vs[:,ind_LXe], us[:,ind_LXe], nLXe, sigma_smr)
# if len(ind_toLXe)>0:
# vs[:,ind_toLXe], us[:,ind_toLXe]=tracetoLXe(vs[:,ind_toLXe], us[:,ind_toLXe], nLXe, sigma_smr)
# if len(ind_toVac)>0:
# vs[:,ind_toVac], us[:,ind_toVac]=tracetoVac(vs[:,ind_toVac], us[:,ind_toVac], nLXe, sigma_smr)
pmt_hit = whichPMT(v, us)
for j in range(len(Q)):
#area=np.sum(np.random.normal(1, 1, np.random.binomial(len(np.nonzero(pmt_hit==j)[0]), Q[j])))
s[j] = np.random.binomial(len(np.nonzero(pmt_hit == j)[0]), Q[j])
#s[j]=int(np.round(area))
return s
def Sim(N, F, Tf, Ts, R, a, eta, Q, T, St, PEs, mu, x1, x2, left, right):
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
Strig = 2
d = np.zeros((N_events, 200, len(Q)))
H = np.zeros((50, 200, len(Q)))
spectra = np.zeros((len(PEs), len(Q)))
cov = np.zeros((11, 15))
G = np.zeros((250, 200))
trp = np.zeros((N_events, 200, len(Q)))
sng = np.zeros((N_events, 200, len(Q)))
Rtrp = np.zeros((N_events, 200, len(Q)))
Rsng = np.zeros((N_events, 200, len(Q)))
Gtrp = np.zeros((250, 200))
Gsng = np.zeros((250, 200))
GRtrp = np.zeros((250, 200))
GRsng = np.zeros((250, 200))
v = make_v(N_events, mu, x1, x2)
for i in range(N_events):
# print(i, 'out of', N_events)
t0 = np.zeros(len(Q))
trig = np.random.normal(0, Strig, 1)
N_glob = np.random.poisson(N)
ex = np.random.binomial(N_glob, 1 - R)
recomb = np.random.binomial(N_glob - ex, 1 - eta)
t = np.zeros(recomb + ex)
t[:recomb] += np.random.choice(np.arange(len(f)) / 5,
size=recomb,
replace=True,
p=f)
ch = np.random.choice(2, size=recomb + ex, replace=True, p=[F, 1 - F])
t[ch == 0] += np.random.exponential(Tf, len(t[ch == 0]))
t[ch == 1] += np.random.exponential(Ts, len(t[ch == 1]))
slow_i = np.nonzero(ch == 1)[0]
fast_i = np.nonzero(ch == 0)[0]
costheta = np.random.uniform(-1, 1, len(t))
phi = np.random.uniform(0, 2 * np.pi, len(t))
us = np.vstack((np.vstack(
(np.sin(np.arccos(costheta)) * np.cos(phi),
np.sin(np.arccos(costheta)) * np.sin(phi))), costheta))
pmt_hit = whichPMT(v[i], us)
for j in range(len(Q)):
hits = np.nonzero(pmt_hit == j)[0]
ind = np.nonzero(1 == np.random.choice(
2, size=len(hits), replace=True, p=[1 - Q[j], Q[j]]))[0]
tj = np.random.normal(trig + T[j] + t[hits[ind]], St[j], len(ind))
h, bins = np.histogram(tj, bins=np.arange(201))
if np.any(h > 0):
t0[j] = np.amin(np.nonzero(h > 0)[0])
d[i, :, j] = h
trp[i, :, j] = np.histogram(tj[np.nonzero(
np.logical_and(hits[ind] >= recomb, np.isin(hits[ind],
slow_i)))[0]],
bins=np.arange(201))[0]
sng[i, :, j] = np.histogram(tj[np.nonzero(
np.logical_and(hits[ind] >= recomb, np.isin(hits[ind],
fast_i)))[0]],
bins=np.arange(201))[0]
Rtrp[i, :, j] = np.histogram(tj[np.nonzero(
np.logical_and(hits[ind] < recomb, np.isin(hits[ind],
slow_i)))[0]],
bins=np.arange(201))[0]
Rsng[i, :, j] = np.histogram(tj[np.nonzero(
np.logical_and(hits[ind] < recomb, np.isin(hits[ind],
fast_i)))[0]],
bins=np.arange(201))[0]
for j in range(len(Q)):
d[i, :, j] = np.roll(d[i, :, j], -int(np.amin(t0)))
trp[i, :, j] = np.roll(trp[i, :, j], -int(np.amin(t0)))
sng[i, :, j] = np.roll(sng[i, :, j], -int(np.amin(t0)))
Rtrp[i, :, j] = np.roll(Rtrp[i, :, j], -int(np.amin(t0)))
Rsng[i, :, j] = np.roll(Rsng[i, :, j], -int(np.amin(t0)))
s = np.sum(np.sum(d[:, :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, Gtrp - w, Gsng - w, GRtrp - w, GRsng - w
d = d[ind]
trp = trp[ind]
sng = sng[ind]
Rtrp = Rtrp[ind]
Rsng = Rsng[ind]
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[:, :100, i], axis=1)
Mi = np.mean(Si)
for j in range(i + 1, 6):
Sj = np.sum(d[:, :100, 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(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)
def make_d(iter):
N, Q, T, St, nLXe, sigma_smr, R, a, F, Tf, Ts, v, i = iter
np.random.seed(int(i * time.time() % (2**32)))
h = np.zeros((200, len(Q)))
Strig = 2
trig = np.random.normal(0, Strig, 1)
recomb = np.random.binomial(N, R)
ex = N - recomb
t = np.zeros(recomb + ex)
u = np.random.uniform(size=recomb)
t[:recomb] += 1 / a * (u / (1 - u))
sng_ind = np.random.choice(2, size=N, replace=True, p=[F, 1 - F])
t[sng_ind == 0] += np.random.exponential(Tf, len(t[sng_ind == 0]))
t[sng_ind == 1] += np.random.exponential(Ts, len(t[sng_ind == 1]))
costheta = np.random.uniform(-1, 1, N)
phi = np.random.uniform(0, 2 * np.pi, N)
us = np.vstack(
(np.vstack((np.sin(np.arccos(costheta)) * np.cos(phi),
np.sin(np.arccos(costheta)) * np.sin(phi))), costheta))
vs = np.repeat(v, N).reshape(3, N)
count = np.zeros(N)
while np.any(np.sqrt(np.sum(vs**2, axis=0)) < 0.75):
absorb = np.nonzero(count > 10)[0]
vs[0, absorb] = 2
us[:, absorb] = vs[:, absorb]
ind_LXe = np.nonzero(np.sqrt(np.sum(vs**2, axis=0)) <= 0.25)[0]
ind_toLXe = np.nonzero(
np.logical_and(
np.sqrt(np.sum(vs**2, axis=0)) > 0.25,
np.sum(vs * us, axis=0) <= 0))[0]
ind_toVac = np.nonzero(
np.logical_and(
np.logical_and(
np.sqrt(np.sum(vs**2, axis=0)) < 0.75,
np.sqrt(np.sum(vs**2, axis=0)) > 0.25),
np.sum(vs * us, axis=0) > 0))[0]
count[ind_LXe] += 1
count[ind_toLXe] += 1
count[ind_toVac] += 1
if len(ind_LXe) > 0:
vs[:, ind_LXe], us[:, ind_LXe] = traceLXe(vs[:, ind_LXe],
us[:, ind_LXe], nLXe,
sigma_smr)
if len(ind_toLXe) > 0:
vs[:,
ind_toLXe], us[:,
ind_toLXe] = tracetoLXe(vs[:, ind_toLXe],
us[:, ind_toLXe], nLXe,
sigma_smr)
if len(ind_toVac) > 0:
vs[:,
ind_toVac], us[:,
ind_toVac] = tracetoVac(vs[:, ind_toVac],
us[:, ind_toVac], nLXe,
sigma_smr)
pmt_hit = whichPMT(vs, us)
t0 = np.zeros(len(Q)) + 1000
for j in range(len(Q)):
hit_ind = np.nonzero(pmt_hit == j)[0]
PE_extrct = hit_ind[np.nonzero(
np.random.choice(
2, size=len(hit_ind), replace=True, p=[Q[j], 1 -
Q[j]]) == 0)[0]]
tj = np.random.normal(trig + T[j] + t[PE_extrct], St[j],
len(PE_extrct))
h[:, j] = np.histogram(tj, bins=np.arange(201) - 0.5)[0]
if np.any(h[:, j] > 0):
t0[j] = np.amin(np.nonzero(h[:, j] > 0)[0])
for j in range(len(Q)):
h[:, j] = np.roll(h[:, j], -int(np.amin(t0)))
return h[:100]
