def log_likelihood(cube, ndim, nparams):
i = np.random.choice(nL)
#j = np.random.choice(nF)
L = Lpost[i]
d = dpost[i]
#F = Fpost[j]
# L = 0.380173301412519577E+05
# d = 0.883628502371783142E+00
F = 21.0
vals = forward_model(r,
L,
d,
F,
w0,
mu,
cube[2],
cube[0],
cube[1],
sm_ang=False,
nlambda=1024)
chisq = np.sum((vals - sig)**2 / sig_error**2)
return -chisq / 2.0
def _forward_model(r,
Lpost,
dpost,
Fpost,
Apost,
Tipost,
Vpost,
w0,
mu,
out=None,
label=None):
n = len(Lpost)
sig = np.zeros((len(r), n))
for i in range(n):
sig[:, i] = forward_model(r,
Lpost[i],
dpost[i],
Fpost[i],
w0,
mu,
Apost[i],
Tipost[i],
Vpost[i],
sm_ang=False,
nlambda=1024)
if out and label:
out.put((label, sig))
else:
return sig
def log_likelihood(cube, ndim, nparams):
# forward_model(r, L, d, F, w0, mu, amp, temp, v, nlambda=1024, sm_ang=True)
i = np.random.choice(npost)
L = Lpost[i]
d = dpost[i]
w = w0 + extra_w
mus = mu + extra_mu
extra_amp = [cube[1] * x for x in extra_A]
amp = [cube[2] * cube[1], cube[1]] + extra_amp
Ti = [0.025 * 1000.0 / 300.0, cube[3]] + extra_Ti
V = [0.0, 0.0] + extra_V
vals = forward_model(r,
L,
d,
cube[0],
w,
mus,
amp,
Ti,
V,
sm_ang=False,
nlambda=1024)
#vals = forward_model(r, L, d, cube[0], w0, mu,
# [cube[2]*cube[1], cube[1]], [0.025*1000.0/300.0, cube[3]],
# [0.0, 0.0], sm_ang=False, nlambda=1024)
#vals += cube[4]
chisq = np.sum((vals - sig)**2 / sig_error**2)
return -chisq / 2.0
def _calculate_model_spectrum(r,
L,
d,
F,
w0,
mu,
amp,
temp,
v,
nlambda=1000):
signal = forward_model(r,
L,
d,
F,
w0,
mu,
amp,
temp,
v,
nlambda=nlambda)
return r, signal
def _forward_model(r,
Lpost,
dpost,
Fpost,
Apost,
Arelpost,
Tipost,
w0,
mu,
out=None,
label=None):
n = len(Lpost)
sig = np.zeros((len(r), n))
w = w0 + extra_w
mus = mu + extra_mu
V = [0.0, 0.0] + extra_V
#vals = forward_model(r, L, d, cube[0], w, mus,
# amp, Ti, V, sm_ang=False, nlambda=1024)
for i in range(n):
extra_amp = [Apost[i] * x for x in extra_A]
amp = [Arelpost[i] * Apost[i], Apost[i]] + extra_amp
Ti = [0.025 * 1000.0 / 300.0, Tipost[i]] + extra_Ti
sig[:, i] = forward_model(r,
Lpost[i],
dpost[i],
Fpost[i],
w,
mus,
amp,
Ti,
V,
sm_ang=False,
nlambda=1024)
#sig[:, i] += offsetpost[i]
if out and label:
out.put((label, sig))
else:
return sig
temps = [1000.0 * 0.025 / 300.0 for x in range(len(amps))]
vs = [0.0 for x in range(len(amps))]
w = w0 + extra_w
mus = mu + extra_mu
extra_amp = [Amean * x for x in extra_A]
amp = [Amean * Arelmean, Amean] + extra_amp
Ti = [0.025 * 1000.0 / 300.0, Timean] + extra_Ti
V = [0.0, 0.0] + extra_V
rr = np.linspace(0.0, 900.0, 1001)
vals = forward_model(rr,
Lmean,
dmean,
Fmean,
w,
mus,
amp,
Ti,
V,
sm_ang=False,
nlambda=1024)
#ax.plot(rr**2, vals, 'k')
#ax.plot(new_r**2, sig_mean, 'C7')
#w += [487.873302, 487.98634]
#amps += [Amean * Arelmean, Amean]
#mus += [232.0, 40.0]
#temps += [temps[0], Timean]
#vs += [0.0, 0.0]
#rr = np.linspace(0.0, 1000.0, 1001)
#newoffset = forward_model(rr, Lmean, dmean, Fmean, w, mus, amps, temps, vs, sm_ang=False)