def lnprob_step2(theta, x, y, yerr):
lp = lnprior_step2(theta)
if not np.isfinite(lp):
return -np.inf
logprobs.append(lp + lnlike_fast_linear.lnlike(theta, x, y, yerr))
logvals.append(theta)
return lp + lnlike_fast_linear.lnlike(theta, x, y, yerr)
def perform_emcee_single(time, flux, err_2, dMu, scale, ROW, mu, color):
diff_time = [x - time[i - 1] for i, x in enumerate(time)][1:]
fig = plt.figure(figsize=(10, 10))
nll = lambda *args: -lnlike_fast_linear.lnlike(*args) #-lnlike_old(*args)
ndim, nwalkers = 2, 100
# MAKE POSITION ARRAY ARRAY FOR WALKERS
if sys.argv[4].lower() == 'normal':
result = [np.log10(V), np.log10(Tau)]
pos = (np.random.rand(nwalkers, ndim) - 0.5) * np.array([1, 1
]) + result
elif sys.argv[4].lower() == 'optimal':
# not sure how this will work for the multi-band situation??
result = op.minimize(nll, [np.log10(V), np.log10(Tau)],
args=(time, flux, err_2))
pos = [
result['x'] + 1e-1 * np.random.rand(ndim) for i in range(nwalkers)
]
else:
print("What the hell do you want to do?")
print("'optimal', or 'normal' search through MCMC?")
exit()
# run sampler
sampler = emcee.EnsembleSampler(nwalkers,
ndim,
lnprob_step2,
args=(time, flux, err_2))
# run mcmc
sampler.run_mcmc(pos, 200)
samples = sampler.chain[:, 50:, :].reshape((-1, ndim))
logprobs_samp = sampler.lnprobability[:, 50:].reshape((-1))
# make corner plot
max_theta = samples[np.argmax(logprobs_samp)]
fig1 = corner.corner(samples,
labels=[r"log$_{10}V$", r"log$_{10}\tau$"],
truths=[max_theta[0], max_theta[1]])
fig1.savefig(file_path + str(ROW) + sys.argv[4] + "_" + color + "_band_" +
"triangle_linear_" + str(sys.argv[5]) + "VAR.pdf")
# PRINT MAX THETA VALUES TO THE SCREEN
print('ROW:', ROW, 'Tau:', str(max_theta[1]), 'V:', str(max_theta[0]))
sausageplot_step2(max_theta[0], time, flux, max_theta[1], 5, err_2, dMu,
scale, ROW, fig, color)
fig.savefig(file_path + str(ROW) + sys.argv[4] + "_" + color + "_band_" +
"sausage_step2_linear_" + str(sys.argv[5]) + "VAR.pdf")
plt.close("all")
# WRITE THE FOUND MAX THETA VALUES TO FILE
fit_str = str(sys.argv[1].split("/")[-1].split("_")[0])
filename = 'scratch_new/' + str(ROW) + "_" + fit_str + "_" + sys.argv[
4] + "_" + color + "_band_linear_" + str(sys.argv[5]) + '.txt'
with open(filename, 'w+') as fout:
fout.write('Fits-Object, Tau, V \n' + fit_str + "\t" + str(ROW) +
'\t' + str(max_theta[1]) + '\t' + str(max_theta[0]))
def lnprob_step2(theta, x, y, yerr):
lp = lnprior_step2(theta)
if not np.isfinite(lp):
return -np.inf
try:
lp_app = lp + lnlike_fast_linear.lnlike(theta, x, y, yerr)
except ValueError: #Nan value case
lp_app = -np.inf #ignore the Nan value case
logprobs.append(lp_app)
logvals.append(theta)
return lp_app