print("plots will be saved under directory: {0}".format(plot_dir))
if not os.path.exists(plot_dir):
os.makedirs(plot_dir)
star_learning_rate = 0.1
telluric_learning_rate = 0.01
for r,o in enumerate(orders):
model = wobble.Model(data, results, r)
model.add_star('star', variable_bases=K_star,
regularization_par_file=star_reg_file,
learning_rate_template=star_learning_rate)
model.add_telluric('tellurics', rvs_fixed=True, variable_bases=K_t,
regularization_par_file=tellurics_reg_file,
learning_rate_template=telluric_learning_rate)
print("--- ORDER {0} ---".format(o))
if plots:
wobble.optimize_order(model, niter=niter, save_history=True,
basename=plot_dir+'history', movies=movies, epochs_to_plot=epochs)
fig, ax = plt.subplots(1, 1, figsize=(8,5))
ax.plot(data.dates, results.star_rvs[r] + data.bervs - data.drifts - np.mean(results.star_rvs[r] + data.bervs),
'k.', alpha=0.8, ms=4)
ax.plot(data.dates, data.pipeline_rvs + data.bervs - np.mean(data.pipeline_rvs + data.bervs),
'r.', alpha=0.5, ms=4)
ax.set_ylabel('RV (m/s)', fontsize=14)
ax.set_xlabel('BJD', fontsize=14)
plt.savefig(plot_dir+'results_rvs_o{0}.png'.format(o))
plt.close(fig)
for e in epochs:
results.plot_spectrum(r, e, data, plot_dir+'results_synth_o{0}_e{1}.png'.format(o, e))
else:
wobble.optimize_order(model, niter=niter)
del model # not sure if this does anything
print("order {1} optimization finished. time elapsed: {0:.2f} min".format((time() - start_time)/60.0, o))
for r in range(data.R):
model = wobble.Model(data, results, r)
model.add_star('star',
variable_bases=K_star,
regularization_par_file=star_reg_file,
learning_rate_template=0.01,
learning_rate_rvs=1.)
model.add_telluric('tellurics',
rvs_fixed=True,
variable_bases=K_t,
regularization_par_file=tellurics_reg_file,
learning_rate_template=0.01)
wobble.optimize_order(model,
niter=niter,
save_history=False,
rv_uncertainties=False,
template_uncertainties=False,
basename='../results/test',
epochs=epochs,
movies=movies)
results.write('../results/test_{0}_Kstar{1}_Kt{2}.hdf5'.format(
starname, K_star, K_t))
assert False
print(
"running wobble on star {0} with K_star = {1}, K_t = {2}, orders[{3},{4})"
.format(starname, K_star, K_t, start_order, end_order))
start_time = time()
#orders = np.arange(11,53)
orders = np.arange(start_order, end_order)
data = wobble.Data(starname + '_vis' + '_e2ds.hdf5',
filepath='../data/',
for r in range(data.R):
model = wobble.Model(data, results, r)
model.add_star('star',
variable_bases=K_star,
regularization_par_file=star_reg_file,
learning_rate_template=0.01,
learning_rate_rvs=1.)
model.add_telluric('tellurics',
rvs_fixed=True,
variable_bases=K_t,
regularization_par_file=tellurics_reg_file,
learning_rate_template=0.01)
wobble.optimize_order(model,
niter=niter,
save_history=True,
uncertainties=False,
basename='results/test',
epochs=epochs,
movies=movies)
results.write('results/test_{0}_Kstar{1}_Kt{2}.hdf5'.format(
starname, K_star, K_t))
assert False
print("running wobble on star {0} with K_star = {1}, K_t = {2}".format(
starname, K_star, K_t))
start_time = time()
orders = np.arange(72)
data = wobble.Data(starname + '_e2ds.hdf5',
filepath='../data/',
orders=orders)
if True: # reload data and remove all post-upgrade spectra
if not os.path.exists(plot_dir):
os.makedirs(plot_dir)
star_learning_rate = 0.1
telluric_learning_rate = 0.01
for r,o in enumerate(orders):
model = wobble.Model(data, results, r)
model.add_star('star', variable_bases=K_star,
regularization_par_file=None,
learning_rate_template=star_learning_rate)
model.add_telluric('tellurics', rvs_fixed=True, variable_bases=K_t,
learning_rate_template=telluric_learning_rate,
template_fixed=True, template_xs=results_51peg.tellurics_template_xs[o],
template_ys=results_51peg.tellurics_template_ys[o]) # assumes all orders are there for 51 Peg
print("--- ORDER {0} ---".format(o))
if plots:
wobble.optimize_order(model, niter=niter, save_history=True,
basename=plot_dir+'history', epochs_to_plot=epochs, movies=movies)
fig, ax = plt.subplots(1, 1, figsize=(8,5))
ax.plot(data.dates, results.star_rvs[r] + data.bervs - np.mean(results.star_rvs[r] + data.bervs),
'k.', alpha=0.8)
ax.plot(data.dates, data.pipeline_rvs + data.bervs - np.mean(data.pipeline_rvs + data.bervs),
'r.', alpha=0.5)
ax.set_ylabel('RV (m/s)', fontsize=14)
ax.set_xlabel('BJD', fontsize=14)
plt.savefig(plot_dir+'results_rvs_o{0}.png'.format(o))
plt.close(fig)
for e in epochs:
fig, (ax, ax2) = plt.subplots(2, 1, gridspec_kw = {'height_ratios':[4, 1]}, figsize=(12,5))
xs = np.exp(data.xs[r][e])
ax.scatter(xs, np.exp(data.ys[r][e]), marker=".", alpha=0.5, c='k', label='data', s=40)
mask = data.ivars[r][e] <= 1.e-8
ax.scatter(xs[mask], np.exp(data.ys[r][e][mask]), marker=".", alpha=1., c='white', s=20)
with open(reg_log_file, "a") as f:
lst = []
for name in model.components[1].regularization_par:
lst.append(int(np.log10(getattr(model.components[1], name))))
#print("{0},{1},{2},{3},{4}".format(lst[0],lst[1],lst[2],lst[3],lst[4]))
#f.write("{0},{1},{2},{3},{4}".format(lst[0],lst[1],lst[2],lst[3],lst[4]))
#print("{0},{1},{2},{3},{4}".format(lst[0],lst[1],lst[2],lst[3],lst[4]))
f.write("{5},{0},{1},{2},{3},{4}\n".format(lst[0], lst[1], lst[2],
lst[3], lst[4], o))
if plots:
wobble.optimize_order(
model,
niter=niter,
save_history=True,
basename=plot_dir +
'history', #movies=movies,#deprecated in 14_06_2019
#epochs_to_plot=epochs,
#epochs = epochs, #to plot?
rv_uncertainties=True)
fig, ax = plt.subplots(1, 1, figsize=(8, 5))
ax.plot(data.dates,
results.star_rvs[r] + data.bervs -
np.mean(results.star_rvs[r] + data.bervs),
'k.',
alpha=0.8)
ax.plot(data.dates,
data.pipeline_rvs + data.bervs -
np.mean(data.pipeline_rvs + data.bervs),
'r.',
alpha=0.5)
