"noise_level": noise_level,
"show_legend": True,
"show_title": True,
"show_next_point": False,
"show_acq_func": True
}
#############################################################################
# We run a an optimization loop with standard settings
for i in range(30):
next_x = opt.ask()
f_val = objective(next_x)
opt.tell(next_x, f_val)
# The same output could be created with opt.run(objective, n_iter=30)
_ = plot_gaussian_process(opt.get_result(), **plot_args)
#############################################################################
# We see that some minima is found and "exploited"
#
# Now lets try to set kappa and xi using'to other values and
# pass it to the optimizer:
acq_func_kwargs = {"xi": 10000, "kappa": 10000}
#############################################################################
opt = Optimizer([(-2.0, 2.0)],
"GP",
n_initial_points=3,
acq_optimizer="sampling",
acq_func_kwargs=acq_func_kwargs)
#############################################################################
print(f'Evaluation #{i}')
print(args)
print('#######')
jobs.append(Job(p.apply_async(objective, args), args))
for job in jobs:
if job.result.ready():
optimizer.tell(job.args, job.result.get())
jobs.remove(job)
while sum(map(not_ready, jobs)) >= n_procs:
time.sleep(0.5)
for job in jobs:
optimizer.tell(job.args, job.result.get())
except KeyboardInterrupt:
pass
res = optimizer.get_result()
with open(result_filename, 'wb') as f:
dill.dump(optimizer, f)
threshold, fwhm, sigma_radius, roundlo, roundhi, sharplo, sharphi = res.x
res_table = DAOStarFinder(threshold=median + std * threshold,
fwhm=fwhm,
sigma_radius=sigma_radius,
sharplo=sharplo,
sharphi=sharphi,
roundlo=roundlo,
roundhi=roundhi,
exclude_border=True)(img)
plt.ion()
