#obs = np.recfromcsv('04012015_trimmed.csv',usecols=(1,4,14,32),delimiter=",")
#obs = obs[obs['koi_disposition'] != "FALSE POSITIVE"]
#obs.dtype.names = 'ktc_kepler_id','koi_disposition','period', 'T'
theta_0 = (2.0, 0.1, 9)
obs = model.generate_data(theta_0)
model.set_prior([stats.uniform(0, 90.0),
stats.uniform(0, 1),
stats.uniform(0, 20)])
model.set_data(obs)
start = time.time()
OT = simple_abc.pmc_abc(model, obs, epsilon_0=eps, min_particles=min_part,
steps=2, target_epsilon=eps, parallel=False)
out_pickle = file('pickles/kepler_pmc_xi_based_each_bin_dist_100_KNOWN_prime.pkl', 'w')
pickle.dump(OT, out_pickle)
out_pickle.close()
for i in range(0, steps):
PT = OT
OT = simple_abc.pmc_abc(model, obs, epsilon_0=eps, min_particles=min_part,
resume=PT, steps=2, target_epsilon=eps, parallel=False)
out_pickle = file('pickles/kepler_pmc_xi_based_each_bin_dist_100_KNOW__{:}.pkl'.format(i),
'w')
pickle.dump(OT, out_pickle)
out_pickle.close()
end = time.time()
print 'Serial took {}s'.format(end - start)
model.set_data(obs)
start = time.time()
if known:
PT = pickle.load(file(
'RUNS/{0}/KNOWN/{0}_{1}samples_{2}.pkl'.format(name, min_part,
start_step), 'r'))
else:
PT = pickle.load(file(
'RUNS/{0}/SCIENCE/{0}_{1}samples_{2}.pkl'.format(name, min_part,
start_step),'r'))
OT = simple_abc.pmc_abc(model, obs, epsilon_0=1, min_samples=job_particles,
resume=PT, steps=1, parallel=False, n_procs=n_procs,
sample_only=True)
if known:
out_pickle = file(
'RUNS/{0}/KNOWN/{0}_{1}_oversample{3}_{2}.pkl'.format(name, min_part, osamp_num, start_step+1),
'w')
else:
out_pickle = file(
'RUNS/{0}/SCIENCE/{0}_{1}_oversamples{3}_{2}.pkl'.format(name, min_part, osamp_num, start_step+1),
'w')
pickle.dump(OT, out_pickle)
out_pickle.close()
end = time.time()
print 'This run took {}s'.format(end - start)
'T', 'koi_prad')
obs = obs[(obs['period'] >= 10.0) & (obs['period'] <= 320.0) &
(obs['koi_prad'] <=20.0)]
print obs[0:3]
model.set_prior([stats.uniform(0, 90.0),
stats.uniform(0, 1),
stats.uniform(0, 20)])
model.set_data(obs)
start = time.time()
OT = simple_abc.pmc_abc(model, obs, epsilon_0=eps, min_samples=min_part,
steps=1, parallel=True, n_procs=n_procs)
if known:
out_pickle = file('RUNS/{0}/KNOWN/{0}_{1}samples_0.pkl'.format(name,
min_part), 'w')
observed = file('RUNS/{0}/KNOWN/obs_data.pkl'.format(name), 'w')
pickle.dump(obs, observed)
observed.close()
else:
out_pickle = file('RUNS/{0}/SCIENCE/{0}_{1}samples_0.pkl'.format(name,
min_part), 'w')
observed = file('RUNS/{0}/SCIENCE/obs_data.pkl'.format(name), 'w')
pickle.dump(obs, observed)
observed.close()
pickle.dump(OT, out_pickle)
eff = DetectionRamp(6, 16)
data = pop.observe(new=True, regr_trap=True).observe(eff)
model = ABCModel(pop, eff)
print("True model parameters {}: {}".format(model.params, model.theta_0))
model.null_distance_test(N=args.n_null)
print("norms = {}".format(model.distance_norms))
# model._distance_norms = np.array([ 1. , 4.49241213, 2.60025772, 2.73734061])
pmc_posterior = pmc_abc(
model,
data,
epsilon_0=args.epsilon_0,
min_samples=args.min_samples,
steps=args.steps,
verbose=True,
parallel=True,
n_procs=args.n_procs,
)
try:
np.save(args.file, pmc_posterior)
except:
logging.warning(
"Posterior not saved to desired location "
+ "({}) because of problem! Saved to recovered.npy".format(args.file)
)
np.save("recovered", pmc_posterior)