def scan_llh(dom_tables_kw, hypo_kw, events_kw, scan_kw):
"""Script "main" function"""
t00 = time.time()
scan_values = []
for dim in HYPO_PARAMS_T._fields:
val_str = ''.join(scan_kw.pop(dim))
val_str = val_str.lower().replace('pi', format(np.pi, '.17e'))
scan_values.append(hrlist2list(val_str))
dom_tables = init_obj.setup_dom_tables(**dom_tables_kw)
hypo_handler = init_obj.setup_discrete_hypo(**hypo_kw)
events_generator = init_obj.get_events(**events_kw)
# Pop 'outdir' from `scan_kw` since we don't want to store this info in
# the metadata dict.
outdir = expand(scan_kw.pop('outdir'))
mkdir(outdir)
print('Scanning paramters')
t0 = time.time()
fast_llh = True
if fast_llh:
get_llh = dom_tables._get_llh
dom_info = dom_tables.dom_info
tables = dom_tables.tables
table_norm = dom_tables.table_norm
t_indep_tables = dom_tables.t_indep_tables
t_indep_table_norm = dom_tables.t_indep_table_norm
sd_idx_table_indexer = dom_tables.sd_idx_table_indexer
metric_kw = {}
def metric_wrapper(hypo, hits, hits_indexer, unhit_sd_indices,
time_window):
sources = hypo_handler.get_sources(hypo)
return get_llh(sources=sources,
hits=hits,
hits_indexer=hits_indexer,
unhit_sd_indices=unhit_sd_indices,
sd_idx_table_indexer=sd_idx_table_indexer,
time_window=time_window,
dom_info=dom_info,
tables=tables,
table_norm=table_norm,
t_indep_tables=t_indep_tables,
t_indep_table_norm=t_indep_table_norm)
else:
metric_kw = dict(dom_tables=dom_tables, tdi_table=None)
get_llh = likelihood.get_llh
def metric_wrapper(hypo, **metric_kw):
sources = hypo_handler.get_sources(hypo)
return get_llh(sources=sources, **metric_kw)
n_points_total = 0
metric_vals = []
for _, event in events_generator:
hits = event['hits']
hits_indexer = event['hits_indexer']
hits_summary = event['hits_summary']
metric_kw['hits'] = hits
metric_kw['hits_indexer'] = hits_indexer
hit_sd_indices = hits_indexer['sd_idx']
unhit_sd_indices = np.array(sorted(
ALL_STRS_DOMS_SET.difference(hit_sd_indices)),
dtype=np.uint32)
metric_kw['unhit_sd_indices'] = unhit_sd_indices
metric_kw['time_window'] = np.float32(
hits_summary['time_window_stop'] -
hits_summary['time_window_start'])
t1 = time.time()
metric_vals.append(scan(scan_values, metric_wrapper, metric_kw))
dt = time.time() - t1
n_points = metric_vals[-1].size
n_points_total += n_points
print(' ---> {:.3f} s, {:d} points ({:.3f} ms per LLH)'.format(
dt, n_points, dt / n_points * 1e3))
dt = time.time() - t0
info = OrderedDict([
('hypo_params', HYPO_PARAMS_T._fields),
('scan_values', scan_values),
('metric_name', 'llh'),
('metric_vals', metric_vals),
('scan_kw', sort_dict(scan_kw)),
('dom_tables_kw', sort_dict(dom_tables_kw)),
('hypo_kw', sort_dict(hypo_kw)),
('events_kw', sort_dict(events_kw)),
])
outfpath = join(outdir, 'scan.pkl')
print('Saving results in pickle file, path "{}"'.format(outfpath))
pickle.dump(info, open(outfpath, 'wb'), protocol=pickle.HIGHEST_PROTOCOL)
print('Total time to scan: {:.3f} s; {:.3f} ms avg per LLH'.format(
time.time() - t00, dt / n_points_total * 1e3))
return metric_vals, info
spec['y_max'] = float(y_max)
spec['n_y'] = int(n_y)
spec['z_min'] = float(z_min)
spec['z_max'] = float(z_max)
spec['n_z'] = int(n_z)
spec['costhetadir_min'] = -1
spec['costhetadir_max'] = 1
spec['n_costhetadir'] = int(n_costhetadir)
spec['phidir_min'] = -np.pi
spec['phidir_max'] = np.pi
spec['n_phidir'] = int(n_phidir)
specs.append(spec)
hypo_handler = setup_discrete_hypo(
cascade_kernel=None,
track_kernel='table_e_loss',
track_time_step=3,
)
all_sources = hypo_handler.get_generic_sources(SIM['mc_true_params'])
assert len(all_sources) > 0
print('len(all_sources):', len(all_sources))
n_costhetadir = specs[0]['n_costhetadir']
costhetadir_bin_edges = np.linspace(
start=specs[0]['costhetadir_min'],
stop=specs[0]['costhetadir_max'],
num=n_costhetadir,
)
# tables=kwargs['dom_tables_kw']['dom_tables_kind'],
# norm=kwargs['dom_tables_kw']['norm_version'],
# no_dir_str='no_dir_' if not kwargs['dom_tables_kw']['use_directionality'] else '',
# cone_str=(
# 'sigma{}deg_{}phi_'.format(kwargs['dom_tables_kw']['ckv_sigma_deg'], kwargs['dom_tables_kw']['ckv_sigma_deg'])
# if (kwargs['dom_tables_kw']['use_directionality'] and
# kwargs['dom_tables_kw']['dom_tables_kind'] in ['raw_uncompr', 'raw_templ_compr'])
# else ''
# ),
# dedx_str='dedx_' if kwargs['hypo_kw']['track_kernel'] == 'table_e_loss' else '',
# muon_dt=kwargs['hypo_kw']['track_time_step']
#
#)
dom_tables = init_obj.setup_dom_tables(**kwargs['dom_tables_kw'])
hypo_handler = init_obj.setup_discrete_hypo(**kwargs['hypo_kw'])
sim = SIMULATIONS[kwargs['other_kw']['sim_to_test']]
outdir = expand(kwargs['other_kw'].pop('outdir'))
fwd_sim_dir = '/data/icecube/retro/sims/'
sim['fwd_sim_histo_file'] = join(fwd_sim_dir, sim['fwd_sim_histo_file'])
fwd_sim_histo_file_md5 = None
if sim['fwd_sim_histo_file'] is not None:
print('Loading and hashing forward simulation histograms from "%s"...'
% sim['fwd_sim_histo_file'])
contents = open(expand(sim['fwd_sim_histo_file']), 'rb').read()
fwd_sim_histo_file_md5 = hashlib.md5(contents).hexdigest()
fwd_sim_histos = pickle.loads(contents)
bin_edges = fwd_sim_histos['bin_edges']
bin_centers = 0.5 * (bin_edges[:-1] + bin_edges[1:])
t=0,
x=0,
y=0,
z=-400,
track_azimuth=0,
track_zenith=np.pi,
# cascade_azimuth=0, cascade_zenith=np.pi,
track_energy=0,
cascade_energy=20),
fwd_sim_histo_file=
'EMinus_energy20_x0_y0_z-400_cz-1.0_az0_ice_spice_mie_holeice_as.h2-50cm_gcd_md5_14bd15d0_geant_false_nsims1000000_step1_photon_histos_0-4000ns_400bins.pkl'
),
)
dom_tables = init_obj.setup_dom_tables(**dom_tables_kw)
hypo_handler = init_obj.setup_discrete_hypo(**hypo_kw)
sim = SIMULATIONS[pdf_kw['sim_to_test']]
outdir = expand(pdf_kw.pop('outdir'))
fwd_sim_dir = '/data/icecube/retro/sims/'
sim['fwd_sim_histo_file'] = join(fwd_sim_dir, sim['fwd_sim_histo_file'])
fwd_sim_histo_file_md5 = None
if sim['fwd_sim_histo_file'] is not None:
print('Loading and hashing forward simulation histograms from "%s"...' %
sim['fwd_sim_histo_file'])
t0 = time.time()
contents = open(expand(sim['fwd_sim_histo_file']), 'rb').read()
fwd_sim_histo_file_md5 = hashlib.md5(contents).hexdigest()
fwd_sim_histos = pickle.loads(contents)
bin_edges = fwd_sim_histos['bin_edges']
def reco(dom_tables_kw, hypo_kw, events_kw, reco_kw):
"""Script "main" function"""
t00 = time.time()
dom_tables = init_obj.setup_dom_tables(**dom_tables_kw)
hypo_handler = init_obj.setup_discrete_hypo(**hypo_kw)
events_iterator = init_obj.get_events(**events_kw)
print('Running reconstructions...')
spatial_prior_orig = reco_kw.pop('spatial_prior').strip()
spatial_prior_name = spatial_prior_orig.lower()
if spatial_prior_name == 'ic':
x_prior = (PRI_UNIFORM, (-860, 870))
y_prior = (PRI_UNIFORM, (-780, 770))
z_prior = (PRI_UNIFORM, (-780, 790))
elif spatial_prior_name == 'dc':
x_prior = (PRI_UNIFORM, (-150, 270))
y_prior = (PRI_UNIFORM, (-210, 150))
z_prior = (PRI_UNIFORM, (-770, 760))
elif spatial_prior_name == 'dc_subdust':
x_prior = (PRI_UNIFORM, (-150, 270))
y_prior = (PRI_UNIFORM, (-210, 150))
z_prior = (PRI_UNIFORM, (-610, -60))
elif spatial_prior_name == 'spefit2':
x_prior = (
PRI_SPEFIT2,
(
# scipy.stats.cauchy loc, scale parameters
-0.19687812829978152,
14.282171566308806,
# Hard limits
-600,
750))
y_prior = (
PRI_SPEFIT2,
(
# scipy.stats.cauchy loc, scale parameters
-0.2393645701205161,
15.049528023495354,
# Hard limits
-750,
650))
z_prior = (
PRI_SPEFIT2,
(
# scipy.stats.cauchy loc, scale parameters
-5.9170661027492546,
12.089399308036718,
# Hard limits
-1200,
200))
else:
raise ValueError(
'Spatial prior "{}" not recognized'.format(spatial_prior_orig))
temporal_prior_orig = reco_kw.pop('temporal_prior').strip()
temporal_prior_name = temporal_prior_orig.lower()
if temporal_prior_name == PRI_UNIFORM:
time_prior = (PRI_UNIFORM, (-4e3, 0.0))
elif temporal_prior_name == PRI_SPEFIT2:
time_prior = (
PRI_SPEFIT2,
(
# scipy.stats.cauchy loc (rel to SPEFit2 time), scale
-82.631395081663754,
75.619895703067343,
# Hard limits (relative to left, right edges of window,
# respectively)
-4e3,
0.0))
else:
raise ValueError(
'Temporal prior "{}" not recognized'.format(temporal_prior_orig))
energy_prior_name = reco_kw.pop('energy_prior')
energy_lims = reco_kw.pop('energy_lims')
if energy_prior_name == PRI_UNIFORM:
energy_prior = (PRI_UNIFORM, (np.min(energy_lims),
np.max(energy_lims)))
elif energy_prior_name == PRI_LOG_UNIFORM:
energy_prior = (PRI_LOG_UNIFORM, (np.min(energy_lims),
np.max(energy_lims)))
elif energy_prior_name == PRI_LOG_NORMAL:
energy_prior = (
PRI_LOG_NORMAL,
(
# scipy.stats.lognorm 3 paramters
0.96251341305506233,
0.4175592980195757,
17.543915051586644,
# hard limits
np.min(energy_lims),
np.max(energy_lims)))
else:
raise ValueError(str(energy_prior_name))
priors = OrderedDict([('time', time_prior), ('x', x_prior), ('y', y_prior),
('z', z_prior),
('track_zenith', (PRI_COSINE, (-1, 1))),
('track_azimuth', (PRI_UNIFORM, (0, TWO_PI))),
('energy', energy_prior),
('track_fraction', (PRI_UNIFORM, (0, 1)))])
if HYPO_PARAMS_T is HypoParams10D:
priors['cascade_zenith'] = (PRI_COSINE, (-1, 1))
priors['cascade_azimuth'] = (PRI_UNIFORM, (0, TWO_PI))
for event_idx, event in events_iterator: # pylint: disable=unused-variable
t1 = time.time()
if 'mc_truth' in event:
print(event['mc_truth'])
llhp, _ = run_multinest(event_idx=event_idx,
event=event,
dom_tables=dom_tables,
hypo_handler=hypo_handler,
priors=priors,
**reco_kw)
dt = time.time() - t1
n_points = llhp.size
print(' ---> {:.3f} s, {:d} points ({:.3f} ms per LLH)'.format(
dt, n_points, dt / n_points * 1e3))
print('Total script run time is {:.3f} s'.format(time.time() - t00))