# //////////////////////////////////////////////////////////////////////
# For each trial, generate two pseudo-data experiemnts (one for each
# hierarchy), and for each find the best matching template in each of the
# hierarchy hypothesis.
# //////////////////////////////////////////////////////////////////////
results = {}
for data_tag, data_normal in [('data_NMH',True),('data_IMH',False)]:
results[data_tag] = {}
# 0) get a random seed and store with the data
results[data_tag]['seed'] = get_seed()
# 1) get a pseudo data fmap from fiducial model (best fit vals of params).
fmap = get_pseudo_data_fmap(template_maker,
get_values(select_hierarchy(params,
normal_hierarchy=data_normal)),
seed=results[data_tag]['seed'])
# 2) find max llh (and best fit free params) from matching pseudo data
# to templates.
for hypo_tag, hypo_normal in [('hypo_NMH',True),('hypo_IMH',False)]:
physics.info("Finding best fit for %s under %s assumption"%(data_tag,hypo_tag))
profile.info("start optimizer")
llh_data = find_max_llh_bfgs(fmap,template_maker,params,
minimizer_settings,args.save_steps,normal_hierarchy=hypo_normal)
profile.info("stop optimizer")
#Store the LLH data
results[data_tag][hypo_tag] = llh_data
for itrial in xrange(1, args.ntrials + 1):
profile.info("start trial %d" % itrial)
logging.info(">" * 10 + "Running trial: %05d" % itrial + "<" * 10)
# //////////////////////////////////////////////////////////////////////
# For each trial, generate two pseudo-data experiemnts (one for each
# hierarchy), and for each find the best matching template in each of the
# hierarchy hypothesis.
# //////////////////////////////////////////////////////////////////////
results = {}
for data_tag, data_normal in [("data_NMH", True), ("data_IMH", False)]:
results[data_tag] = {}
# 1) get a pseudo data fmap from fiducial model (best fit vals of params).
fmap = get_pseudo_data_fmap(template_maker, get_values(select_hierarchy(params, normal_hierarchy=data_normal)))
# 2) find max llh (and best fit free params) from matching pseudo data
# to templates.
for hypo_tag, hypo_normal in [("hypo_NMH", True), ("hypo_IMH", False)]:
physics.info("Finding best fit for %s under %s assumption" % (data_tag, hypo_tag))
profile.info("start scan")
llh_data = find_max_grid(
fmap, template_maker, params, grid_settings, args.save_steps, normal_hierarchy=hypo_normal
)
profile.info("stop scan")
# Store the LLH data
results[data_tag][hypo_tag] = llh_data
# For each trial, generate two pseudo-data experiemnts (one for each
# hierarchy), and for each find the best matching template in each of the
# hierarchy hypotheses.
# //////////////////////////////////////////////////////////////////////
results = {}
for data_tag, data_normal in [('data_NMH', True), ('data_IMH', False)]:
results[data_tag] = {}
# 0) get a random seed and store with the data
results[data_tag]['seed'] = get_seed()
logging.info(" RNG seed: %ld" % results[data_tag]['seed'])
# 1) get a pseudo data fmap from fiducial model (best fit vals of params).
fmap = get_pseudo_data_fmap(pseudo_data_template_maker,
get_values(
select_hierarchy(
pseudo_data_settings['params'],
normal_hierarchy=data_normal)),
seed=results[data_tag]['seed'],
chan=channel)
# 2) find max llh (and best fit free params) from matching pseudo data
# to templates.
for hypo_tag, hypo_normal in [('hypo_NMH', True),
('hypo_IMH', False)]:
physics.info("Finding best fit for %s under %s assumption" %
(data_tag, hypo_tag))
with Timer() as t:
llh_data = find_max_llh_bfgs(fmap,
template_maker,
template_settings['params'],
# //////////////////////////////////////////////////////////////////////
# For each trial, generate two pseudo-data experiemnts (one for each
# hierarchy), and for each find the best matching template in each of the
# hierarchy hypotheses.
# //////////////////////////////////////////////////////////////////////
results = {}
for data_tag, data_normal in [('data_NMH',True),('data_IMH',False)]:
results[data_tag] = {}
# 0) get a random seed and store with the data
results[data_tag]['seed'] = get_seed()
logging.info(" RNG seed: %ld"%results[data_tag]['seed'])
# 1) get a pseudo data fmap from fiducial model (best fit vals of params).
fmap = get_pseudo_data_fmap(
pseudo_data_template_maker,
get_values(select_hierarchy(pseudo_data_settings['params'],
normal_hierarchy=data_normal)),
seed=results[data_tag]['seed'],chan=channel)
# 2) find max llh (and best fit free params) from matching pseudo data
# to templates.
for hypo_tag, hypo_normal in [('hypo_NMH',True),('hypo_IMH',False)]:
physics.info("Finding best fit for %s under %s assumption"%(data_tag,hypo_tag))
with Timer() as t:
llh_data = find_max_llh_bfgs(fmap,template_maker,template_settings['params'],
minimizer_settings,args.save_steps,
normal_hierarchy=hypo_normal)
profile.info("==> elapsed time for optimizer: %s sec"%t.secs)
# Store the LLH data
logging.info(">"*10 + "Running trial: %05d"%itrial + "<"*10)
# //////////////////////////////////////////////////////////////////////
# For each trial, generate two pseudo-data experiemnts (one for each
# hierarchy), and for each find the best matching template in each of the
# hierarchy hypothesis.
# //////////////////////////////////////////////////////////////////////
results = {}
for data_tag, data_normal in [('data_NMH',True),('data_IMH',False)]:
results[data_tag] = {}
# 1) get a pseudo data fmap from fiducial model (best fit vals of params).
fiducial_param_values = get_values(select_hierarchy(params, normal_hierarchy=data_normal))
fmap = get_pseudo_data_fmap(template_maker=template_maker,
fiducial_params=fiducial_param_values,
channel=fiducial_param_values['channel'])
# 2) find max llh (and best fit free params) from matching pseudo data
# to templates.
for hypo_tag, hypo_normal in [('hypo_NMH',True),('hypo_IMH',False)]:
physics.info("Finding best fit for %s under %s assumption"%(data_tag,hypo_tag))
profile.info("start scan")
llh_data = find_max_grid(fmap=fmap,
template_maker=template_maker,
params=params,
grid_settings=grid_settings,
save_steps=args.save_steps,
normal_hierarchy=hypo_normal)
profile.info("stop scan")
for itrial in xrange(1, args.ntrials + 1):
profile.info("start trial %d" % itrial)
logging.info(">" * 10 + "Running trial: %05d" % itrial + "<" * 10)
# //////////////////////////////////////////////////////////////////////
# For each trial, generate two pseudo-data experiemnts (one for each
# hierarchy), and for each find the best matching template in each of the
# hierarchy hypothesis.
# //////////////////////////////////////////////////////////////////////
results = {}
for data_tag, data_normal in [('data_NMH', True), ('data_IMH', False)]:
results[data_tag] = {}
# 1) get a pseudo data fmap from fiducial model (best fit vals of params).
fmap = get_pseudo_data_fmap(
template_maker,
get_values(select_hierarchy(params, normal_hierarchy=data_normal)))
# 2) find max llh (and best fit free params) from matching pseudo data
# to templates.
for hypo_tag, hypo_normal in [('hypo_NMH', True), ('hypo_IMH', False)]:
physics.info("Finding best fit for %s under %s assumption" %
(data_tag, hypo_tag))
profile.info("start scan")
llh_data = find_max_grid(fmap,
template_maker,
params,
grid_settings,
args.save_steps,
normal_hierarchy=hypo_normal)
