def get_llh_hypothesis(
data_tag, asimov_data, ntrials, template_maker, template_params,
minimizer_settings, save_steps, check_octant):
"""
Runs the llh fitter ntrials number of times, pulling pseudo data sets from
asimov_data.
\Params:
* data_tag - hierarchy type running for assumed true.
* asimov_data - asimov (unfluctuated) data from which to generate poisson
fluctuated pseudo data
* ntrials - number of trials to run for each hierarchy hypothesis
* template_maker - instance of TemplateMaker class, from which to fit pseudo
data to
* template_params - dictionary of parameters at which to test the pseudo
data and find the best match llh
* minimizer_settings - settings for bfgs minimizer in llh fit
* save_steps - flag to save the optimizer steps
* check_octant - boolean to check both octants of theta23
\returns - trials list that holds the dictionaries of llh results.
"""
trials = []
for itrial in xrange(1,ntrials+1):
results = {} # one trial of results
tprofile.info("start trial %d"%itrial)
logging.info(">"*10 + "Running trial: %05d"%itrial + "<"*10)
results['seed'] = get_seed()
logging.info(" RNG seed: %ld"%results['seed'])
# Get random map generated from asimov data (or from data_tag).
fmap = get_random_map(asimov_data, seed=results['seed'])
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_params,
minimizer_settings, save_steps,
normal_hierarchy=hypo_normal, check_octant=check_octant)
tprofile.info("==> elapsed time for optimizer: %s sec"%t.secs)
# Store the LLH data
results[hypo_tag] = llh_data
trials += [results]
tprofile.info("stop trial %d"%itrial)
return trials
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] = {}
# 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")
select_hierarchy(template_settings['params'],
normal_hierarchy= (not data_normal)))
alt_mh_expectation = get_asimov_fmap(
template_maker, null_settings, channel=null_settings['channel'] )
else:
alt_mh_expectation = asimov_data_null
trials = []
for itrial in xrange(1,args.ntrials+1):
results = {} # one trial of results
tprofile.info("start trial %d"%itrial)
logging.info(">"*10 + "Running trial: %05d"%itrial + "<"*10)
results['seed'] = get_seed()
logging.info(" RNG seed: %ld"%results['seed'])
fmap = get_random_map(alt_mh_expectation, seed=results['seed'])
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_opt_bfgs(
fmap, template_maker, template_settings['params'],
minimizer_settings, args.save_steps,
normal_hierarchy=hypo_normal, check_octant=check_octant)
tprofile.info("==> elapsed time for optimizer: %s sec"%t.secs)
# Store the LLH data
try:
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 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)]:
output[data_tag] = {}
# Get Asimov data set for assuming true: data_tag, and store for
# later comparison
asimov_data = getAsimovData(
template_maker, template_settings['params'], data_normal)
output[data_tag]['asimov_data'] = asimov_data
trials = []
for itrial in xrange(1,args.ntrials+1):
results = {} # one trial of results
tprofile.info("start trial %d"%itrial)
logging.info(">"*10 + "Running trial: %05d"%itrial + "<"*10)
results['seed'] = get_seed()
logging.info(" RNG seed: %ld"%results['seed'])
# Get random map generated from asimov data (or from data_tag).
fmap = get_random_map(asimov_data, seed=results['seed'])
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_opt_bfgs(
fmap, template_maker, template_settings['params'],
minimizer_settings, args.save_steps,
normal_hierarchy=hypo_normal, check_octant=check_octant)
tprofile.info("==> elapsed time for optimizer: %s sec"%t.secs)
