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
def get_pseudo_data_fmap(template_maker,fiducial_params,seed=None):
'''
Creates a true template from fiducial_params, then uses Poisson statistics
to vary the expected counts per bin to create a pseudo data set.
If seed is provided, the random state is seeded with seed before the map is
created.
IMPORTANT: returns a SINGLE flattened map of trck/cscd combined
'''
true_template = template_maker.get_template(fiducial_params)
true_fmap = flatten_map(true_template)
fmap = get_random_map(true_fmap, seed=seed)
return fmap
def get_pseudo_data_fmap(template_maker, fiducial_params, channel, seed=None):
"""
Creates a true template from fiducial_params, then uses Poisson statistics
to vary the expected counts per bin to create a pseudo data set.
If seed is provided, the random state is seeded with seed before the map is
created.
IMPORTANT: returns a SINGLE flattened map of trck/cscd combined
\params:
* channel = channel of flattened fmap to use.
if 'all': returns a single flattened map of trck/cscd combined.
if 'cscd' or 'trck' only returns the channel requested.
"""
true_template = template_maker.get_template(fiducial_params)
true_fmap = flatten_map(true_template, channel=channel)
fmap = get_random_map(true_fmap, seed=seed)
return fmap
def get_pseudo_data_fmap(template_maker, fiducial_params, channel, seed=None):
"""
Creates a true template from fiducial_params, then uses Poisson statistics
to vary the expected counts per bin to create a pseudo data set.
If seed is provided, the random state is seeded with seed before the map is
created.
IMPORTANT: returns a SINGLE flattened map of trck/cscd combined
\params:
* channel = channel of flattened fmap to use.
if 'all': returns a single flattened map of trck/cscd combined.
if 'cscd' or 'trck' only returns the channel requested.
"""
true_template = template_maker.get_template(fiducial_params)
true_fmap = flatten_map(true_template, channel=channel)
fmap = get_random_map(true_fmap, seed=seed)
return fmap
output[dkey][hkey][key] = 0
if key not in ['theta23','deltam31','llh']:
names.append(key)
for AtmOscBin in all_data[dkey][hkey]:
if AtmOscBin['theta23'][0] == theta23tocheck and AtmOscBin['deltam31'][0] == deltam31tocheck:
for key in AtmOscBin.keys():
output[dkey][hkey][key] = AtmOscBin[key][0]
asimov_data = getAsimovData(template_maker,
template_settings['params'],
data_normal)
if output['seed'] is not None:
fmap = get_random_map(asimov_data,
seed=output['seed'])
else:
print "Using Asimov Data"
fmap = asimov_data
for itrial in xrange(1,2):
results = {}
results[dkey] = {}
tprofile.info("start trial %d"%itrial)
logging.info(">"*10 + "Running trial: %05d"%itrial + "<"*10)
results[dkey][hkey] = {}
for Tname in names:
for key in output[dkey][hkey].keys():
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
results[hypo_tag] = llh_data
# 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)
# Store the LLH data
results[hypo_tag] = llh_data
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)
