def getAltHierarchyBestFit(asimov_data, template_maker, params, minimizer_settings,
hypo_normal, check_octant):
"""
Finds the best fit value of alternative hierarchy to that which
was used to produce the asimov data set.
\Params:
* asimov_data - array of values of asimov data set (float)
* template_maker - instance of class TemplateMaker service.
* params - parameters with values, fixed, range, etc. of systematics
* minimizer_settings - used with bfgs_b minimizer
* hypo_normal - bool for Mass hierarchy being Normal (True)
or inverted (False)
* check_octant - bool to check the opposite octant for a solution
to the minimization of the LLH.
"""
llh_data = find_alt_hierarchy_fit(
asimov_data, template_maker, params, hypo_normal,
minimizer_settings, only_atm_params=True, check_octant=check_octant)
alt_params = get_values(select_hierarchy(params, normal_hierarchy=hypo_normal))
for key in llh_data.keys():
if key == 'llh': continue
alt_params[key] = llh_data[key][-1]
return alt_params, llh_data
def getAltHierarchyBestFit(asimov_data, template_maker, params,
minimizer_settings, hypo_normal, check_octant):
"""
Finds the best fit value of alternative hierarchy to that which
was used to produce the asimov data set.
\Params:
* asimov_data - array of values of asimov data set (float)
* template_maker - instance of class TemplateMaker service.
* params - parameters with values, fixed, range, etc. of systematics
* minimizer_settings - used with bfgs_b minimizer
* hypo_normal - bool for Mass hierarchy being Normal (True)
or inverted (False)
* check_octant - bool to check the opposite octant for a solution
to the minimization of the LLH.
"""
llh_data = find_alt_hierarchy_fit(asimov_data,
template_maker,
params,
hypo_normal,
minimizer_settings,
only_atm_params=True,
check_octant=check_octant)
alt_params = get_values(
select_hierarchy(params, normal_hierarchy=hypo_normal))
for key in llh_data.keys():
if key == 'llh': continue
alt_params[key] = llh_data[key][-1]
return alt_params, llh_data
asimov_data_set = get_asimov_fmap(template_maker,
asimov_params,
channel=asimov_params['channel'])
# Store injected true values in result:
for key in free_params.keys():
if 'theta23' in key: continue
result['true_' + key].append(asimov_params[key])
result['true_theta23'].append(step)
result['asimov_data'].append(asimov_data_set)
# now get fitted values of opposite hierarchy:
hypo_normal = False if true_normal else True
hypo_tag = 'hypo_IMH' if true_normal else 'hypo_NMH'
llh_data = find_alt_hierarchy_fit(asimov_data_set,
template_maker,
params,
hypo_normal,
minimizer_settings,
only_atm_params=False,
check_octant=args.check_octant)
for key in free_params.keys():
result['fit_' + key].append(llh_data[key][-1])
results[true_tag] = result
logging.warn("FINISHED. Saving to file: %s" % args.outfile)
to_json(results, args.outfile)
# the asimov data set:
for step in steplist:
print "Running at asimov parameters: %s"%step
asimov_params = get_values(getAsimovParams(params,true_normal,step))
asimov_data_set = get_asimov_fmap(
template_maker, asimov_params,
chan=asimov_params['channel'])
# Store injected true values in result:
for key in free_params.keys():
if 'theta23' in key: continue
result['true_'+key].append(asimov_params[key])
result['true_theta23'].append(step)
result['asimov_data'].append(asimov_data_set)
# now get fitted values of opposite hierarchy:
hypo_normal = False if true_normal else True
hypo_tag = 'hypo_IMH' if true_normal else 'hypo_NMH'
llh_data = find_alt_hierarchy_fit(
asimov_data_set,template_maker, params, hypo_normal,
minimizer_settings, only_atm_params=False, check_octant=args.check_octant)
for key in free_params.keys(): result['fit_'+key].append(llh_data[key][-1])
results[true_tag] = result
logging.warn("FINISHED. Saving to file: %s"%args.outfile)
to_json(results,args.outfile)
