def createStepList(params, true_normal, grid_settings):
"""
No matter how many params are listed as free, force only theta23 to be free.
"""
new_params = fixAllButTheta23(params)
free_params = select_hierarchy(get_free_params(new_params),
normal_hierarchy=true_normal)
fixed_params = select_hierarchy(get_fixed_params(new_params),
normal_hierarchy=true_normal)
calc_steps(free_params, grid_settings['steps'])
# Form list from all parameters that holds a list of (name,step) tuples.
steplist = free_params['theta23']['steps']
return steplist
def createStepList(params,true_normal,grid_settings):
"""
No matter how many params are listed as free, force only theta23 to be free.
"""
new_params = fixAllButTheta23(params)
free_params = select_hierarchy(get_free_params(new_params),
normal_hierarchy=true_normal)
fixed_params = select_hierarchy(get_fixed_params(new_params),
normal_hierarchy=true_normal)
calc_steps(free_params, grid_settings['steps'])
# Form list from all parameters that holds a list of (name,step) tuples.
steplist = free_params['theta23']['steps']
return steplist
results[data_tag] = {}
data_params = select_hierarchy(params,normal_hierarchy=data_normal)
asimov_data_set = get_asimov_fmap(template_maker,get_values(data_params),
chan=channel)
results[data_tag]['asimov_data'] = asimov_data_set
hypo_types = [('hypo_NMH',True)]
#hypo_types = [('hypo_NMH',True),('hypo_IMH',False)]
for hypo_tag, hypo_normal in hypo_types:
hypo_params = select_hierarchy(params,normal_hierarchy=hypo_normal)
# Now scan over theta23,deltam31 values and fix params to
# these values:
# Calculate steps for all free parameters
atm_params = get_atm_params(hypo_params)
calc_steps(atm_params, grid_settings['steps'])
# Build a list from all parameters that holds a list of (name, step) tuples
steplist = [ [(name,step) for step in param['steps']]
for name, param in sorted(atm_params.items())]
print "steplist: ",steplist
print "atm_params: ",atm_params
# Prepare to store all the steps
steps = {key:[] for key in atm_params.keys()}
steps['llh'] = []
# Iterate over the cartesian product, and set fixed parameter to value
for pos in product(*steplist):
pos_dict = dict(list(pos))
results[data_tag] = {}
data_params = select_hierarchy(params,normal_hierarchy=data_normal)
asimov_data_set = get_asimov_fmap(template_maker,get_values(data_params),
chan=channel)
results[data_tag]['asimov_data'] = asimov_data_set
hypo_types = [('hypo_NMH',True)]
#hypo_types = [('hypo_NMH',True),('hypo_IMH',False)]
for hypo_tag, hypo_normal in hypo_types:
hypo_params = select_hierarchy(params,normal_hierarchy=hypo_normal)
# Now scan over theta23,deltam31 values and fix params to
# these values:
# Calculate steps for all free parameters
atm_params = get_atm_params(hypo_params)
calc_steps(atm_params, grid_settings['steps'])
# Build a list from all parameters that holds a list of (name, step) tuples
steplist = [ [(name,step) for step in param['steps']]
for name, param in sorted(atm_params.items())]
print "steplist: ",steplist
print "atm_params: ",atm_params
# Prepare to store all the steps
steps = {key:[] for key in atm_params.keys()}
steps['llh'] = []
# Iterate over the cartesian product, and set fixed parameter to value
for pos in product(*steplist):
pos_dict = dict(list(pos))
