def get_most_significant_diffuse(roi, allowed_fraction,max_n_models=1, verbosity=False):
from lande.fermi.likelihood.save import get_background
significant_list = []
for name in get_background(roi):
oc=observed_counts(roi)
mc=model_counts(roi,name)
fraction=float(mc)/oc
if verbosity:
print ' .. Source %s predicts %0.2f%% of total counts' % (name,fraction)
if fraction > allowed_fraction:
significant_list.append((name,)+(fraction,))
if verbosity: print 'keeping it.'
else:
if verbosity: print 'discarding it.'
sorted_list=sorted(significant_list, key=lambda frac: frac[1], reverse= True)
cut_list=[]
for i in sorted_list[:max_n_models]:
cut_list.append(i[0])
if verbosity:
print significant_list
print sorted_list
print cut_list
return cut_list
def get_most_significant_diffuse(roi, allowed_fraction,max_n_models=1, verbosity=False):
from lande.fermi.likelihood.save import get_background
significant_list = []
for name in get_background(roi):
oc=observed_counts(roi)
mc=model_counts(roi,name)
fraction=float(mc)/oc
if verbosity:
print ' .. Source %s predicts %0.2f%% of total counts' % (name,fraction)
if fraction > allowed_fraction:
significant_list.append((name,)+(fraction,))
if verbosity: print 'keeping it.'
else:
if verbosity: print 'discarding it.'
sorted_list=sorted(significant_list, key=lambda frac: frac[1], reverse= True)
cut_list=[]
for i in sorted_list[:max_n_models]:
cut_list.append(i[0])
if verbosity:
print significant_list
print sorted_list
print cut_list
return cut_list
def is_significant(roi, name, allowed_fraction, verbosity=False):
oc=observed_counts(roi)
mc=model_counts(roi,name)
fraction=float(mc)/oc
if verbosity:
print ' .. Source %s predicts %0.2f%% of total counts' % (name,fraction)
if fraction < allowed_fraction:
return False
else:
return True
