def __init__(self, roi, nbands, folder):
""" fit the front and back"""
from uw.like import Models
iso_source = roi.get_source('isotrop')
old_model = iso_source.model.copy()
roi.sources.set_model(Models.FrontBackConstant(), 'isotrop')
iso_model = iso_source.model
roi.reinitialize()
cx = []
dx = []
for eband in range(nbands):
roi.select(eband)
print '*** Energy Band {}: iso counts {}'.format(
eband, [t[1].counts.round() for t in roi.selected])
iso_model[0] = 1
iso_model[1] = 1
n = len(roi.selected)
roi.fit(range(n))
u = iso_model.get_all_parameters()
du = np.array([iso_model.error(i) for i in range(2)])
cx.append(u)
dx.append(du)
roi.select()
roi.sources.set_model(old_model)
if folder is not None:
if not os.path.exists(folder): os.mkdir(folder)
filename = '{}/{}.pickle'.format(folder, roi.name)
pickle.dump(dict(val=cx, err=dx), open(filename, 'w'))
print 'wrote file {}'.format(filename)
self.val = cx
self.err = dx
def fit_isotropic(roi, nbands=8, folder='isotropic_fit'):
""" fit only the front and back"""
from uw.like import Models
iso_source = roi.get_source('isotrop')
old_model=iso_source.model.copy()
roi.sources.set_model(Models.FrontBackConstant(), 'isotrop')
iso_model=iso_source.model
roi.reinitialize()
cx = []
for eband in range(nbands):
print '*** Energy Band {}: iso counts {}'.format( eband,
[t[1].counts.round() for t in roi[2*eband:2*eband+2]])
roi.select(eband)
iso_model[0]=1; iso_model[1]=1
roi.fit([0,1]);
u = iso_model.get_all_parameters();
cx.append(u)
roi.select()
roi.sources.set_model(old_model)
if folder is not None:
if not os.path.exists(folder): os.mkdir(folder)
filename= '{}/{}.pickle'.format(folder, roi.name)
pickle.dump(np.array(cx), open(filename, 'w'))
print 'wrote file {}'.format(filename)
return np.array(cx)
def fit_diffuse(roi, nbands=8, select=[0,1,2], restore=False, folder='diffuse_fit'):
"""
Perform indpendent fits to the gal, iso_front, and iso_back for each of the first nbands bands
select: None or list of variables
"""
from uw.like import Models
# freeze all free sources, thaw gal and iso
roi.thaw('Norm', 'ring')
iso_model =roi.sources.find_source('isotrop').model.copy()
roi.sources.set_model(Models.FrontBackConstant(), 'isotrop')
roi.reinitialize()
# do the fitting
dpars=[]
energies = []
for ie in range(nbands):
roi.select(ie);
roi.fit(select, ignore_exception=True)
energies.append(int(roi.energies[0]))
dpars.append( roi.sources.parameters.get_parameters()[:3])
t =np.power(10, dpars)
df = pd.DataFrame(t, columns=['gal iso_front iso_back'.split()])
df.index=energies
if restore:
# does not seem to work, comment this out for now
# restore sources
roi.sources.diffuse_normalization *= df
for s,f in zip(free_sources, saved_free):
s.model.free=f
roi.sources.find_source('ring').model.free[0]=False
roi.sources.set_model(iso_model, 'isotrop')
roi.reinitialize()
roi.select()
roi.fit() # needed to restore gradient, at least.
# update the pickle file
write_pickle(roi)
elif folder is not None:
# simply save results
if not os.path.exists(folder):
os.mkdir(folder)
filename= '{}/{}.pickle'.format(folder, roi.name)
pickle.dump(df, open(filename, 'w'))
print 'wrote file {}'.format(filename)
return df
def FBconstant(f, b, **kw):
return Models.FrontBackConstant(f, b, **kw)
