def plec():
index, a, b = ce.PLEC_Index, ce.PLEC_Expfactor, ce.PLEC_Exp_Index
cutoff = (1 / a)**(1 / b)
prefactor = flux * np.exp(a * pivot**b)
return Models.PLSuperExpCutoff(p=[prefactor, index, cutoff, b],
e0=pivot,
free=free)
def model(st, flux, index, beta, pivot, cutoff, b):
if st == 'PowerLaw':
return Models.PowerLaw(p=[flux, index], e0=pivot)
elif st == 'PLSuperExpCutoff':
prefactor = flux * np.exp((pivot / cutoff)**b)
return Models.PLSuperExpCutoff(p=[prefactor, index, cutoff, b],
e0=pivot)
elif st == 'PLExpCutoff':
prefactor = flux * np.exp((pivot / cutoff))
return Models.PLSuperExpCutoff(
p=[prefactor, index, cutoff, 1.0], e0=pivot)
elif st == 'LogParabola':
return Models.LogParabola(p=[flux, index, beta, pivot])
elif st == 'PowerLaw2': ### same as PowerLaw in table
return Models.PowerLaw(p=[flux, index], e0=pivot)
else:
raise Exception(
'unexpected spectrum type {}, source name {}'.format(
st, name))
def pmodel(source):
""" create a pointlike model from a Series object from DataFrame row
"""
expandbits = lambda x: [(x & 2**i) != 0 for i in range(4)]
modelname, freebits, e0, norm, norm_unc, pindex, pindex_unc, index2,index2_unc, cutoff, cutoff_unc=\
[source[x] for x in 'modelname freebits e0 flux flux_unc pindex pindex_unc index2 index2_unc cutoff cutoff_unc'.split()]
free = expandbits(freebits)
errors = [norm_unc, pindex_unc]
if modelname == 'LogParabola':
if np.abs(index2) < 2e-3:
modelname = 'PowerLaw'
model = Models.PowerLaw(p=[norm, pindex], e0=e0)
else:
# if index2<0:
# print 'Source %s has beta (%.2f) <0: setting to 0.' % ( source.name, index2, )
# index2=0
model = Models.LogParabola(p=[norm, pindex, index2, e0])
model.free[-1] = False
errors.append(index2_unc)
elif modelname == 'PLSuperExpCutoff':
prefactor = np.exp(-(e0 / cutoff)**index2)
model = Models.PLSuperExpCutoff(
p=[norm / prefactor, pindex, cutoff, index2], e0=e0)
errors[0] /= prefactor
errors += [cutoff_unc, index2_unc]
elif modelname == 'ExpCutoff':
prefactor = np.exp(-(e0 / cutoff))
model = Models.PLSuperExpCutoff(
p=[norm / prefactor, pindex, cutoff, 0.], e0=e0)
model.free[3] == False
errors[0] /= prefactor
errors += [cutoff_unc]
else:
raise Exception('model "%s" not recognized' % modelname)
map(model.set_error, range(len(errors)), errors)
model.free[:] = free[:model.len()]
return model
def PLSuperExpCutoff(*pars):
model = Models.PLSuperExpCutoff(p=pars, free={True, True, False, False})
sources.set_default_bounds(model)
return model
def PSR_default():
return Models.PLSuperExpCutoff(p=(1e-13, 1.25, 1500, 0.67),
free=[True, True, True, False])
def PLSuperExpCutoff(*pars, **kw):
return Models.PLSuperExpCutoff(p=pars, **kw)
def PLSuperExpCutoff(*pars):
model = Models.PLSuperExpCutoff(p=pars, free=[True, True, False, False])
return model
