def compute_curve(name,pwndata,phimin, phimax, fit_emin):
"""Function to compute the points TS vs (phi range)"""
TS=np.zeros_like(phimin)
print 'First, analyzing unphased data'
roi=setup_pwn(name,pwndata,phase=[0,1], quiet=True, fit_emin=fit_emin)
print 'bin edges:',roi.bin_edges
print roi
gtlike = roi_gtlike.Gtlike(roi)
like=gtlike.like
# for i in range(len(like.model.params)):
# like.freeze(i)
# like[like.par_index(name, 'Prefactor')].setFree(1)
like.fit()
like[like.par_index(name, 'Prefactor')].setBounds(1.0e-5,1.0e5)
print like.model
# freeze index of PWN
index=like[like.par_index(name, 'Index')]
index.setTrueValue(-2)
index.setFree(1)
# fix everything in the ROI except prefactor
#for i in range(len(like.model.params)):
# like.freeze(i)
#like[like.par_index(name, 'Prefactor')].setFree(1)
print like.model
# now, freeze everything else in the ROI:
saved_state = LikelihoodState(gtlike.like)
print "----------------------------------Begin Loop---------------------------------------"
for i,phase in enumerate(zip(phimin,phimax)):
print 'Loop %4d/%4d: phase min=%.2f, max=%.2f' % (i+1,len(phimin),phase[0],phase[1])
roi=setup_pwn(name,pwndata,phase, quiet=True, fit_emin=fit_emin)
gtlike = roi_gtlike.Gtlike(roi)
print gtlike.like.model
# give model the same parameters as global fit.
saved_state.like = gtlike.like
saved_state.restore()
for i in range(len(gtlike.like.model.params)):
gtlike.like.freeze(i)
gtlike.like[gtlike.like.par_index(name, 'Prefactor')].setFree(1)
gtlike.like[like.par_index(name, 'Prefactor')].setBounds(1.0e-5,1.0e5)
gtlike.like.fit()
# n.b. no need to reoptimize since only one parameter
# is fit.
TS[i] = gtlike.like.Ts(name,reoptimize=False)
print 'phase=%s, TS=%s' % (phase,TS[i])
f=open("results_%s.yaml" % name,"w")
yaml.dump(dict(TS=TS[0:i+1].tolist(),
phimin=phimin[0:i+1].tolist(),
phimax=phimax[0:i+1].tolist()),
f)
f.close()
return TS,phimin,phimax
def compute_curve(name,pwndata,phimin, phimax, fit_emin):
"""Function to compute the points TS vs (phi range)"""
TS=np.zeros_like(phimin)
flux=np.zeros_like(phimin)
Npred=np.zeros_like(phimin)
print 'First, analyzing unphased data'
roi=setup_pwn(name,pwndata,phase=[0,1], quiet=True, fit_emin=fit_emin)
print 'bin edges:',roi.bin_edges
print roi
gtlike = roi_gtlike.Gtlike(roi)
like=gtlike.like
for i in range(len(like.model.params)):
like.freeze(i)
like[like.par_index(name, 'Prefactor')].setFree(1)
like[like.par_index(name, 'Prefactor')].setBounds(0.0,1.0e5)
# like.fit()
print like.model
# freeze index of PWN
index=like[like.par_index(name, 'Index')]
index.setTrueValue(-2)
index.setFree(0)
print like.model
# now, freeze everything else in the ROI:
saved_state = LikelihoodState(gtlike.like)
print "----------------------------------Begin Loop---------------------------------------"
for i,phase in enumerate(zip(phimin,phimax)):
print 'Loop %4d/%4d: phase min=%.2f, max=%.2f' % (i+1,len(phimin),phase[0],phase[1])
roi=setup_pwn(name,pwndata,phase, quiet=True, fit_emin=fit_emin)
gtlike = roi_gtlike.Gtlike(roi)
gtlike.like[gtlike.like.par_index(name, 'Prefactor')].setBounds(0.0,1.0e5)
# put the value of flux of the whole phase scaled by the phase_factor.
print roi.phase_factor
print "Model avant modif"
print gtlike.like.model
# give model the same parameters as global fit.
#saved_state.like = gtlike.like
#saved_state.restore()
for j in range(len(gtlike.like.model.params)):
gtlike.like.freeze(j)
gtlike.like[gtlike.like.par_index(name, 'Prefactor')].setFree(1)
gtlike.like[gtlike.like.par_index(name, 'Prefactor')].setBounds(0.0,1.0e5)
gtlike.like[gtlike.like.par_index("Galactic Diffuse (gll_iem_v02.fit)", 'Prefactor')].setBounds(0.0,1.0e5)
gtlike.like["Galactic Diffuse (gll_iem_v02.fit)"].funcs['Spectrum'].getParam('Prefactor').setValue(1.0*roi.phase_factor)
gtlike.like[gtlike.like.par_index("Isotropic Diffuse (isotropic_iem_v02.txt)", 'Normalization')].setBounds(0.0,1.0e5)
gtlike.like["Isotropic Diffuse (isotropic_iem_v02.txt)"].funcs['Spectrum'].getParam('Normalization').setValue(1.0*roi.phase_factor)
print "Model apres modif"
print gtlike.like.model
gtlike.like.fit()
outputModel = "output_model_%s_%s_%s.xml" %(name,phase[0], phase[1])
print "Model apres fit"
print gtlike.like.model
# n.b. no need to reoptimize since only one parameter
# is fit.
print i
print len(TS)
TS[i] = gtlike.like.Ts(name,reoptimize=True)
flux[i]=gtlike.like.flux(name,1.0e2,1.0e5)
Npred[i]=gtlike.like.logLike.NpredValue(name)
print 'phase=%s, TS=%s' % (phase,TS[i])
f=open("results_%s.yaml" % name,"w")
yaml.dump(dict(TS=TS[0:i+1].tolist(),
phimin=phimin[0:i+1].tolist(),
phimax=phimax[0:i+1].tolist(),
flux=flux[0:i+1].tolist(),
Npred=Npred[0:i+1].tolist()),
f)
f.close()
return TS,phimin,phimax
