def make_xml(nametag):
spatial = gammalib.GModelSpatialDiffuseCube(
gammalib.GFilename(nametag + '_map.fits'))
spectral = gammalib.GModelSpectralConst(1.)
model = gammalib.GModelSky(spatial, spectral)
model.name(nametag)
# fill to model container and write to disk
models = gammalib.GModels()
models.append(model)
models.save(nametag + '.xml')
def save(self):
"""
Save the cube and optionally the updated XML model file
"""
# Save the generated cube if the cube and filename are not empty
if ((not self._cubegen.mapcube().cube().is_empty()) and
self['outcube'].is_valid()):
self._cubegen.mapcube().save(self['outcube'].filename(),
self['clobber'].boolean())
# If requested, save the updated list of models
if self['outmodel'].is_valid():
# Generate a list of models that will be output to file
outmodels = gammalib.GModels(self._models)
# Remove all models used in the generated cube
for model in self._cubemodels:
outmodels.remove(model.name())
# Generate the actual cube model
spat = gammalib.GModelSpatialDiffuseCube(self['outcube'].filename())
spec = gammalib.GModelSpectralConst()
newmodel = gammalib.GModelSky(spat, spec)
# Give the model a default name
newmodel.name(self.cubemodelname())
# Now append the model to the list of output models
outmodels.append(newmodel)
# Save the model list
outmodels.save(self['outmodel'].filename())
# Return
return
def _generate_initial_model(self, sigma_min=2.0, sigma_max=1000.0):
"""
Generate initial background model
Parameters
----------
sigma_min : float, optional
Minimum sigma
sigma_max : float, optional
Maximum sigma
Returns
-------
model : `~gammalib.GModelData()`
Background model
"""
# Handle IRF model
if self['spatial'].string() == 'IRF':
epivot = gammalib.GEnergy(1.0, 'TeV')
spectral = gammalib.GModelSpectralPlaw(1.0, 0.0, epivot)
model = gammalib.GCTAModelIrfBackground(spectral)
# Handle AEFF model
elif self['spatial'].string() == 'AEFF':
epivot = gammalib.GEnergy(1.0, 'TeV')
spectral = gammalib.GModelSpectralPlaw(1.0e-13, -2.5, epivot)
model = gammalib.GCTAModelAeffBackground(spectral)
# Handle other models
else:
# Handle LOOKUP model
if self['spatial'].string() == 'LOOKUP':
# Set spatial model
factor1 = gammalib.GCTAModelSpatialLookup(
self['slufile'].filename())
# Handle GAUSS model
elif self['spatial'].string() == 'GAUSS':
# Set spatial model
factor1 = gammalib.GCTAModelRadialGauss(3.0)
factor1['Sigma'].min(sigma_min)
factor1['Sigma'].max(sigma_max)
# Handle GAUSS(E) model
elif self['spatial'].string() == 'GAUSS(E)':
# Set spatial model. If a single energy bin is specified then
# use the GAUSS model. Otherwise allocate a node spectrum for
# the energy nodes of GAUSS(E).
if self['snumbins'].integer() == 1:
factor1 = gammalib.GCTAModelRadialGauss(3.0)
factor1['Sigma'].min(sigma_min)
factor1['Sigma'].max(sigma_max)
else:
emin = gammalib.GEnergy(self['smin'].real(), 'TeV')
emax = gammalib.GEnergy(self['smax'].real(), 'TeV')
energies = gammalib.GEnergies(self['snumbins'].integer(),
emin, emax)
spectrum = gammalib.GModelSpectralConst(3.0)
nodes = gammalib.GModelSpectralNodes(spectrum, energies)
for i in range(nodes.nodes()):
nodes[i * 2 + 1].min(sigma_min)
nodes[i * 2 + 1].max(sigma_max)
nodes.autoscale()
factor1 = gammalib.GCTAModelSpatialGaussSpectrum(nodes)
# Handle PROFILE model
elif self['spatial'].string() == 'PROFILE':
# Set spatial model
factor1 = gammalib.GCTAModelRadialProfile(2.0, 4.0, 5.0)
factor1['Width'].min(1.0)
factor1['Width'].max(10.0)
factor1['Core'].min(1.0)
factor1['Core'].max(10.0)
factor1['Tail'].min(1.0)
factor1['Tail'].max(10.0)
factor1['Tail'].free()
# Handle POLYNOM model
elif self['spatial'].string() == 'POLYNOM':
# Set spatial model
factor1 = gammalib.GCTAModelRadialPolynom([1.0, -0.1, +0.1])
factor1['Coeff0'].min(0.1)
factor1['Coeff0'].max(10.0)
factor1['Coeff0'].fix()
factor1['Coeff1'].min(-10.0)
factor1['Coeff1'].max(10.0)
factor1['Coeff2'].min(-10.0)
factor1['Coeff2'].max(10.0)
# Any other strings (should never occur)
else:
model = None
# Optionally add gradient
if self['gradient'].boolean():
spatial = gammalib.GCTAModelSpatialMultiplicative()
factor2 = gammalib.GCTAModelSpatialGradient()
spatial.append(factor1)
spatial.append(factor2)
else:
spatial = factor1
# Set spectral model
epivot = gammalib.GEnergy(1.0, 'TeV')
spectral = gammalib.GModelSpectralPlaw(3.0e-4, -1.5, epivot)
spectral['Prefactor'].min(1.0e-8)
# Set background model
model = gammalib.GCTAModelBackground(spatial, spectral)
# Set background name and instrument
if model is not None:
model.name('Background')
model.instruments(self._instrument)
# Return model
return model
def _background_spectrum(self, prefactor, index, emin=0.01, emax=100.0):
"""
Create a background spectrum model dependent on user parameters
Parameters
----------
prefactor : float
Power law prefactor of spectral model
index : float
Power law index of spectral model
emin : float
Minimum energy (in case a spectral node function is required)
emax : float
Maximum energy (in case a spectral node function is required)
Returns
-------
spec : `~gammalib.GModelSpectral()`
Spectral model for the background shape
"""
# Handle constant spectral model
if index == 0.0 and self._bkgpars <= 1:
spec = gammalib.GModelSpectralConst()
# Set parameter range
spec['Normalization'].min(prefactor / self._bkg_range_factor)
spec['Normalization'].max(prefactor * self._bkg_range_factor)
spec['Normalization'].value(prefactor)
# Freeze or release normalisation parameter
if self._bkgpars == 0:
spec['Normalization'].fix()
else:
spec['Normalization'].free()
else:
# Create power law model
if self._bkgpars <= 2:
# Set Power Law model with arbitrary pivot energy
pivot = gammalib.GEnergy(1.0, 'TeV')
spec = gammalib.GModelSpectralPlaw(prefactor, index, pivot)
# Set parameter ranges
spec[0].min(prefactor / self._bkg_range_factor)
spec[0].max(prefactor * self._bkg_range_factor)
spec[1].scale(1)
spec[1].min(-5.0)
spec[1].max(5.0)
# Set number of free parameters
if self._bkgpars == 0:
spec[0].fix()
spec[1].fix()
elif self._bkgpars == 1:
spec[0].free()
spec[1].fix()
else:
spec[0].free()
spec[1].free()
else:
# Create reference powerlaw
pivot = gammalib.GEnergy(1.0, 'TeV')
plaw = gammalib.GModelSpectralPlaw(prefactor, index, pivot)
# Create spectral model and energy values
spec = gammalib.GModelSpectralNodes()
# Create logarithmic energy nodes
bounds = gammalib.GEbounds(self._bkgpars,
gammalib.GEnergy(emin, 'TeV'),
gammalib.GEnergy(emax, 'TeV'), True)
# Loop over bounds and set intensity value
for i in range(bounds.size()):
energy = bounds.elogmean(i)
value = plaw.eval(energy)
# Append energy, value - tuple to node function
spec.append(energy, value)
# Loop over parameters
for par in spec:
# Fix energy nodes
if 'Energy' in par.name():
par.fix()
# Release intensity nodes
elif 'Intensity' in par.name():
value = par.value()
par.scale(value)
par.min(value / self._bkg_range_factor)
par.max(value * self._bkg_range_factor)
# Return spectrum
return spec
def _background_spectrum(self,
run,
prefactor,
index,
emin=0.01,
emax=100.0):
# Handle constant spectral model
if index == 0.0 and self._bkgpars <= 1:
spec = gammalib.GModelSpectralConst()
spec['Normalization'].min(prefactor / self._bkg_range_factor)
spec['Normalization'].max(prefactor * self._bkg_range_factor)
spec['Normalization'].value(prefactor)
if self._bkgpars == 0:
spec['Normalization'].fix()
else:
spec['Normalization'].free()
else:
# Create power law model
if self._bkgpars <= 2:
e = gammalib.GEnergy(1.0, 'TeV')
spec = gammalib.GModelSpectralPlaw(prefactor, index, e)
# Set parameter ranges
spec[0].min(prefactor / self._bkg_range_factor)
spec[0].max(prefactor * self._bkg_range_factor)
spec[1].scale(1)
spec[1].min(-5.0)
spec[1].max(5.0)
# Set number of free parameters
if self._bkgpars == 0:
spec[0].fix()
spec[1].fix()
elif self._bkgpars == 1:
spec[0].free()
spec[1].fix()
else:
spec[0].free()
spec[1].free()
else:
# Create reference powerlaw
plaw = gammalib.GModelSpectralPlaw(
prefactor, index, gammalib.GEnergy(1.0, 'TeV'))
# Create spectral model and energy values
spec = gammalib.GModelSpectralNodes()
bounds = gammalib.GEbounds(self._bkgpars,
gammalib.GEnergy(emin, 'TeV'),
gammalib.GEnergy(emax, 'TeV'), True)
for i in range(bounds.size()):
energy = bounds.elogmean(i)
value = plaw.eval(energy, gammalib.GTime())
spec.append(energy, value)
for par in spec:
if 'Energy' in par.name():
par.fix()
elif 'Intensity' in par.name():
value = par.value()
par.scale(value)
par.min(value / self._bkg_range_factor)
par.max(value * self._bkg_range_factor)
# Return spectrum
return spec
for idx2 in (np.arange(0,int(width/binsize))[::-1]):
if (idx2+1.0)/(idx1+1.0) < (np.tan((125-ang)*np.pi/180.) - 7/binsize/(idx1+1.0)): #+ 80/idx1+1.0:
skymap.data[j,idx1,idx2] *= 0
else:
continue
### Smooth the map
skymap = skymap.smooth(width=0.08 * u.deg, kernel="gauss")
### Save the map
skymap.write('hessj1825_cube.fits', hdu='Primary', overwrite='True')
hdul = fits.open('hessj1825_cube.fits')
hdul[0].header.set('BUNIT', 'photon/cm2/s/MeV/sr', 'Photon flux')
hdul[0].verify('fix')
del hdul[0].header['BANDSHDU']
hdul[1].name='ENERGIES'
hdul[1].header['TTYPE2']='Energy'
hdul[1].verify('fix')
hdul.writeto('hessj1825_map.fits',overwrite=True)
### create gammalib model
spatial = gammalib.GModelSpatialDiffuseCube(gammalib.GFilename('hessj1825_map.fits'))
spectral = gammalib.GModelSpectralConst(1.)
model = gammalib.GModelSky(spatial,spectral)
## fill to model container and write to disk
models = gammalib.GModels()
models.append(model)
models.save('hessj1825.xml')
