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 _check_result_file(self, filename):
"""
Check content of map cube
Parameters
----------
filename : str
Map cube file name
"""
# Load map cube
cube = gammalib.GModelSpatialDiffuseCube(filename)
# Check map cube
self._check_cube(cube)
# Return
return
def _check_cube(self, filename):
"""
Check content of map cube
Parameters
----------
filename : str
Map cube file name.
"""
# Load map cube
cube = gammalib.GModelSpatialDiffuseCube(filename)
# Test map cube
#self.test_value(cube.maps(), 20, "20 maps")
#self.test_value(cube.pixels(), 40000, "40000 map pixels")
# The map cube is not loaded by default !!!! We should add the method so that
# the attributes are known !!!!
self.test_value(cube.maps(), 0, '20 maps')
self.test_value(cube.pixels(), 0, '40000 map pixels')
# Return
return
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
added += 1
# add templates
models_template = gammalib.GModels('../known-sources/templates/{}.xml'.format(name))
for model in models_template:
# if model contains spatial map take care of it
if model.spatial().type() == 'DiffuseMap' or model.spatial().type() == 'DiffuseMapCube':
# find model map name and path
filename = model.spatial().filename().file()
filepath = model.spatial().filename().path()
# copy file to output directory
shutil.copy(filepath + filename, './')
# replace file with the one in output directory
if model.spatial().type() == 'DiffuseMap':
model.spatial(gammalib.GModelSpatialDiffuseMap(filename))
elif model.spatial().type() == 'DiffuseMapCube':
model.spatial(gammalib.GModelSpatialDiffuseCube(gammalib.GFilename(filename),
model.spatial()['Normalization'].value()))
# if model contains spatial map take care of it
if model.spectral().type() == 'FileFunction':
# find spectrum file name and path
filename = model.spectral().filename().file()
filepath = model.spectral().filename().path()
# copy file to output directory
shutil.copy(filepath + filename, './')
# replace file with the one in output directory
model.spectral(gammalib.GModelSpectralFunc(gammalib.GFilename(filename),
model.spectral()['Normalization'].value()))
# append model to container
models.append(model)
# if new remove synthetic source
if new:
src_dir = get_model_dir(model)
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')