def _get_ref_cube(self, enumbins=11):
p = self.parameters
wcs = self.reference.wcs.deepcopy()
shape = (enumbins, ) + self.reference.data.shape
data = np.zeros(shape)
energy = Energy.equal_log_spacing(p['emin'], p['emax'], enumbins,
'TeV')
energy_axis = LogEnergyAxis(energy, mode='center')
return SkyCube(data=data, wcs=wcs, energy_axis=energy_axis)
def prepare_images():
# Read in data
background_file = FermiVelaRegion.filenames()['diffuse_model']
exposure_file = FermiVelaRegion.filenames()['exposure_cube']
counts_file = FermiVelaRegion.filenames()['counts_cube']
background_model = SkyCube.read(background_file)
exposure_cube = SkyCube.read(exposure_file)
# Add correct units
exposure_cube.data = Quantity(exposure_cube.data.value, 'cm2 s')
# Re-project background cube
repro_bg_cube = background_model.reproject_to(exposure_cube)
# Define energy band required for output
energies = EnergyBounds([10, 500], 'GeV')
# Compute the predicted counts cube
npred_cube = compute_npred_cube(repro_bg_cube, exposure_cube, energies)
# Convolve with Energy-dependent Fermi LAT PSF
psf = EnergyDependentTablePSF.read(FermiVelaRegion.filenames()['psf'])
convolved_npred_cube = convolve_cube(npred_cube,
psf,
offset_max=Angle(3, 'deg'))
# Counts data
counts_data = fits.open(counts_file)[0].data
counts_wcs = WCS(fits.open(counts_file)[0].header)
counts_cube = SkyCube(data=Quantity(counts_data, ''),
wcs=counts_wcs,
energy=energies)
counts_cube = counts_cube.reproject_to(npred_cube,
projection_type='nearest-neighbor')
counts = counts_cube.data[0]
model = convolved_npred_cube.data[0]
# Load Fermi tools gtmodel background-only result
gtmodel = fits.open(
FermiVelaRegion.filenames()['background_image'])[0].data.astype(float)
# Ratio for the two background images
ratio = np.nan_to_num(model / gtmodel)
# Header is required for plotting, so returned here
wcs = npred_cube.wcs
header = wcs.to_header()
return model, gtmodel, ratio, counts, header
"_TeV.fits")["exposure"].data * 1e4
#exposure_data[i_E,:,:] = SkyImageList.read(outdir_data + "/fov_bg_maps" + str(E1) + "_" + str(E2) + "_TeV.fits")["exposure"].data
bkg_data[i_E, :, :] = SkyImageList.read(outdir_data + "/fov_bg_maps" +
str(E1) + "_" + str(E2) +
"_TeV.fits")["bkg"].data
psf_file = Table.read(outdir_data + "/psf_table_" + source_name + "_" +
str(E1) + "_" + str(E2) + ".fits")
header = on.to_image_hdu().header
psf_data[i_E, :, :] = fill_acceptance_image(
header, on.center, psf_file["theta"].to("deg"),
psf_file["psf_value"].data, psf_file["theta"].to("deg")[-1]).data
#logenergy_axis=LogEnergyAxis(energy_bins[0:imax+1],mode='edges')
logenergy_axis = LogEnergyAxis(energy_bins, mode='edges')
counts_3D = SkyCube(name="counts3D",
data=Quantity(counts, " "),
wcs=on.wcs,
energy_axis=logenergy_axis)
cube = counts_3D.to_sherpa_data3d(dstype='Data3DInt')
logenergy_axis = LogEnergyAxis(energy_bins[0:imax + 1], mode='edges')
psf_image_3D = SkyCube(name="counts3D",
data=Quantity(psf_data, " "),
wcs=on.wcs,
energy_axis=logenergy_axis)
exposure_image_3D = SkyCube(name="counts3D",
data=Quantity(exposure_data, "m2 s "),
wcs=on.wcs,
energy_axis=logenergy_axis)
spatial_model = NormGauss2DInt('spatial-model')
spectral_model = PowLaw1D('spectral-model')
#spectral_model = MyPLExpCutoff('spectral-model')