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
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 = SpectralCube.read(background_file)
exposure_cube = SpectralCube.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 = SpectralCube(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
def prepare_images():
# Read in data
fermi_vela = FermiVelaRegion()
background_file = FermiVelaRegion.filenames()['diffuse_model']
exposure_file = FermiVelaRegion.filenames()['exposure_cube']
counts_file = FermiVelaRegion.filenames()['counts_cube']
background_model = SkyCube.read(background_file, format='fermi-background')
exposure_cube = SkyCube.read(exposure_file, format='fermi-exposure')
# Re-project background cube
repro_bg_cube = background_model.reproject(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,
integral_resolution=5)
# Convolve with Energy-dependent Fermi LAT PSF
psf = EnergyDependentTablePSF.read(FermiVelaRegion.filenames()['psf'])
kernels = psf.kernels(npred_cube)
convolved_npred_cube = npred_cube.convolve(kernels, mode='reflect')
# Counts data
counts_cube = SkyCube.read(counts_file, format='fermi-counts')
counts_cube = counts_cube.reproject(npred_cube)
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
def prepare_images():
# Read in data
fermi_vela = FermiVelaRegion()
background_file = FermiVelaRegion.filenames()['diffuse_model']
exposure_file = FermiVelaRegion.filenames()['exposure_cube']
counts_file = FermiVelaRegion.filenames()['counts_cube']
background_model = SkyCube.read(background_file, format='fermi-background')
exposure_cube = SkyCube.read(exposure_file, format='fermi-exposure')
# Re-project background cube
repro_bg_cube = background_model.reproject(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,
integral_resolution=5)
# Convolve with Energy-dependent Fermi LAT PSF
psf = EnergyDependentTablePSF.read(FermiVelaRegion.filenames()['psf'])
kernels = psf.kernels(npred_cube)
convolved_npred_cube = npred_cube.convolve(kernels, mode='reflect')
# Counts data
counts_cube = SkyCube.read(counts_file, format='fermi-counts')
counts_cube = counts_cube.reproject(npred_cube)
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
