def prepare_images():
# Reads 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 = Quantity([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)
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
wcs = wcs.dropaxis(2)
header = wcs.to_header()
return model, gtmodel, ratio, counts, header
def prepare_images():
# Reads 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 = Quantity([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)
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
wcs = wcs.dropaxis(2)
header = wcs.to_header()
return model, gtmodel, ratio, counts, header
"""Test npred model image computation. """ from astropy.units import Quantity from astropy.coordinates import Angle from gammapy.datasets import FermiGalacticCenter from gammapy.irf import EnergyDependentTablePSF from gammapy.data import (SpectralCube, compute_npred_cube, convolve_cube) filenames = FermiGalacticCenter.filenames() spectral_cube = SpectralCube.read(filenames['diffuse_model']) exposure_cube = SpectralCube.read(filenames['exposure_cube']) psf = EnergyDependentTablePSF.read(filenames['psf']) spectral_cube = spectral_cube.reproject_to(exposure_cube) energy_bounds = Quantity([10, 30, 100, 500], 'GeV') npred_cube = compute_npred_cube(spectral_cube, exposure_cube, energy_bounds) offset_max = Angle(1, 'deg') npred_cube_convolved = convolve_cube(npred_cube, psf, offset_max)
