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 plot_containment_radii(fraction):
"""Plotting script for 68% and 95% containment radii."""
psf_gc = FermiGalacticCenter.psf()
gtpsf_table_gc = get_psf_table(psf_gc, 10000, 300000, 15)
psf_vela = FermiVelaRegion.psf()
gtpsf_table_vela = get_psf_table(psf_vela, 10000, 300000, 15)
if fraction == 68:
true_table_rep = load_lat_psf_performance('P7REP_SOURCE_V15_68')
true_table = load_lat_psf_performance('P7SOURCEV6_68')
rad = 'CONT_68'
elif fraction == 95:
true_table_rep = load_lat_psf_performance('P7REP_SOURCE_V15_95')
true_table = load_lat_psf_performance('P7SOURCEV6_95')
rad = 'CONT_95'
plt.plot(gtpsf_table_gc['ENERGY'], gtpsf_table_gc[rad],
color='red',label='Fermi Tools PSF @ Galactic Center')
plt.plot(gtpsf_table_vela['ENERGY'], gtpsf_table_vela[rad],
color='blue', label='Fermi Tools PSF @ Vela Region')
plt.plot(true_table_rep['energy'], true_table_rep['containment_angle'],
color='green', linestyle='--', label='P7REP_SOURCE_V15')
plt.plot(true_table['energy'], true_table['containment_angle'],
color='black', linestyle='--', label='P7SOURCEV6')
plt.xlim([10000, 300000])
plt.legend()
plt.semilogx()
plt.xlabel('Energy/MeV')
plt.ylabel('PSF Containment Radius/deg')
return plt
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
from astropy.modeling.models import Gaussian2D
from astropy.convolution.utils import discretize_model
from astropy.convolution import convolve
from aplpy import FITSFigure
# In[2]:
from gammapy.datasets import FermiVelaRegion
from gammapy.morphology import Shell2D
from gammapy.irf import EnergyDependentTablePSF
# In[3]:
# Get background model from Fermi diffuse model
energy_range = Quantity([10, 500], 'GeV')
fermi_diffuse = FermiVelaRegion.diffuse_model()
flux_bkg = fermi_diffuse.integral_flux_image(energy_range)
# Define source model
vela_junior = Shell2D(amplitude=3E-6,
x_0=-93.72,
y_0=-1.24,
r_in=1,
width=0.5,
normed=True)
vela_x = Gaussian2D(amplitude=7.5E-7,
x_mean=-96.14,
y_mean=-3.09,
x_stddev=0.6,
y_stddev=0.6)
