def test_psf_kernel_from_gauss_read_write(tmp_path):
sigma = 0.5 * u.deg
binsz = 0.1 * u.deg
geom = WcsGeom.create(binsz=binsz, npix=150, axes=[MapAxis((0, 1, 2))])
kernel = PSFKernel.from_gauss(geom, sigma)
# Check that both maps are identical
assert_allclose(kernel.psf_kernel_map.data[0],
kernel.psf_kernel_map.data[1])
# Is there an odd number of pixels
assert_allclose(np.array(kernel.psf_kernel_map.geom.npix) % 2, 1)
kernel.write(tmp_path / "tmp.fits", overwrite=True)
kernel2 = PSFKernel.read(tmp_path / "tmp.fits")
assert_allclose(kernel.psf_kernel_map.data, kernel2.psf_kernel_map.data)
def test_psf_kernel_from_gauss_read_write(tmpdir):
sigma = 0.5 * u.deg
binsz = 0.1 * u.deg
geom = WcsGeom.create(binsz=binsz, npix=150, axes=[MapAxis((0, 1, 2))])
kernel = PSFKernel.from_gauss(geom, sigma)
# Check that both maps are identical
assert_allclose(kernel.psf_kernel_map.data[0],
kernel.psf_kernel_map.data[1])
# Is there an odd number of pixels
assert_allclose(np.array(kernel.psf_kernel_map.geom.npix) % 2, 1)
filename = str(tmpdir / "test_kernel.fits")
# Test read and write
kernel.write(filename, overwrite=True)
newkernel = PSFKernel.read(filename)
assert_allclose(kernel.psf_kernel_map.data, newkernel.psf_kernel_map.data)
# In[ ]:
counts = Map.read("$GAMMAPY_DATA/fermi-3fhl-gc/fermi-3fhl-gc-counts.fits.gz")
background = Map.read(
"$GAMMAPY_DATA/fermi-3fhl-gc/fermi-3fhl-gc-background.fits.gz"
)
exposure = Map.read(
"$GAMMAPY_DATA/fermi-3fhl-gc/fermi-3fhl-gc-exposure.fits.gz"
)
maps = {"counts": counts, "background": background, "exposure": exposure}
kernel = PSFKernel.read(
"$GAMMAPY_DATA/fermi-3fhl-gc/fermi-3fhl-gc-psf.fits.gz"
)
# In[ ]:
get_ipython().run_cell_magic('time', '', 'estimator = TSMapEstimator()\nimages = estimator.run(maps, kernel.data)')
# ## Plot images
# In[ ]:
plt.figure(figsize=(15, 5))
model = SkyModel(spatial_model=spatial_model, spectral_model=spectral_model)
# Now we read the maps and IRFs and create the dataset for each observation:
# In[ ]:
datasets = []
for obs_id in obs_ids:
path = Path("analysis_3d_joint") / "obs_{}".format(obs_id)
# read counts map and IRFs
counts = Map.read(path / "counts.fits.gz")
exposure = Map.read(path / "exposure.fits.gz")
psf = PSFKernel.read(path / "psf.fits.gz")
edisp = EnergyDispersion.read(path / "edisp.fits.gz")
# create background model per observation / dataset
background = Map.read(path / "background.fits.gz")
background_model = BackgroundModel(background)
background_model.tilt.frozen = False
background_model.norm.value = 1.3
# optionally define a safe energy threshold
emin = None
mask_data = counts.geom.energy_mask(emin=emin)
mask = Map.from_geom(geom=counts.geom, data=mask_data)
dataset = MapDataset(
model=model,
# ## Likelihood fit
#
# ### Reading maps and IRFs
# As first step we read in the maps and IRFs that we have saved to disk again:
# In[ ]:
# read maps
maps = {
"counts": Map.read(str(path / "counts.fits")),
"background": Map.read(str(path / "background.fits")),
"exposure": Map.read(str(path / "exposure.fits")),
}
# read IRFs
psf_kernel = PSFKernel.read(str(path / "psf.fits"))
edisp = EnergyDispersion.read(str(path / "edisp.fits"))
# ### Fit mask
#
# To select a certain energy range for the fit we can create a fit mask:
# In[ ]:
mask = Map.from_geom(maps["counts"].geom)
coords = mask.geom.get_coord()
mask.data = coords["energy"] > 0.3
# ### Model fit
#
