def test_convolve_nd():
energy_axis = MapAxis.from_edges(np.logspace(-1.0, 1.0, 4),
unit="TeV",
name="energy_true")
geom = WcsGeom.create(binsz=0.02 * u.deg,
width=4.0 * u.deg,
axes=[energy_axis])
m = Map.from_geom(geom)
m.fill_by_coord([[0.2, 0.4], [-0.1, 0.6], [0.5, 3.6]])
# TODO : build EnergyDependentTablePSF programmatically rather than using CTA 1DC IRF
filename = (
"$GAMMAPY_DATA/cta-1dc/caldb/data/cta//1dc/bcf/South_z20_50h/irf_file.fits"
)
psf = EnergyDependentMultiGaussPSF.read(filename,
hdu="POINT SPREAD FUNCTION")
table_psf = psf.to_energy_dependent_table_psf(theta=0.5 * u.deg)
psf_kernel = PSFKernel.from_table_psf(table_psf,
geom,
max_radius=1 * u.deg)
assert psf_kernel.psf_kernel_map.data.shape == (3, 101, 101)
mc = m.convolve(psf_kernel)
assert_allclose(mc.data.sum(axis=(1, 2)), [0, 1, 1], atol=1e-5)
kernel_2d = Box2DKernel(3, mode="center")
kernel_2d.normalize("peak")
mc = m.convolve(kernel_2d.array)
assert_allclose(mc.data[0, :, :].sum(), 0, atol=1e-5)
assert_allclose(mc.data[1, :, :].sum(), 9, atol=1e-5)
def test_psf_kernel_to_image():
sigma1 = 0.5 * u.deg
sigma2 = 0.2 * u.deg
binsz = 0.1 * u.deg
axis = MapAxis.from_energy_bounds(1, 10, 2, unit="TeV", name="energy_true")
geom = WcsGeom.create(binsz=binsz, npix=50, axes=[axis])
rad = Angle(np.linspace(0.0, 1.5 * sigma1.to("deg").value, 100), "deg")
table_psf1 = TablePSF.from_shape(shape="disk", width=sigma1, rad=rad)
table_psf2 = TablePSF.from_shape(shape="disk", width=sigma2, rad=rad)
kernel1 = PSFKernel.from_table_psf(table_psf1, geom)
kernel2 = PSFKernel.from_table_psf(table_psf2, geom)
kernel1.psf_kernel_map.data[1, :, :] = kernel2.psf_kernel_map.data[1, :, :]
kernel_image_1 = kernel1.to_image()
kernel_image_2 = kernel1.to_image(exposure=[1, 2])
assert_allclose(kernel_image_1.psf_kernel_map.data.sum(), 1.0, atol=1e-5)
assert_allclose(kernel_image_1.psf_kernel_map.data[0, 25, 25],
0.028415,
atol=1e-5)
assert_allclose(kernel_image_1.psf_kernel_map.data[0, 22, 22],
0.009806,
atol=1e-5)
assert_allclose(kernel_image_1.psf_kernel_map.data[0, 20, 20],
0.0,
atol=1e-5)
assert_allclose(kernel_image_2.psf_kernel_map.data.sum(), 1.0, atol=1e-5)
assert_allclose(kernel_image_2.psf_kernel_map.data[0, 25, 25],
0.03791383,
atol=1e-5)
assert_allclose(kernel_image_2.psf_kernel_map.data[0, 22, 22],
0.0079069,
atol=1e-5)
assert_allclose(kernel_image_2.psf_kernel_map.data[0, 20, 20],
0.0,
atol=1e-5)
def test_table_psf_to_kernel_map():
sigma = 0.5 * u.deg
binsz = 0.1 * u.deg
geom = WcsGeom.create(binsz=binsz, npix=150)
rad = Angle(np.linspace(0.0, 3 * sigma.to("deg").value, 100), "deg")
table_psf = TablePSF.from_shape(shape="gauss", width=sigma, rad=rad)
kernel = PSFKernel.from_table_psf(table_psf, geom)
kernel_array = kernel.psf_kernel_map.data
# Is normalization OK?
assert_allclose(kernel_array.sum(), 1.0, atol=1e-5)
# maximum at the center of map?
ind = np.unravel_index(np.argmax(kernel_array, axis=None), kernel_array.shape)
# absolute tolerance at 0.5 because of even number of pixel here
assert_allclose(ind, geom.center_pix, atol=0.5)
"""Plot Fermi PSF.""" import matplotlib.pyplot as plt from gammapy.irf import EnergyDependentTablePSF, PSFKernel from gammapy.maps import WcsGeom filename = "$GAMMAPY_DATA/tests/unbundled/fermi/psf.fits" fermi_psf = EnergyDependentTablePSF.read(filename) psf = fermi_psf.table_psf_at_energy(energy="1 GeV") geom = WcsGeom.create(npix=100, binsz=0.01) kernel = PSFKernel.from_table_psf(psf, geom) plt.imshow(kernel.data) plt.colorbar() plt.show()
def run_region(self, kr, lon, lat, radius):
# TODO: for now we have to read/create the allsky maps each in each job
# because we can't pickle <functools._lru_cache_wrapper object
# send this back to init when fixed
# exposure
exposure_hpx = Map.read(
"$GAMMAPY_DATA/fermi_3fhl/fermi_3fhl_exposure_cube_hpx.fits.gz"
)
exposure_hpx.unit = "cm2 s"
# iem
iem_filepath = BASE_PATH / "data" / "gll_iem_v06_extrapolated.fits"
iem_fermi_extra = Map.read(iem_filepath)
# norm=1.1, tilt=0.03 see paper appendix A
model_iem = SkyDiffuseCube(
iem_fermi_extra, norm=1.1, tilt=0.03, name="iem_extrapolated"
)
# ROI
roi_time = time()
ROI_pos = SkyCoord(lon, lat, frame="galactic", unit="deg")
width = 2 * (radius + self.psf_margin)
# Counts
counts = Map.create(
skydir=ROI_pos,
width=width,
proj="CAR",
frame="galactic",
binsz=1 / 8.0,
axes=[self.energy_axis],
dtype=float,
)
counts.fill_by_coord(
{"skycoord": self.events.radec, "energy": self.events.energy}
)
axis = MapAxis.from_nodes(
counts.geom.axes[0].center, name="energy_true", unit="GeV", interp="log"
)
wcs = counts.geom.wcs
geom = WcsGeom(wcs=wcs, npix=counts.geom.npix, axes=[axis])
coords = geom.get_coord()
# expo
data = exposure_hpx.interp_by_coord(coords)
exposure = WcsNDMap(geom, data, unit=exposure_hpx.unit, dtype=float)
# read PSF
psf_kernel = PSFKernel.from_table_psf(
self.psf, geom, max_radius=self.psf_margin * u.deg
)
# Energy Dispersion
e_true = exposure.geom.axes[0].edges
e_reco = counts.geom.axes[0].edges
edisp = EDispKernel.from_diagonal_response(e_true=e_true, e_reco=e_reco)
# fit mask
if coords["lon"].min() < 90 * u.deg and coords["lon"].max() > 270 * u.deg:
coords["lon"][coords["lon"].value > 180] -= 360 * u.deg
mask = (
(coords["lon"] >= coords["lon"].min() + self.psf_margin * u.deg)
& (coords["lon"] <= coords["lon"].max() - self.psf_margin * u.deg)
& (coords["lat"] >= coords["lat"].min() + self.psf_margin * u.deg)
& (coords["lat"] <= coords["lat"].max() - self.psf_margin * u.deg)
)
mask_fermi = WcsNDMap(counts.geom, mask)
# IEM
eval_iem = MapEvaluator(
model=model_iem, exposure=exposure, psf=psf_kernel, edisp=edisp
)
bkg_iem = eval_iem.compute_npred()
# ISO
eval_iso = MapEvaluator(model=self.model_iso, exposure=exposure, edisp=edisp)
bkg_iso = eval_iso.compute_npred()
# merge iem and iso, only one local normalization is fitted
dataset_name = "3FHL_ROI_num" + str(kr)
background_total = bkg_iem + bkg_iso
background_model = BackgroundModel(
background_total, name="bkg_iem+iso", datasets_names=[dataset_name]
)
background_model.parameters["norm"].min = 0.0
# Sources model
in_roi = self.FHL3.positions.galactic.contained_by(wcs)
FHL3_roi = []
for ks in range(len(self.FHL3.table)):
if in_roi[ks] == True:
model = self.FHL3[ks].sky_model()
model.spatial_model.parameters.freeze_all() # freeze spatial
model.spectral_model.parameters["amplitude"].min = 0.0
if isinstance(model.spectral_model, PowerLawSpectralModel):
model.spectral_model.parameters["index"].min = 0.1
model.spectral_model.parameters["index"].max = 10.0
else:
model.spectral_model.parameters["alpha"].min = 0.1
model.spectral_model.parameters["alpha"].max = 10.0
FHL3_roi.append(model)
model_total = Models([background_model] + FHL3_roi)
# Dataset
dataset = MapDataset(
models=model_total,
counts=counts,
exposure=exposure,
psf=psf_kernel,
edisp=edisp,
mask_fit=mask_fermi,
name=dataset_name,
)
cat_stat = dataset.stat_sum()
datasets = Datasets([dataset])
fit = Fit(datasets)
results = fit.run(**self.optimize_opts)
print("ROI_num", str(kr), "\n", results)
fit_stat = datasets.stat_sum()
if results.message != "Optimization failed.":
datasets.write(path=Path(self.resdir), prefix=dataset.name, overwrite=True)
np.savez(
self.resdir / f"3FHL_ROI_num{kr}_fit_infos.npz",
message=results.message,
stat=[cat_stat, fit_stat],
)
exec_time = time() - roi_time
print("ROI", kr, " time (s): ", exec_time)
for model in FHL3_roi:
if (
self.FHL3[model.name].data["ROI_num"] == kr
and self.FHL3[model.name].data["Signif_Avg"] >= self.sig_cut
):
flux_points = FluxPointsEstimator(
e_edges=self.El_flux, source=model.name, n_sigma_ul=2,
).run(datasets=datasets)
filename = self.resdir / f"{model.name}_flux_points.fits"
flux_points.write(filename, overwrite=True)
exec_time = time() - roi_time - exec_time
print("ROI", kr, " Flux points time (s): ", exec_time)
