def test_spectrum_dataset_on_off_to_yaml(tmpdir):
spectrum_datasets_on_off = make_observation_list()
datasets = Datasets(spectrum_datasets_on_off)
datasets.to_yaml(path=tmpdir)
datasets_read = Datasets.from_yaml(tmpdir / "_datasets.yaml",
tmpdir / "_models.yaml")
assert len(datasets_read) == len(datasets)
assert datasets_read[0].name == datasets[0].name
assert datasets_read[1].name == datasets[1].name
assert datasets_read[1].counts.data.sum() == datasets[1].counts.data.sum()
def test_datasets_io_no_model(tmpdir):
axis = MapAxis.from_energy_bounds("1 TeV", "10 TeV", nbin=2)
geom = WcsGeom.create(npix=(5, 5), axes=[axis])
dataset_1 = MapDataset.create(geom, name="1")
dataset_2 = MapDataset.create(geom, name="2")
datasets = Datasets([dataset_1, dataset_2])
datasets.to_yaml(path=tmpdir, prefix="test")
filename_1 = tmpdir / "test_data_1.fits"
assert filename_1.exists()
filename_2 = tmpdir / "test_data_2.fits"
assert filename_2.exists()
def make_datasets_example():
# Define which data to use and print some information
energy_axis = MapAxis.from_edges(
np.logspace(-1.0, 1.0, 4), unit="TeV", name="energy", interp="log"
)
geom0 = WcsGeom.create(
skydir=(0, 0),
binsz=0.1,
width=(1, 1),
coordsys="GAL",
proj="CAR",
axes=[energy_axis],
)
geom1 = WcsGeom.create(
skydir=(1, 0),
binsz=0.1,
width=(1, 1),
coordsys="GAL",
proj="CAR",
axes=[energy_axis],
)
geoms = [geom0, geom1]
sources_coords = [(0, 0), (0.9, 0.1)]
names = ["gc", "g09"]
models = []
for ind, (lon, lat) in enumerate(sources_coords):
spatial_model = PointSpatialModel(
lon_0=lon * u.deg, lat_0=lat * u.deg, frame="galactic"
)
spectral_model = ExpCutoffPowerLawSpectralModel(
index=2 * u.Unit(""),
amplitude=3e-12 * u.Unit("cm-2 s-1 TeV-1"),
reference=1.0 * u.TeV,
lambda_=0.1 / u.TeV,
)
model_ecpl = SkyModel(
spatial_model=spatial_model, spectral_model=spectral_model, name=names[ind]
)
models.append(model_ecpl)
# test to link a spectral parameter
params0 = models[0].spectral_model.parameters
params1 = models[1].spectral_model.parameters
ind = params0.parameters.index(params0["reference"])
params0.parameters[ind] = params1["reference"]
# update the sky model
ind = models[0].parameters.parameters.index(models[0].parameters["reference"])
models[0].parameters.parameters[ind] = params1["reference"]
obs_ids = [110380, 111140, 111159]
data_store = DataStore.from_dir("$GAMMAPY_DATA/cta-1dc/index/gps/")
diffuse_model = SkyDiffuseCube.read(
"$GAMMAPY_DATA/fermi_3fhl/gll_iem_v06_cutout.fits"
)
datasets_list = []
for idx, geom in enumerate(geoms):
observations = data_store.get_observations(obs_ids)
stacked = MapDataset.create(geom=geom)
stacked.background_model.name = "background_irf_" + names[idx]
maker = MapDatasetMaker(geom=geom, offset_max=4.0 * u.deg)
for obs in observations:
dataset = maker.run(obs)
stacked.stack(dataset)
stacked.psf = stacked.psf.get_psf_kernel(position=geom.center_skydir, geom=geom, max_radius="0.3 deg")
stacked.edisp = stacked.edisp.get_energy_dispersion(position=geom.center_skydir, e_reco=energy_axis.edges)
stacked.name = names[idx]
stacked.model = models[idx] + diffuse_model
datasets_list.append(stacked)
datasets = Datasets(datasets_list)
dataset0 = datasets.datasets[0]
print("dataset0")
print("counts sum : ", dataset0.counts.data.sum())
print("expo sum : ", dataset0.exposure.data.sum())
print("bkg0 sum : ", dataset0.background_model.evaluate().data.sum())
path = "$GAMMAPY_DATA/tests/models/gc_example_"
datasets.to_yaml(path, overwrite=True)
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(
self.datadir + "/fermi_3fhl/fermi_3fhl_exposure_cube_hpx.fits.gz")
exposure_hpx.unit = "cm2 s"
# background iem
infile = self.datadir + "/catalogs/fermi/gll_iem_v06.fits.gz"
outfile = self.resdir + "/gll_iem_v06_extra.fits"
model_iem = extrapolate_iem(infile, outfile, self.logEc_extra)
# 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",
coordsys="GAL",
binsz=self.dlb,
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",
unit="GeV",
interp="log")
wcs = counts.geom.wcs
geom = WcsGeom(wcs=wcs, npix=counts.geom.npix, axes=[axis])
coords = counts.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,
counts.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 = EnergyDispersion.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
background_total = bkg_iem + bkg_iso
background_model = BackgroundModel(background_total)
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 = SkyModels(FHL3_roi)
# Dataset
dataset = MapDataset(
model=model_total,
counts=counts,
exposure=exposure,
psf=psf_kernel,
edisp=edisp,
background_model=background_model,
mask_fit=mask_fermi,
name="3FHL_ROI_num" + str(kr),
)
cat_stat = dataset.stat_sum()
datasets = Datasets([dataset])
fit = Fit(datasets)
results = fit.run(optimize_opts=self.optimize_opts)
print("ROI_num", str(kr), "\n", results)
fit_stat = datasets.stat_sum()
if results.message == "Optimization failed.":
pass
else:
datasets.to_yaml(path=Path(self.resdir),
prefix=dataset.name,
overwrite=True)
np.save(
self.resdir + "/3FHL_ROI_num" + str(kr) + "_covariance.npy",
results.parameters.covariance,
)
np.savez(
self.resdir + "/3FHL_ROI_num" + str(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(
datasets=datasets,
e_edges=self.El_flux,
source=model.name,
sigma_ul=2.0,
).run()
filename = self.resdir + "/" + 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)