def test_datasets_to_io(tmp_path):
filedata = "$GAMMAPY_DATA/tests/models/gc_example_datasets.yaml"
filemodel = "$GAMMAPY_DATA/tests/models/gc_example_models.yaml"
datasets = Datasets.read(filedata, filemodel)
assert len(datasets) == 2
assert len(datasets.parameters) == 22
dataset0 = datasets[0]
assert dataset0.counts.data.sum() == 6824
assert_allclose(dataset0.exposure.data.sum(), 2072125400000.0, atol=0.1)
assert dataset0.psf is not None
assert dataset0.edisp is not None
assert_allclose(dataset0.background_model.evaluate().data.sum(),
4094.2,
atol=0.1)
assert dataset0.background_model.name == "background_irf_gc"
dataset1 = datasets[1]
assert dataset1.background_model.name == "background_irf_g09"
assert (dataset0.models["gll_iem_v06_cutout"] ==
dataset1.models["gll_iem_v06_cutout"])
assert isinstance(dataset0.models, Models)
assert len(dataset0.models) == 2
assert dataset0.models[0].name == "gc"
assert dataset0.models[1].name == "gll_iem_v06_cutout"
assert (dataset0.models[0].parameters["reference"] is
dataset1.models[1].parameters["reference"])
assert_allclose(dataset1.models[1].parameters["lon_0"].value,
0.9,
atol=0.1)
datasets.write(tmp_path, prefix="written")
datasets_read = Datasets.read(tmp_path / "written_datasets.yaml",
tmp_path / "written_models.yaml")
assert len(datasets_read) == 2
dataset0 = datasets_read[0]
assert dataset0.counts.data.sum() == 6824
assert_allclose(dataset0.exposure.data.sum(), 2072125400000.0, atol=0.1)
assert dataset0.psf is not None
assert dataset0.edisp is not None
assert_allclose(dataset0.background_model.evaluate().data.sum(),
4094.2,
atol=0.1)
def input_dataset():
datasets = Datasets.read(
filedata="$GAMMAPY_DATA/fermi-3fhl-crab/Fermi-LAT-3FHL_datasets.yaml",
filemodel="$GAMMAPY_DATA/fermi-3fhl-crab/Fermi-LAT-3FHL_models.yaml",
)
dataset = datasets[0]
dataset.psf = None
return dataset
def test_spectrum_dataset_on_off_to_yaml(tmpdir):
spectrum_datasets_on_off = make_observation_list()
datasets = Datasets(spectrum_datasets_on_off)
datasets.write(path=tmpdir)
datasets_read = Datasets.read(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 read_regions(self):
for kr in self.ROIs_sel:
filedata = self.resdir / f"3FHL_ROI_num{kr}_datasets.yaml"
filemodel = self.resdir / f"3FHL_ROI_num{kr}_models.yaml"
try:
dataset = list(Datasets.read(filedata, filemodel))[0]
except (FileNotFoundError, IOError):
continue
pars = dataset.parameters
pars.covariance = np.load(self.resdir / f"{dataset.name}_covariance.npy")
infos = np.load(self.resdir / f"3FHL_ROI_num{kr}_fit_infos.npz")
self.diags["message"].append(infos["message"])
self.diags["stat"].append(infos["stat"])
if self.savefig:
self.plot_maps(dataset)
for model in dataset.models:
if (
self.FHL3[model.name].data["ROI_num"] == kr
and self.FHL3[model.name].data["Signif_Avg"] >= self.sig_cut
):
model.spatial_model.parameters.covariance = pars.get_subcovariance(
model.spatial_model.parameters
)
model.spectral_model.parameters.covariance = pars.get_subcovariance(
model.spectral_model.parameters
)
dataset.background_model.parameters.covariance = pars.get_subcovariance(
dataset.background_model.parameters
)
res_spec = model.spectral_model
cat_spec = self.FHL3[model.name].spectral_model()
res_fp = FluxPoints.read(
self.resdir / f"{model.name}_flux_points.fits"
)
res_fp.table["is_ul"] = res_fp.table["ts"] < 1.0
cat_fp = self.FHL3[model.name].flux_points.to_sed_type("dnde")
self.update_spec_diags(
dataset, model, cat_spec, res_spec, cat_fp, res_fp
)
if self.savefig:
self.plot_spec(kr, model, cat_spec, res_spec, cat_fp, res_fp)
def test_flux_point_dataset_serialization(tmp_path):
path = "$GAMMAPY_DATA/tests/spectrum/flux_points/diff_flux_points.fits"
data = FluxPoints.read(path)
data.table["e_ref"] = data.e_ref.to("TeV")
spectral_model = PowerLawSpectralModel(index=2.3,
amplitude="2e-13 cm-2 s-1 TeV-1",
reference="1 TeV")
model = SkyModel(spectral_model=spectral_model, name="test_model")
dataset = FluxPointsDataset(model, data, name="test_dataset")
Datasets([dataset]).write(tmp_path, prefix="tmp")
datasets = Datasets.read(tmp_path / "tmp_datasets.yaml",
tmp_path / "tmp_models.yaml")
new_dataset = datasets[0]
assert_allclose(new_dataset.data.table["dnde"], dataset.data.table["dnde"],
1e-4)
if dataset.mask_fit is None:
assert np.all(new_dataset.mask_fit == dataset.mask_safe)
assert np.all(new_dataset.mask_safe == dataset.mask_safe)
assert new_dataset.name == "test_dataset"
def read(filename):
return Datasets.read(f"{filename}_datasets.yaml",
f"{filename}_models.yaml")
# frozen: true # # ``` # ## Reading different datasets # # # ### Fermi-LAT 3FHL: map dataset for 3D analysis # For now we let's use the datasets serialization only to read the 3D `MapDataset` associated to Fermi-LAT 3FHL data and models. # In[ ]: path = "$GAMMAPY_DATA/fermi-3fhl-crab/Fermi-LAT-3FHL" filedata = Path(path + "_datasets.yaml") filemodel = Path(path + "_models.yaml") datasets = Datasets.read(filedata=filedata, filemodel=filemodel) dataset_fermi = datasets[0] # We get the Crab model in order to share it with the other datasets # In[ ]: crab_model = dataset_fermi.models["Crab Nebula"] crab_spec = crab_model.spectral_model print(crab_spec) # ### HESS-DL3: 1D ON/OFF dataset for spectral fitting # # The ON/OFF datasets can be read from PHA files following the [OGIP standards](https://heasarc.gsfc.nasa.gov/docs/heasarc/ofwg/docs/spectra/ogip_92_007/node5.html). # We read the PHA files from each observation, and compute a stacked dataset for simplicity. # Then the Crab spectral model previously defined is added to the dataset.