def test_flux_point_dataset_serialization(tmp_path):
path = "$GAMMAPY_DATA/tests/spectrum/flux_points/diff_flux_points.fits"
table = Table.read(make_path(path))
table["e_ref"] = table["e_ref"].quantity.to("TeV")
data = FluxPoints.from_table(table, format="gadf-sed")
spectral_model = PowerLawSpectralModel(index=2.3,
amplitude="2e-13 cm-2 s-1 TeV-1",
reference="1 TeV")
model = SkyModel(spectral_model=spectral_model, name="test_model")
dataset = FluxPointsDataset(model, data, name="test_dataset")
dataset2 = FluxPointsDataset.read(path, name="test_dataset2")
assert_allclose(dataset.data.dnde.data, dataset2.data.dnde.data)
assert dataset.mask_safe.data == dataset2.mask_safe.data
assert dataset2.name == "test_dataset2"
Datasets([dataset]).write(
filename=tmp_path / "tmp_datasets.yaml",
filename_models=tmp_path / "tmp_models.yaml",
)
datasets = Datasets.read(
filename=tmp_path / "tmp_datasets.yaml",
filename_models=tmp_path / "tmp_models.yaml",
)
new_dataset = datasets[0]
assert_allclose(new_dataset.data.dnde, dataset.data.dnde, 1e-4)
if dataset.mask_fit is None:
assert np.all(new_dataset.mask_fit == dataset.mask_safe)
assert np.all(new_dataset.mask_safe == dataset.mask_safe)
assert new_dataset.name == "test_dataset"
def test_flux_point_dataset_serialization(tmp_path):
path = "$GAMMAPY_DATA/tests/spectrum/flux_points/diff_flux_points.fits"
data = FluxPoints.read(path)
data.table["e_ref"] = data.energy_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(
filename=tmp_path / "tmp_datasets.yaml",
filename_models=tmp_path / "tmp_models.yaml",
)
datasets = Datasets.read(
filename=tmp_path / "tmp_datasets.yaml",
filename_models=tmp_path / "tmp_models.yaml",
)
new_dataset = datasets[0]
assert_allclose(new_dataset.data.dnde, dataset.data.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 dataset():
path = "$GAMMAPY_DATA/tests/spectrum/flux_points/diff_flux_points.fits"
data = FluxPoints.read(path)
data.table["e_ref"] = data.e_ref.to("TeV")
model = SkyModel(spectral_model=PowerLawSpectralModel(
index=2.3, amplitude="2e-13 cm-2 s-1 TeV-1", reference="1 TeV"))
dataset = FluxPointsDataset(model, data)
return dataset
def dataset():
path = "$GAMMAPY_DATA/tests/spectrum/flux_points/diff_flux_points.fits"
data = FluxPoints.read(path)
data.table["e_ref"] = data.e_ref.to("TeV")
model = SkyModel(spectral_model=PowerLawSpectralModel(
index=2.3, amplitude="2e-13 cm-2 s-1 TeV-1", reference="1 TeV"))
obs_table = Table()
obs_table["TELESCOP"] = ["CTA"]
obs_table["OBS_ID"] = ["0001"]
obs_table["INSTRUME"] = ["South_Z20_50h"]
dataset = FluxPointsDataset(model, data, meta_table=obs_table)
return dataset
def get_flux_points(self):
"""Calculate flux points for a specific model component."""
if not self.fit:
raise RuntimeError("No results available from Fit.")
fp_settings = self.config.flux_points
log.info("Calculating flux points.")
energy_edges = self._make_energy_axis(fp_settings.energy).edges
flux_point_estimator = FluxPointsEstimator(
energy_edges=energy_edges,
source=fp_settings.source,
**fp_settings.parameters,
)
fp = flux_point_estimator.run(datasets=self.datasets)
fp.table["is_ul"] = fp.table["ts"] < 4
self.flux_points = FluxPointsDataset(
data=fp, models=self.models[fp_settings.source])
cols = ["e_ref", "ref_flux", "dnde", "dnde_ul", "dnde_err", "is_ul"]
log.info("\n{}".format(self.flux_points.data.table[cols]))
def get_flux_points(self):
"""Calculate flux points for a specific model component."""
if not self.datasets:
raise RuntimeError("No datasets set.")
fp_settings = self.config.flux_points
log.info("Calculating flux points.")
energy_edges = self._make_energy_axis(fp_settings.energy).edges
flux_point_estimator = FluxPointsEstimator(
energy_edges=energy_edges,
source=fp_settings.source,
fit=self.fit,
**fp_settings.parameters,
)
fp = flux_point_estimator.run(datasets=self.datasets)
self.flux_points = FluxPointsDataset(
data=fp, models=self.models[fp_settings.source])
cols = ["e_ref", "dnde", "dnde_ul", "dnde_err", "sqrt_ts"]
table = self.flux_points.data.to_table(sed_type="dnde")
log.info("\n{}".format(table[cols]))
