def make_plots(reference, result):
fpoints_ref = FluxPoints.read(reference)
fpoints_res = FluxPoints.read(result)
fig = plt.figure(figsize=(7, 5))
opts = {"energy_power": 2}
fpoints_ref.plot(**opts, label="reference")
fpoints_res.plot(**opts)
plt.legend()
return fig
def dataset():
path = "$GAMMAPY_DATA/tests/spectrum/flux_points/diff_flux_points.fits"
data = FluxPoints.read(path)
data.table["e_ref"] = data.e_ref.to("TeV")
model = SkyModel(spectral_model=PowerLawSpectralModel(
index=2.3, amplitude="2e-13 cm-2 s-1 TeV-1", reference="1 TeV"))
dataset = FluxPointsDataset(model, data)
return dataset
def read_regions(self):
for kr in self.ROIs_sel:
filedata = Path(self.resdir + "/3FHL_ROI_num" + str(kr) +
"_datasets.yaml")
filemodel = Path(self.resdir + "/3FHL_ROI_num" + str(kr) +
"_models.yaml")
try:
dataset = list(Datasets.from_yaml(filedata, filemodel))[0]
except (FileNotFoundError, IOError):
continue
pars = dataset.parameters
pars.covariance = np.load(self.resdir + "/" + dataset.name +
"_covariance.npy")
infos = np.load(self.resdir + "/3FHL_ROI_num" + str(kr) +
"_fit_infos.npz")
self.diags["message"].append(infos["message"])
self.diags["stat"].append(infos["stat"])
if self.savefig:
self.plot_maps(dataset)
for model in list(dataset.model):
if (self.FHL3[model.name].data["ROI_num"] == kr
and self.FHL3[model.name].data["Signif_Avg"] >=
self.sig_cut):
model.spatial_model.parameters.covariance = pars.get_subcovariance(
model.spatial_model.parameters)
model.spectral_model.parameters.covariance = pars.get_subcovariance(
model.spectral_model.parameters)
dataset.background_model.parameters.covariance = pars.get_subcovariance(
dataset.background_model.parameters)
res_spec = model.spectral_model
cat_spec = self.FHL3[model.name].spectral_model()
res_fp = FluxPoints.read(self.resdir + "/" + model.name +
"_flux_points.fits")
res_fp.table["is_ul"] = res_fp.table["ts"] < 1.0
cat_fp = self.FHL3[model.name].flux_points.to_sed_type(
"dnde")
self.update_spec_diags(dataset, model, cat_spec, res_spec,
cat_fp, res_fp)
if self.savefig:
self.plot_spec(kr, model, cat_spec, res_spec, cat_fp,
res_fp)
def test_flux_point_dataset_serialization(tmp_path):
path = "$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 test_flux_point_dataset_serialization(tmp_path):
path = "$GAMMAPY_DATA/tests/spectrum/flux_points/diff_flux_points.fits"
data = FluxPoints.read(path)
data.table["e_ref"] = data.e_ref.to("TeV")
# TODO: remove duplicate definition this once model is redefine as skymodel
spatial_model = ConstantSpatialModel()
spectral_model = PowerLawSpectralModel(index=2.3,
amplitude="2e-13 cm-2 s-1 TeV-1",
reference="1 TeV")
model = SkyModel(spatial_model, spectral_model, name="test_model")
dataset = FluxPointsDataset(SkyModels([model]), data, name="test_dataset")
Datasets([dataset]).to_yaml(tmp_path, prefix="tmp")
datasets = Datasets.from_yaml(tmp_path / "tmp_datasets.yaml",
tmp_path / "tmp_models.yaml")
new_dataset = datasets[0]
assert_allclose(new_dataset.data.table["dnde"], dataset.data.table["dnde"],
1e-4)
if dataset.mask_fit is None:
assert np.all(new_dataset.mask_fit == dataset.mask_safe)
assert np.all(new_dataset.mask_safe == dataset.mask_safe)
assert new_dataset.name == "test_dataset"
def test_write_ecsv(self, tmp_path, flux_points):
flux_points.write(tmp_path / "flux_points.ecsv")
actual = FluxPoints.read(tmp_path / "flux_points.ecsv")
assert str(flux_points) == str(actual)
def test_write_fits(self, tmp_path, flux_points):
flux_points.write(tmp_path / "tmp.fits")
actual = FluxPoints.read(tmp_path / "tmp.fits")
assert str(flux_points) == str(actual)
def flux_points_likelihood():
path = "$GAMMAPY_DATA/tests/spectrum/flux_points/binlike.fits"
return FluxPoints.read(path).to_sed_type("dnde")
def flux_points(request):
path = "$GAMMAPY_DATA/tests/spectrum/flux_points/" + request.param
return FluxPoints.read(path)
def test_write_ecsv(self, tmpdir, flux_points):
filename = tmpdir / "flux_points.ecsv"
flux_points.write(filename)
actual = FluxPoints.read(filename)
assert str(flux_points) == str(actual)
datasets.append(dataset)
dataset_hess = Datasets(datasets).stack_reduce()
dataset_hess.name = "HESS"
dataset_hess.models = crab_model
# ### HAWC: 1D dataset for flux point fitting
#
# The HAWC flux point are taken from https://arxiv.org/pdf/1905.12518.pdf. Then these flux points are read from a pre-made FITS file and passed to a `FluxPointsDataset` together with the source spectral model.
#
# In[ ]:
# read flux points from https://arxiv.org/pdf/1905.12518.pdf
filename = "$GAMMAPY_DATA/hawc_crab/HAWC19_flux_points.fits"
flux_points_hawc = FluxPoints.read(filename)
dataset_hawc = FluxPointsDataset(crab_model, flux_points_hawc, name="HAWC")
# ## Datasets serialization
#
# The `datasets` object contains each dataset previously defined.
# It can be saved on disk as datasets.yaml, models.yaml, and several data files specific to each dataset. Then the `datasets` can be rebuild later from these files.
# In[ ]:
datasets = Datasets([dataset_fermi, dataset_hess, dataset_hawc])
path = Path("crab-3datasets")
path.mkdir(exist_ok=True)
datasets.write(path=path, prefix="crab_10GeV_100TeV", overwrite=True)
filedata = path / "crab_10GeV_100TeV_datasets.yaml"