def test_flux_estimator_norm_range_template():
energy = MapAxis.from_energy_bounds(0.1,
10,
3.0,
unit="TeV",
name="energy_true")
template = WcsNDMap.create(npix=10,
axes=[energy],
unit="cm-2 s-1 sr-1 TeV-1")
spatial = TemplateSpatialModel(template, normalize=False)
spectral = PowerLawNormSpectralModel()
model = SkyModel(spectral_model=spectral,
spatial_model=spatial,
name="test")
model.spectral_model.norm.max = 10
model.spectral_model.norm.min = 0
estimator = FluxEstimator(source="test",
selection_optional=[],
reoptimize=True)
scale_model = estimator.get_scale_model(Models([model]))
assert_allclose(scale_model.norm.min, 0)
assert_allclose(scale_model.norm.max, 10)
assert scale_model.norm.interp == "log"
def test_flux_estimator_fermi_no_reoptimization(fermi_datasets):
estimator = FluxEstimator(
0,
norm_n_values=5,
norm_min=0.5,
norm_max=2,
selection_optional="all",
reoptimize=False
)
datasets = fermi_datasets.slice_by_energy(
energy_min="1 GeV", energy_max="100 GeV"
)
datasets.models = fermi_datasets.models
result = estimator.run(datasets)
assert_allclose(result["norm"], 0.98949, atol=1e-3)
assert_allclose(result["ts"], 25083.75408, rtol=1e-3)
assert_allclose(result["norm_err"], 0.01998, atol=1e-3)
assert_allclose(result["norm_errn"], 0.0199, atol=1e-3)
assert_allclose(result["norm_errp"], 0.0199, atol=1e-3)
assert len(result["norm_scan"]) == 5
assert_allclose(result["norm_scan"][0], 0.5)
assert_allclose(result["norm_scan"][-1], 2)
assert_allclose(result["e_min"], 10 * u.GeV, atol=1e-3)
assert_allclose(result["e_max"], 83.255 * u.GeV, atol=1e-3)
def test_flux_estimator_fermi_with_reoptimization(fermi_datasets):
estimator = FluxEstimator(0, energy_range=["1 GeV", "100 GeV"], reoptimize=True)
result = estimator.run(fermi_datasets, steps=["err", "ts"])
assert_allclose(result["norm"], 0.970614, atol=1e-3)
assert_allclose(result["ts"], 13005.903067, atol=1e-3)
assert_allclose(result["norm_err"], 0.01998, atol=1e-3)
def test_flux_estimator_incorrect_energy_range(hess_datasets):
with pytest.raises(ValueError):
FluxEstimator(hess_datasets,
source="Crab",
energy_range=[1, 3, 10] * u.TeV)
with pytest.raises(ValueError):
FluxEstimator(hess_datasets,
source="Crab",
energy_range=[10, 1] * u.TeV)
def test_flux_estimator_1d(hess_datasets):
estimator = FluxEstimator(hess_datasets, source="Crab")
result = estimator.run('1 TeV', '10 TeV', steps=["ts", "errp-errn", "ul"])
assert_allclose(result["norm"], 1.176789, atol=1e-3)
assert_allclose(result["delta_ts"], 693.111777, atol=1e-3)
assert_allclose(result["err"], 0.078087, atol=1e-3)
assert_allclose(result["errn"], 0.078046, atol=1e-3)
assert_allclose(result["errp"], 0.081665, atol=1e-3)
assert_allclose(result["ul"], 1.431722, atol=1e-3)
def test_flux_estimator_1d(hess_datasets):
estimator = FluxEstimator(source="Crab", energy_range=[1, 10] * u.TeV)
result = estimator.run(hess_datasets, steps=["err", "ts", "errp-errn", "ul"])
assert_allclose(result["norm"], 1.176789, atol=1e-3)
assert_allclose(result["ts"], 693.111777, atol=1e-3)
assert_allclose(result["norm_err"], 0.078087, atol=1e-3)
assert_allclose(result["norm_errn"], 0.078046, atol=1e-3)
assert_allclose(result["norm_errp"], 0.081665, atol=1e-3)
assert_allclose(result["norm_ul"], 1.431722, atol=1e-3)
def test_flux_estimator_fermi_with_reoptimization(fermi_datasets):
estimator = FluxEstimator(0,
e_min="1 GeV",
e_max="100 GeV",
reoptimize=True,
selection_optional=None)
result = estimator.run(fermi_datasets)
assert_allclose(result["norm"], 1.010983, atol=1e-3)
assert_allclose(result["ts"], 20896.1864, atol=1e-3)
assert_allclose(result["norm_err"], 0.01998, atol=1e-3)
def test_flux_estimator_fermi_with_reoptimization(fermi_datasets):
estimator = FluxEstimator(0,
energy_min="1 GeV",
energy_max="100 GeV",
selection_optional=None,
reoptimize=True)
result = estimator.run(fermi_datasets)
assert_allclose(result["norm"], 0.989989, atol=1e-3)
assert_allclose(result["ts"], 18729.907481, rtol=1e-3)
assert_allclose(result["norm_err"], 0.01998, atol=1e-3)
def test_flux_estimator_fermi_with_reoptimization(fermi_datasets):
estimator = FluxEstimator(0, selection_optional=None, reoptimize=True)
datasets = fermi_datasets.slice_by_energy(energy_min="1 GeV",
energy_max="100 GeV")
datasets.models = fermi_datasets.models
result = estimator.run(datasets)
assert_allclose(result["norm"], 0.989989, atol=1e-3)
assert_allclose(result["ts"], 18729.368105, rtol=1e-3)
assert_allclose(result["norm_err"], 0.01998, atol=1e-3)
def test_flux_estimator_incorrect_energy_range(fermi_datasets):
with pytest.raises(ValueError):
FluxEstimator(source="Crab",
energy_min=10 * u.TeV,
energy_max=1 * u.TeV)
fe = FluxEstimator(source="Crab Nebula",
energy_min=0.18 * u.TeV,
energy_max=0.2 * u.TeV)
result = fe.run(fermi_datasets)
assert np.isnan(result["norm"])
def test_flux_estimator_1d(hess_datasets):
estimator = FluxEstimator(source="Crab",
e_min=1 * u.TeV,
e_max=10 * u.TeV,
selection_optional=["errn-errp", "ul"])
result = estimator.run(hess_datasets)
assert_allclose(result["norm"], 1.176789, atol=1e-3)
assert_allclose(result["ts"], 693.111777, atol=1e-3)
assert_allclose(result["norm_err"], 0.078087, atol=1e-3)
assert_allclose(result["norm_errn"], 0.078046, atol=1e-3)
assert_allclose(result["norm_errp"], 0.081665, atol=1e-3)
assert_allclose(result["norm_ul"], 1.431722, atol=1e-3)
def test_flux_estimator_norm_range():
model = SkyModel.create("pl", "gauss", name="test")
model.spectral_model.amplitude.min = 1e-15
model.spectral_model.amplitude.max = 1e-10
estimator = FluxEstimator(source="test",
selection_optional=[],
reoptimize=True)
scale_model = estimator.get_scale_model(Models([model]))
assert_allclose(scale_model.norm.min, 1e-3)
assert_allclose(scale_model.norm.max, 1e2)
assert scale_model.norm.interp == "log"
def test_flux_estimator_naima_model():
import naima
ECPL = naima.models.ExponentialCutoffPowerLaw(1e36 * u.Unit("1/eV"),
1 * u.TeV, 2.1, 13 * u.TeV)
IC = naima.models.InverseCompton(ECPL, seed_photon_fields=["CMB"])
naima_model = NaimaSpectralModel(IC)
model = SkyModel(spectral_model=naima_model, name="test")
estimator = FluxEstimator(source="test",
selection_optional=[],
reoptimize=True)
scale_model = estimator.get_scale_model(Models([model]))
assert_allclose(scale_model.norm.min, np.nan)
assert_allclose(scale_model.norm.max, np.nan)
def test_flux_estimator_fermi_no_reoptimization(fermi_datasets):
estimator = FluxEstimator(fermi_datasets,
0,
norm_n_values=5,
norm_min=0.5,
norm_max=2,
reoptimize=False)
result = estimator.run("1 GeV", "100 GeV")
assert_allclose(result["norm"], 0.970614, atol=1e-3)
assert_allclose(result["delta_ts"], 29695.720611, atol=1e-3)
assert_allclose(result["err"], 0.01998, atol=1e-3)
assert_allclose(result["errn"], 0.0199, atol=1e-3)
assert_allclose(result["errp"], 0.0199, atol=1e-3)
assert len(result["norm_scan"]) == 5
assert_allclose(result["norm_scan"][0], 0.5)
assert_allclose(result["norm_scan"][-1], 2)
def test_flux_estimator_compound_model():
pl = PowerLawSpectralModel()
pl.amplitude.min = 1e-15
pl.amplitude.max = 1e-10
pln = PowerLawNormSpectralModel()
pln.norm.value = 0.1
spectral_model = pl * pln
model = SkyModel(spectral_model=spectral_model, name="test")
estimator = FluxEstimator(source="test",
selection_optional=[],
reoptimize=True)
scale_model = estimator.get_scale_model(Models([model]))
assert_allclose(scale_model.norm.min, 1e-3)
assert_allclose(scale_model.norm.max, 1e2)
def test_flux_estimator_1d(hess_datasets):
estimator = FluxEstimator(
source="Crab",
energy_min=1 * u.TeV,
energy_max=10 * u.TeV,
selection_optional=["errn-errp", "ul"],
)
result = estimator.run(hess_datasets)
assert_allclose(result["norm"], 1.218139, atol=1e-3)
assert_allclose(result["ts"], 527.492959, atol=1e-3)
assert_allclose(result["norm_err"], 0.095496, atol=1e-3)
assert_allclose(result["norm_errn"], 0.093204, atol=1e-3)
assert_allclose(result["norm_errp"], 0.097818, atol=1e-3)
assert_allclose(result["norm_ul"], 1.525773, atol=1e-3)
assert_allclose(result["e_min"], 1 * u.TeV, atol=1e-3)
assert_allclose(result["e_max"], 10 * u.TeV, atol=1e-3)
def test_inhomogeneous_datasets(fermi_datasets, hess_datasets):
for dataset in hess_datasets:
dataset.models = fermi_datasets.models
datasets = Datasets()
datasets.extend(fermi_datasets)
datasets.extend(hess_datasets)
estimator = FluxEstimator(source="Crab Nebula",
e_min=1 * u.TeV,
e_max=10 * u.TeV,
selection_optional=None)
result = estimator.run(datasets)
assert_allclose(result["norm"], 1.022802, atol=1e-3)
assert_allclose(result["ts"], 21584.515969, atol=1e-3)
assert_allclose(result["norm_err"], 0.01966, atol=1e-3)
def test_flux_estimator_fermi_no_reoptimization(fermi_datasets):
estimator = FluxEstimator(
0,
energy_range=["1 GeV", "100 GeV"],
norm_n_values=5,
norm_min=0.5,
norm_max=2,
reoptimize=False,
)
result = estimator.run(fermi_datasets)
assert_allclose(result["norm"], 0.970614, atol=1e-3)
assert_allclose(result["ts"], 29695.689216, atol=1e-3)
assert_allclose(result["norm_err"], 0.01998, atol=1e-3)
assert_allclose(result["norm_errn"], 0.0199, atol=1e-3)
assert_allclose(result["norm_errp"], 0.0199, atol=1e-3)
assert len(result["norm_scan"]) == 5
assert_allclose(result["norm_scan"][0], 0.5)
assert_allclose(result["norm_scan"][-1], 2)
def test_flux_estimator_1d(hess_datasets):
estimator = FluxEstimator(
source="Crab",
selection_optional=["errn-errp", "ul"],
reoptimize=False
)
datasets = hess_datasets.slice_by_energy(
energy_min=1 * u.TeV, energy_max=10 * u.TeV,
)
datasets.models = hess_datasets.models
result = estimator.run(datasets)
assert_allclose(result["norm"], 1.218139, atol=1e-3)
assert_allclose(result["ts"], 527.492959, atol=1e-3)
assert_allclose(result["norm_err"], 0.095496, atol=1e-3)
assert_allclose(result["norm_errn"], 0.093204, atol=1e-3)
assert_allclose(result["norm_errp"], 0.097818, atol=1e-3)
assert_allclose(result["norm_ul"], 1.418475, atol=1e-3)
assert_allclose(result["e_min"], 1 * u.TeV, atol=1e-3)
assert_allclose(result["e_max"], 10 * u.TeV, atol=1e-3)
def test_flux_estimator_fermi_no_reoptimization(fermi_datasets):
estimator = FluxEstimator(
0,
e_min="1 GeV",
e_max="100 GeV",
norm_n_values=5,
norm_min=0.5,
norm_max=2,
reoptimize=False,
)
result = estimator.run(fermi_datasets)
assert_allclose(result["norm"], 1.010983, atol=1e-3)
assert_allclose(result["ts"], 28086.565, atol=1e-3)
assert_allclose(result["norm_err"], 0.01998, atol=1e-3)
assert_allclose(result["norm_errn"], 0.0199, atol=1e-3)
assert_allclose(result["norm_errp"], 0.0199, atol=1e-3)
assert len(result["norm_scan"]) == 5
assert_allclose(result["norm_scan"][0], 0.5)
assert_allclose(result["norm_scan"][-1], 2)
def test_inhomogeneous_datasets(fermi_datasets, hess_datasets):
datasets = Datasets()
datasets.extend(fermi_datasets)
datasets.extend(hess_datasets)
datasets = datasets.slice_by_energy(
energy_min=1 * u.TeV,
energy_max=10 * u.TeV,
)
datasets.models = fermi_datasets.models
estimator = FluxEstimator(source="Crab Nebula",
selection_optional=[],
reoptimize=True)
result = estimator.run(datasets)
assert_allclose(result["norm"], 1.190622, atol=1e-3)
assert_allclose(result["ts"], 612.50171, atol=1e-3)
assert_allclose(result["norm_err"], 0.090744, atol=1e-3)
assert_allclose(result["e_min"], 0.693145 * u.TeV, atol=1e-3)
assert_allclose(result["e_max"], 10 * u.TeV, atol=1e-3)
def test_flux_estimator_fermi_no_reoptimization(fermi_datasets):
estimator = FluxEstimator(
0,
energy_min="1 GeV",
energy_max="100 GeV",
norm_n_values=5,
norm_min=0.5,
norm_max=2,
reoptimize=False,
)
result = estimator.run(fermi_datasets)
assert_allclose(result["norm"], 0.982434, atol=1e-3)
assert_allclose(result["ts"], 23856.262603, atol=1e-3)
assert_allclose(result["norm_err"], 0.01998, atol=1e-3)
assert_allclose(result["norm_errn"], 0.0199, atol=1e-3)
assert_allclose(result["norm_errp"], 0.0199, atol=1e-3)
assert len(result["norm_scan"]) == 5
assert_allclose(result["norm_scan"][0], 0.5)
assert_allclose(result["norm_scan"][-1], 2)
assert_allclose(result["e_min"], 10 * u.GeV, atol=1e-3)
assert_allclose(result["e_max"], 83.255 * u.GeV, atol=1e-3)
def test_inhomogeneous_datasets(fermi_datasets, hess_datasets):
for dataset in hess_datasets:
dataset.models = fermi_datasets.models
datasets = Datasets()
datasets.extend(fermi_datasets)
datasets.extend(hess_datasets)
estimator = FluxEstimator(
source="Crab Nebula",
energy_min=1 * u.TeV,
energy_max=10 * u.TeV,
selection_optional=None,
)
result = estimator.run(datasets)
assert_allclose(result["norm"], 1.190622, atol=1e-3)
assert_allclose(result["ts"], 612.500392, atol=1e-3)
assert_allclose(result["norm_err"], 0.090744, atol=1e-3)
assert_allclose(result["e_min"], 0.693145 * u.TeV, atol=1e-3)
assert_allclose(result["e_max"], 2 * u.TeV, atol=1e-3)
def test_flux_estimator_incorrect_energy_range():
with pytest.raises(ValueError):
FluxEstimator(source="Crab",
energy_min=10 * u.TeV,
energy_max=1 * u.TeV)
def test_inhomogeneous_datasets(fermi_datasets, hess_datasets):
fermi_datasets.append(hess_datasets[0])
with pytest.raises(ValueError):
estimator = FluxEstimator(source=0, energy_range=[1, 10] * u.TeV)
estimator.run(fermi_datasets)
def test_inhomogeneous_datasets(fermi_datasets, hess_datasets):
fermi_datasets.append(hess_datasets[0])
with pytest.raises(ValueError):
FluxEstimator(fermi_datasets, source=0)
