def test_map_fit_one_energy_bin(sky_model, geom_image):
dataset = get_map_dataset(sky_model, geom_image, geom_image)
sky_model.spectral_model.index.value = 3.0
sky_model.spectral_model.index.frozen = True
dataset.background_model.norm.value = 0.5
dataset.counts = dataset.npred()
# Move a bit away from the best-fit point, to make sure the optimiser runs
sky_model.parameters["sigma"].value = 0.21
dataset.background_model.parameters["norm"].frozen = True
fit = Fit(dataset)
result = fit.run()
assert result.success
npred = dataset.npred().data.sum()
assert_allclose(npred, 1087.073518, rtol=1e-3)
assert_allclose(result.total_stat, 5177.19198, rtol=1e-3)
pars = result.parameters
assert_allclose(pars["lon_0"].value, 0.2, rtol=1e-2)
assert_allclose(pars.error("lon_0"), 0.04623, rtol=1e-2)
assert_allclose(pars["sigma"].value, 0.2, rtol=1e-2)
assert_allclose(pars.error("sigma"), 0.031759, rtol=1e-2)
assert_allclose(pars["amplitude"].value, 1e-11, rtol=1e-2)
assert_allclose(pars.error("amplitude"), 2.163318e-12, rtol=1e-2)
def test_ecpl_fit(self):
self.set_model(self.ecpl)
fit = Fit(self.obs_list[0])
fit.run()
actual = fit.datasets.parameters["lambda_"].quantity
assert actual.unit == "TeV-1"
assert_allclose(actual.value, 0.145215, rtol=1e-2)
def test_likelihood_profile_reoptimize():
dataset = MyDataset()
fit = Fit(dataset)
fit.run()
dataset.parameters["y"].value = 0
result = fit.likelihood_profile("x", nvalues=3, reoptimize=True)
assert_allclose(result["values"], [0, 2, 4], atol=1e-7)
assert_allclose(result["likelihood"], [4, 0, 4], atol=1e-7)
def test_joint_fit(self):
self.set_model(self.pwl)
fit = Fit(self.obs_list)
fit.run()
actual = fit.datasets.parameters["index"].value
assert_allclose(actual, 2.7806, rtol=1e-3)
actual = fit.datasets.parameters["amplitude"].quantity
assert actual.unit == "cm-2 s-1 TeV-1"
assert_allclose(actual.value, 5.200e-11, rtol=1e-3)
def test_likelihood_profile():
dataset = MyDataset()
fit = Fit(dataset)
fit.run()
result = fit.likelihood_profile("x", nvalues=3)
assert_allclose(result["values"], [0, 2, 4], atol=1e-7)
assert_allclose(result["likelihood"], [4, 0, 4], atol=1e-7)
# Check that original value state wasn't changed
assert_allclose(dataset.parameters["x"].value, 2)
def test_compound(self):
model = self.pwl * 2
self.set_model(model)
fit = Fit(self.obs_list[0])
fit.run()
pars = fit.datasets.parameters
assert_allclose(pars["index"].value, 2.8166, rtol=1e-3)
p = pars["amplitude"]
assert p.unit == "cm-2 s-1 TeV-1"
assert_allclose(p.value, 5.0714e-12, rtol=1e-3)
def test_stats(self):
dataset = self.obs_list[0]
dataset.model = self.pwl
fit = Fit([dataset])
result = fit.run()
stats = dataset.likelihood_per_bin()
actual = np.sum(stats[dataset.mask_safe])
desired = result.total_stat
assert_allclose(actual, desired)
def test_likelihood_profile(self):
dataset = SpectrumDataset(
model=self.source_model,
counts=self.src,
mask_safe=np.ones(self.src.energy.nbin, dtype=bool),
)
fit = Fit([dataset])
result = fit.run()
true_idx = result.parameters["index"].value
values = np.linspace(0.95 * true_idx, 1.05 * true_idx, 100)
profile = fit.likelihood_profile("index", values=values)
actual = values[np.argmin(profile["likelihood"])]
assert_allclose(actual, true_idx, rtol=0.01)
def test_stacked_fit(self):
obs_stacker = SpectrumDatasetOnOffStacker(self.obs_list)
obs_stacker.run()
dataset = obs_stacker.stacked_obs
dataset.model = self.pwl
fit = Fit([dataset])
result = fit.run()
pars = result.parameters
assert_allclose(pars["index"].value, 2.7767, rtol=1e-3)
assert u.Unit(pars["amplitude"].unit) == "cm-2 s-1 TeV-1"
assert_allclose(pars["amplitude"].value, 5.191e-11, rtol=1e-3)
def test_no_edisp(self):
dataset = self.obs_list[0]
# Bring aeff in RECO space
energy = dataset.counts.energy.center
data = dataset.aeff.data.evaluate(energy=energy)
e_edges = dataset.counts.energy.edges
dataset.aeff = EffectiveAreaTable(data=data,
energy_lo=e_edges[:-1],
energy_hi=e_edges[1:])
dataset.edisp = None
dataset.model = self.pwl
fit = Fit([dataset])
result = fit.run()
assert_allclose(result.parameters["index"].value, 2.7961, atol=0.02)
def test_cash(self):
"""Simple CASH fit to the on vector"""
fit = Fit(self.dataset)
result = fit.run()
assert result.success
assert "minuit" in repr(result)
npred = self.dataset.npred().data.sum()
assert_allclose(npred, self.npred.sum(), rtol=1e-3)
assert_allclose(result.total_stat, -18087404.624, rtol=1e-3)
pars = result.parameters
assert_allclose(pars["index"].value, 2.1, rtol=1e-2)
assert_allclose(pars.error("index"), 0.00127, rtol=1e-2)
assert_allclose(pars["amplitude"].value, 1e5, rtol=1e-3)
assert_allclose(pars.error("amplitude"), 153.450, rtol=1e-2)
def test_cash(self):
"""Simple CASH fit to the on vector"""
dataset = SpectrumDataset(model=self.source_model, counts=self.src)
npred = dataset.npred().data
assert_allclose(npred[5], 660.5171, rtol=1e-5)
stat_val = dataset.likelihood()
assert_allclose(stat_val, -107346.5291, rtol=1e-5)
self.source_model.parameters["index"].value = 1.12
fit = Fit([dataset])
result = fit.run()
# These values are check with sherpa fits, do not change
pars = result.parameters
assert_allclose(pars["index"].value, 1.995525, rtol=1e-3)
assert_allclose(pars["amplitude"].value, 100245.9, rtol=1e-3)
def setup(self):
path = "$GAMMAPY_DATA/joint-crab/spectra/hess/"
obs1 = SpectrumDatasetOnOff.from_ogip_files(path + "pha_obs23523.fits")
obs2 = SpectrumDatasetOnOff.from_ogip_files(path + "pha_obs23592.fits")
self.obs_list = [obs1, obs2]
self.pwl = PowerLaw(index=2,
amplitude=1e-12 * u.Unit("cm-2 s-1 TeV-1"),
reference=1 * u.TeV)
self.ecpl = ExponentialCutoffPowerLaw(
index=2,
amplitude=1e-12 * u.Unit("cm-2 s-1 TeV-1"),
reference=1 * u.TeV,
lambda_=0.1 / u.TeV,
)
# Example fit for one observation
self.obs_list[0].model = self.pwl
self.fit = Fit(self.obs_list[0])
def test_wstat(self):
"""WStat with on source and background spectrum"""
on_vector = self.src.copy()
on_vector.data += self.bkg.data
obs = SpectrumDatasetOnOff(
counts=on_vector,
counts_off=self.off,
acceptance=1,
acceptance_off=1 / self.alpha,
)
obs.model = self.source_model
self.source_model.parameters.index = 1.12
fit = Fit(obs)
result = fit.run()
pars = self.source_model.parameters
assert_allclose(pars["index"].value, 1.997342, rtol=1e-3)
assert_allclose(pars["amplitude"].value, 100245.187067, rtol=1e-3)
assert_allclose(result.total_stat, 30.022316, rtol=1e-3)
# Now we'll fit a model to the spectrum with the `Fit` class. First we load a power law model with an initial value for the index and the amplitude and then wo do a likelihood fit. The fit results are printed below.
# In[ ]:
model = PowerLaw(index=4,
amplitude="1.3e-9 cm-2 s-1 TeV-1",
reference="0.02 TeV")
emin_fit, emax_fit = (0.04 * u.TeV, 0.4 * u.TeV)
for obs in extraction.spectrum_observations:
obs.model = model
obs.mask_fit = obs.counts.energy_mask(emin=emin_fit, emax=emax_fit)
joint_fit = Fit(extraction.spectrum_observations)
joint_result = joint_fit.run()
model.parameters.covariance = joint_result.parameters.covariance
print(joint_result)
# Now you might want to do the stacking here even if in our case there is only one observation which makes it superfluous.
# We can compute flux points by fitting the norm of the global model in energy bands.
# In[ ]:
e_edges = np.logspace(np.log10(0.04), np.log10(0.4), 7) * u.TeV
from gammapy.spectrum import SpectrumDatasetOnOffStacker
stacker = SpectrumDatasetOnOffStacker(extraction.spectrum_observations)
