def test_parameter_estimator_3d_no_reoptimization(crab_datasets_fermi):
datasets = crab_datasets_fermi
parameter = datasets[0].models.parameters["amplitude"]
estimator = ParameterEstimator(reoptimize=False, scan_n_values=10)
alpha_value = datasets[0].models.parameters["alpha"].value
result = estimator.run(datasets, parameter)
assert not datasets[0].models.parameters["alpha"].frozen
assert_allclose(datasets[0].models.parameters["alpha"].value, alpha_value)
assert_allclose(result["amplitude"], 0.018251, rtol=1e-4)
assert_allclose(result["amplitude_scan"].shape, 10)
assert_allclose(result["amplitude_scan"][0], 0.017282, atol=1e-3)
def test_parameter_estimator_1d(crab_datasets_1d, PLmodel):
datasets = crab_datasets_1d
for dataset in datasets:
dataset.models = SkyModel(spectral_model=PLmodel, name="Crab")
estimator = ParameterEstimator(scan_n_values=10, selection_optional="all")
result = estimator.run(datasets, parameter="amplitude")
assert_allclose(result["amplitude"], 5.1428e-11, rtol=1e-3)
assert_allclose(result["amplitude_err"], 6.42467e-12, rtol=1e-3)
assert_allclose(result["ts"], 353.2092, rtol=1e-3)
assert_allclose(result["amplitude_errp"], 6.703e-12, rtol=5e-3)
assert_allclose(result["amplitude_errn"], 6.152e-12, rtol=5e-3)
# Add test for scan
assert_allclose(result["amplitude_scan"].shape, 10)
def __init__(
self,
source,
energy_min,
energy_max,
norm_min=0.2,
norm_max=5,
norm_n_values=11,
norm_values=None,
n_sigma=1,
n_sigma_ul=3,
reoptimize=True,
selection_optional="all",
):
if norm_values is None:
norm_values = np.logspace(np.log10(norm_min), np.log10(norm_max),
norm_n_values)
self.norm_values = norm_values
self.source = source
self.energy_min = u.Quantity(energy_min)
self.energy_max = u.Quantity(energy_max)
if self.energy_min >= self.energy_max:
raise ValueError("Incorrect energy_range for Flux Estimator")
self.selection_optional = selection_optional
self.n_sigma = n_sigma
self.n_sigma_ul = n_sigma_ul
self.reoptimize = reoptimize
self._parameter_estimator = ParameterEstimator(
null_value=0,
scan_values=self.norm_values,
n_sigma=self.n_sigma,
n_sigma_ul=self.n_sigma_ul,
reoptimize=self.reoptimize,
selection_optional=self.selection_optional,
)
class FluxEstimator(Estimator):
"""Flux estimator.
Estimates flux for a given list of datasets with their model in a given energy range.
To estimate the model flux the amplitude of the reference spectral model is
fitted within the energy range. The amplitude is re-normalized using the "norm" parameter,
which specifies the deviation of the flux from the reference model in this
energy range.
Parameters
----------
source : str or int
For which source in the model to compute the flux.
energy_min, energy_max: `~astropy.units.Quantity`
The energy interval on which to compute the flux
norm_min : float
Minimum value for the norm used for the fit statistic profile evaluation.
norm_max : float
Maximum value for the norm used for the fit statistic profile evaluation.
norm_n_values : int
Number of norm values used for the fit statistic profile.
norm_values : `numpy.ndarray`
Array of norm values to be used for the fit statistic profile.
n_sigma : int
Sigma to use for asymmetric error computation.
n_sigma_ul : int
Sigma to use for upper limit computation.
reoptimize : bool
Re-optimize other free model parameters.
selection_optional : list of str
Which additional quantities to estimate. Available options are:
* "errn-errp": estimate asymmetric errors.
* "ul": estimate upper limits.
* "norm-scan": estimate fit statistic profiles.
By default all steps are executed.
"""
tag = "FluxEstimator"
_available_selection_optional = ["errn-errp", "ul", "scan"]
def __init__(
self,
source,
energy_min,
energy_max,
norm_min=0.2,
norm_max=5,
norm_n_values=11,
norm_values=None,
n_sigma=1,
n_sigma_ul=3,
reoptimize=True,
selection_optional="all",
):
if norm_values is None:
norm_values = np.logspace(np.log10(norm_min), np.log10(norm_max),
norm_n_values)
self.norm_values = norm_values
self.source = source
self.energy_min = u.Quantity(energy_min)
self.energy_max = u.Quantity(energy_max)
if self.energy_min >= self.energy_max:
raise ValueError("Incorrect energy_range for Flux Estimator")
self.selection_optional = selection_optional
self.n_sigma = n_sigma
self.n_sigma_ul = n_sigma_ul
self.reoptimize = reoptimize
self._parameter_estimator = ParameterEstimator(
null_value=0,
scan_values=self.norm_values,
n_sigma=self.n_sigma,
n_sigma_ul=self.n_sigma_ul,
reoptimize=self.reoptimize,
selection_optional=self.selection_optional,
)
@staticmethod
def get_reference_flux_values(model, energy_min, energy_max):
"""Get reference flux values
Parameters
----------
model : `SpectralModel`
Models
energy_min, energy_max : `~astropy.units.Quantity`
Energy range
Returns
-------
values : dict
Dictionary with reference energies and flux values.
"""
energy_ref = np.sqrt(energy_min * energy_max)
return {
"e_ref": energy_ref,
"e_min": energy_min,
"e_max": energy_max,
"ref_dnde": model(energy_ref),
"ref_flux": model.integral(energy_min, energy_max),
"ref_eflux": model.energy_flux(energy_min, energy_max),
"ref_e2dnde": model(energy_ref) * energy_ref**2,
}
def get_scale_model(self, models):
"""Set scale model
Parameters
----------
models : `Models`
Models
Return
------
model : `ScaleSpectralModel`
Scale spectral model
"""
ref_model = models[self.source].spectral_model
scale_model = ScaleSpectralModel(ref_model)
scale_model.norm.min = -1e5
scale_model.norm.max = 1e5
scale_model.norm.value = 1.0
scale_model.norm.frozen = False
return scale_model
def run(self, datasets):
"""Estimate flux for a given energy range.
Parameters
----------
datasets : list of `~gammapy.datasets.SpectrumDataset`
Spectrum datasets.
Returns
-------
result : dict
Dict with results for the flux point.
"""
datasets = Datasets(datasets)
datasets_sliced = datasets.slice_by_energy(energy_min=self.energy_min,
energy_max=self.energy_max)
models = datasets.models.copy()
datasets_sliced.models = models
if len(datasets_sliced) > 0:
# TODO: this relies on the energy binning of the first dataset
energy_axis = datasets_sliced[0].counts.geom.axes["energy"]
energy_min, energy_max = energy_axis.edges.min(
), energy_axis.edges.max()
else:
energy_min, energy_max = self.energy_min, self.energy_max
contributions = []
for dataset in datasets_sliced:
if dataset.mask is not None:
value = dataset.mask.data.any()
else:
value = True
contributions.append(value)
model = self.get_scale_model(models)
with np.errstate(invalid="ignore", divide="ignore"):
result = self.get_reference_flux_values(model.model, energy_min,
energy_max)
if len(datasets) == 0 or not np.any(contributions):
result.update(self.nan_result)
else:
models[self.source].spectral_model = model
datasets_sliced.models = models
result.update(
self._parameter_estimator.run(datasets_sliced, model.norm))
result["sqrt_ts"] = self.get_sqrt_ts(result["ts"], result["norm"])
return result
@property
def nan_result(self):
result = {
"norm": np.nan,
"stat": np.nan,
"success": False,
"norm_err": np.nan,
"ts": np.nan,
"sqrt_ts": np.nan,
}
if "errn-errp" in self.selection_optional:
result.update({"norm_errp": np.nan, "norm_errn": np.nan})
if "ul" in self.selection_optional:
result.update({"norm_ul": np.nan})
if "scan" in self.selection_optional:
nans = np.nan * np.empty_like(self.norm_values)
result.update({"norm_scan": nans, "stat_scan": nans})
return result