Source code for gammapy.estimators.points.sed

# Licensed under a 3-clause BSD style license - see LICENSE.rst
import logging
import numpy as np
from astropy import units as u
from gammapy.datasets import Datasets
from gammapy.maps import MapAxis
from gammapy.modeling import Fit
from gammapy.utils.pbar import progress_bar
from gammapy.utils.table import table_from_row_data
from ..flux import FluxEstimator
from .core import FluxPoints

log = logging.getLogger(__name__)

__all__ = ["FluxPointsEstimator"]


[docs]class FluxPointsEstimator(FluxEstimator): """Flux points estimator. Estimates flux points for a given list of datasets, energies and spectral model. To estimate the flux point the amplitude of the reference spectral model is fitted within the energy range defined by the energy group. This is done for each group independently. The amplitude is re-normalized using the "norm" parameter, which specifies the deviation of the flux from the reference model in this energy group. See https://gamma-astro-data-formats.readthedocs.io/en/latest/spectra/binned_likelihoods/index.html for details. The method is also described in the Fermi-LAT catalog paper https://ui.adsabs.harvard.edu/#abs/2015ApJS..218...23A or the HESS Galactic Plane Survey paper https://ui.adsabs.harvard.edu/#abs/2018A%26A...612A...1H Parameters ---------- energy_edges : `~astropy.units.Quantity` Energy edges of the flux point bins. source : str or int For which source in the model to compute the flux points. 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 Number of sigma to use for asymmetric error computation. Default is 1. n_sigma_ul : int Number of sigma to use for upper limit computation. Default is 2. selection_optional : list of str Which additional quantities to estimate. Available options are: * "all": all the optional steps are executed * "errn-errp": estimate asymmetric errors on flux. * "ul": estimate upper limits. * "scan": estimate fit statistic profiles. Default is None so the optional steps are not executed. fit : `Fit` Fit instance specifying the backend and fit options. reoptimize : bool Re-optimize other free model parameters. Default is True. sum_over_energy_groups : bool Whether to sum over the energy groups or fit the norm on the full energy grid. """ tag = "FluxPointsEstimator" def __init__( self, energy_edges=[1, 10] * u.TeV, sum_over_energy_groups=False, **kwargs ): self.energy_edges = energy_edges self.sum_over_energy_groups = sum_over_energy_groups fit = Fit(confidence_opts={"backend": "scipy"}) kwargs.setdefault("fit", fit) super().__init__(**kwargs)
[docs] def run(self, datasets): """Run the flux point estimator for all energy groups. Parameters ---------- datasets : `~gammapy.datasets.Datasets` Datasets Returns ------- flux_points : `FluxPoints` Estimated flux points. """ datasets = Datasets(datasets=datasets) rows = [] for energy_min, energy_max in progress_bar( zip(self.energy_edges[:-1], self.energy_edges[1:]), desc="Energy bins" ): row = self.estimate_flux_point( datasets, energy_min=energy_min, energy_max=energy_max, ) rows.append(row) meta = { "n_sigma": self.n_sigma, "n_sigma_ul": self.n_sigma_ul, "sed_type_init": "likelihood", } table = table_from_row_data(rows=rows, meta=meta) model = datasets.models[self.source] return FluxPoints.from_table( table=table, reference_model=model.copy(), gti=datasets.gti, format="gadf-sed", )
[docs] def estimate_flux_point(self, datasets, energy_min, energy_max): """Estimate flux point for a single energy group. Parameters ---------- datasets : `Datasets` Datasets energy_min, energy_max : `~astropy.units.Quantity` Energy bounds to compute the flux point for. Returns ------- result : dict Dict with results for the flux point. """ datasets_sliced = datasets.slice_by_energy( energy_min=energy_min, energy_max=energy_max ) if self.sum_over_energy_groups: datasets_sliced = Datasets( [_.to_image(name=_.name) for _ in datasets_sliced] ) if len(datasets_sliced) > 0: datasets_sliced.models = datasets.models.copy() return super().run(datasets=datasets_sliced) else: log.warning(f"No dataset contribute in range {energy_min}-{energy_max}") model = datasets.models[self.source].spectral_model return self._nan_result(datasets, model, energy_min, energy_max)
def _nan_result(self, datasets, model, energy_min, energy_max): """NaN result""" energy_axis = MapAxis.from_energy_edges([energy_min, energy_max]) with np.errstate(invalid="ignore", divide="ignore"): result = model.reference_fluxes(energy_axis=energy_axis) # convert to scalar values result = {key: value.item() for key, value in result.items()} result.update( { "norm": np.nan, "stat": np.nan, "success": False, "norm_err": np.nan, "ts": np.nan, "counts": np.zeros(len(datasets)), "npred": np.nan * np.zeros(len(datasets)), "npred_excess": np.nan * np.zeros(len(datasets)), "datasets": datasets.names, } ) 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: norm = super()._set_norm_parameter() norm_scan = norm.scan_values result.update({"norm_scan": norm_scan, "stat_scan": np.nan * norm_scan}) return result