# Licensed under a 3-clause BSD style license - see LICENSE.rst
import logging
from itertools import repeat
import numpy as np
from astropy import units as u
from astropy.table import Table
import gammapy.utils.parallel as parallel
from gammapy.datasets import Datasets
from gammapy.datasets.actors import DatasetsActor
from gammapy.datasets.flux_points import _get_reference_model
from gammapy.maps import MapAxis
from gammapy.modeling import Fit
from ..flux import FluxEstimator
from .core import FluxPoints
log = logging.getLogger(__name__)
__all__ = ["FluxPointsEstimator"]
[docs]
class FluxPointsEstimator(FluxEstimator, parallel.ParallelMixin):
"""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 `H.E.S.S. Galactic Plane Survey paper <https://ui.adsabs.harvard.edu/abs/2018A%26A...612A...1H>`__
Parameters
----------
source : str or int
For which source in the model to compute the flux points.
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.
n_sigma_sensitivity : int
Sigma to use for sensitivity computation. Default is 5.
selection_optional : list of str, optional
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.
* "sensitivity": estimate sensitivity for a given significance.
Default is None so the optional steps are not executed.
energy_edges : list of `~astropy.units.Quantity`, optional
Edges of the flux points energy bins. The resulting bin edges won't be exactly equal to the input ones,
but rather the closest values to the energy axis edges of the parent dataset.
Default is None: apply the estimator in each energy bin of the parent dataset.
For further explanation see :ref:`estimators`.
fit : `Fit`
Fit instance specifying the backend and fit options.
reoptimize : bool
If True the free parameters of the other models are fitted in each bin independently,
together with the norm of the source of interest
(but the other parameters of the source of interest are kept frozen).
If False only the norm of the source of interest if fitted,
and all other parameters are frozen at their current values.
sum_over_energy_groups : bool
Whether to sum over the energy groups or fit the norm on the full energy grid.
n_jobs : int
Number of processes used in parallel for the computation. Default is one, unless
`~gammapy.utils.parallel.N_JOBS_DEFAULT` was modified. The number of jobs is
limited to the number of physical CPUs.
parallel_backend : {"multiprocessing", "ray"}
Which backend to use for multiprocessing.
norm : `~gammapy.modeling.Parameter` or dict
Norm parameter used for the fit
Default is None and a new parameter is created automatically,
with value=1, name="norm", scan_min=0.2, scan_max=5, and scan_n_values = 11.
By default the min and max are not set and derived from the source model,
unless the source model does not have one and only one norm parameter.
If a dict is given the entries should be a subset of
`~gammapy.modeling.Parameter` arguments.
"""
tag = "FluxPointsEstimator"
def __init__(
self,
energy_edges=[1, 10] * u.TeV,
sum_over_energy_groups=False,
n_jobs=None,
parallel_backend=None,
**kwargs,
):
self.energy_edges = energy_edges
self.sum_over_energy_groups = sum_over_energy_groups
self.n_jobs = n_jobs
self.parallel_backend = parallel_backend
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.
"""
if not isinstance(datasets, DatasetsActor):
datasets = Datasets(datasets=datasets)
if not datasets.energy_axes_are_aligned:
raise ValueError("All datasets must have aligned energy axes.")
telescopes = []
for d in datasets:
if d.meta_table is not None and "TELESCOP" in d.meta_table.colnames:
telescopes.extend(list(d.meta_table["TELESCOP"].flatten()))
if len(np.unique(telescopes)) > 1:
raise ValueError(
"All datasets must use the same value of the"
" 'TELESCOP' meta keyword."
)
meta = {
"n_sigma": self.n_sigma,
"n_sigma_ul": self.n_sigma_ul,
"sed_type_init": "likelihood",
}
rows = parallel.run_multiprocessing(
self.estimate_flux_point,
zip(
repeat(datasets),
self.energy_edges[:-1],
self.energy_edges[1:],
),
backend=self.parallel_backend,
pool_kwargs=dict(processes=self.n_jobs),
task_name="Energy bins",
)
table = Table(rows, meta=meta)
model = _get_reference_model(datasets.models[self.source], self.energy_edges)
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 : `~gammapy.datasets.Datasets`
Datasets.
energy_min, energy_max : `~astropy.units.Quantity`
Energy bounds to compute the flux point for.
Returns
-------
result : dict
Dictionary 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_sliced.__class__(
[_.to_image(name=_.name) for _ in datasets_sliced]
)
if len(datasets_sliced) > 0:
if datasets.models is not None:
models_sliced = datasets.models._slice_by_energy(
energy_min=energy_min,
energy_max=energy_max,
sum_over_energy_groups=self.sum_over_energy_groups,
)
datasets_sliced.models = models_sliced
return super().run(datasets=datasets_sliced)
else:
log.warning(f"No dataset contribute in range {energy_min}-{energy_max}")
model = _get_reference_model(
datasets.models[self.source], self.energy_edges
)
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_scan = self.norm.copy().scan_values
result.update({"norm_scan": norm_scan, "stat_scan": np.nan * norm_scan})
if "sensitivity" in self.selection_optional:
result.update({"norm_sensitivity": np.nan})
return result
