# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""Tools to create profiles (i.e. 1D "slices" from 2D images)."""
import numpy as np
from astropy import units as u
from regions import CircleAnnulusSkyRegion
from gammapy.datasets import Datasets
from gammapy.maps import MapAxis
from gammapy.modeling.models import PowerLawSpectralModel, SkyModel
from .core import FluxPoints
from .sed import FluxPointsEstimator
__all__ = ["FluxProfileEstimator"]
[docs]class FluxProfileEstimator(FluxPointsEstimator):
"""Estimate flux profiles.
Parameters
----------
regions : list of `~regions.SkyRegion`
Regions to use.
spectrum : `~gammapy.modeling.models.SpectralModel` (optional)
Spectral model to compute the fluxes or brightness.
Default is power-law with spectral index of 2.
**kwargs : dict, optional
Keywords forwarded to the `FluxPointsEstimator` (see documentation
there for further description of valid keywords).
Examples
--------
This example shows how to compute a counts profile for the Fermi galactic
center region::
>>> from astropy import units as u
>>> from astropy.coordinates import SkyCoord
>>> from gammapy.data import GTI
>>> from gammapy.estimators import FluxProfileEstimator
>>> from gammapy.utils.regions import make_orthogonal_rectangle_sky_regions
>>> from gammapy.datasets import MapDataset
>>> from gammapy.maps import RegionGeom
>>> # load example data
>>> filename = "$GAMMAPY_DATA/fermi-3fhl-gc/fermi-3fhl-gc.fits.gz"
>>> dataset = MapDataset.read(filename, name="fermi-dataset")
>>> # configuration
>>> dataset.gti = GTI.create("0s", "1e7s", "2010-01-01")
>>> # creation of the boxes and axis
>>> start_pos = SkyCoord("-1d", "0d", frame='galactic')
>>> end_pos = SkyCoord("1d", "0d", frame='galactic')
>>> regions = make_orthogonal_rectangle_sky_regions(
start_pos=start_pos,
end_pos=end_pos,
wcs=dataset.counts.geom.wcs,
height=2 * u.deg,
nbin=21
)
>>> # set up profile estimator and run
>>> prof_maker = FluxProfileEstimator(regions=regions, energy_edges=[10, 2000] * u.GeV)
>>> fermi_prof = prof_maker.run(dataset)
>>> print(fermi_prof)
FluxPoints
----------
<BLANKLINE>
geom : RegionGeom
axes : ['lon', 'lat', 'energy', 'projected-distance']
shape : (1, 1, 1, 21)
quantities : ['norm', 'norm_err', 'ts', 'npred', 'npred_excess', 'stat', 'counts', 'success'] # noqa: E501
ref. model : pl
n_sigma : 1
n_sigma_ul : 2
sqrt_ts_threshold_ul : 2
sed type init : likelihood
"""
tag = "FluxProfileEstimator"
def __init__(self, regions, spectrum=None, **kwargs):
if len(regions) <= 1:
raise ValueError(
"Please provide at least two regions for flux profile estimation."
)
self.regions = regions
if spectrum is None:
spectrum = PowerLawSpectralModel()
self.spectrum = spectrum
super().__init__(**kwargs)
@property
def projected_distance_axis(self):
"""Get projected distance from the first region.
For normal region this is defined as the distance from the
center of the region. For annulus shaped regions it is the
mean between the inner and outer radius.
Returns
-------
axis : `MapAxis`
Projected distance axis.
"""
distances = []
center = self.regions[0].center
for idx, region in enumerate(self.regions):
if isinstance(region, CircleAnnulusSkyRegion):
distance = (region.inner_radius + region.outer_radius) / 2.0
else:
distance = center.separation(region.center)
distances.append(distance)
return MapAxis.from_nodes(
u.Quantity(distances, "deg"), name="projected-distance"
)
[docs] def run(self, datasets):
"""Run flux profile estimation.
Parameters
----------
datasets : list of `~gammapy.datasets.MapDataset`
Map datasets.
Returns
-------
profile : `~gammapy.estimators.FluxPoints`
Profile flux points.
"""
datasets = Datasets(datasets=datasets)
maps = []
for region in self.regions:
datasets_to_fit = datasets.to_spectrum_datasets(region=region)
for dataset_spec, dataset_map in zip(datasets_to_fit, datasets):
dataset_spec.background.data = (
dataset_map.npred()
.to_region_nd_map(
region, func=np.sum, weights=dataset_map.mask_safe
)
.data
)
datasets_to_fit.models = SkyModel(self.spectrum, name="test-source")
fp = super().run(datasets_to_fit)
maps.append(fp)
return FluxPoints.from_stack(
maps=maps,
axis=self.projected_distance_axis,
)
@property
def config_parameters(self):
"""Configuration parameters."""
pars = self.__dict__.copy()
pars = {key.strip("_"): value for key, value in pars.items()}
pars.pop("regions")
return pars