# Licensed under a 3-clause BSD style license - see LICENSE.rst
import copy
import logging
import numpy as np
from astropy.convolution import Tophat2DKernel
from astropy.coordinates import Angle
from gammapy.datasets import MapDataset, MapDatasetOnOff
from gammapy.maps import Map
from gammapy.modeling.models import PowerLawSpectralModel, SkyModel
from gammapy.stats import CashCountsStatistic, WStatCountsStatistic
from ..core import Estimator
from ..utils import estimate_exposure_reco_energy
from .core import FluxMaps
__all__ = [
"ExcessMapEstimator",
]
log = logging.getLogger(__name__)
def convolved_map_dataset_counts_statistics(dataset, kernel, mask, correlate_off):
"""Return CountsDataset objects containing smoothed maps from the MapDataset"""
# Kernel is modified later make a copy here
kernel = copy.deepcopy(kernel)
kernel.normalize("peak")
# fft convolution adds numerical noise, to ensure integer results we call
# np.rint
n_on = dataset.counts * mask
n_on_conv = np.rint(n_on.convolve(kernel.array).data)
if isinstance(dataset, MapDatasetOnOff):
n_off = dataset.counts_off * mask
npred_sig = dataset.npred_signal() * mask
acceptance_on = dataset.acceptance * mask
acceptance_off = dataset.acceptance_off * mask
npred_sig_convolve = npred_sig.convolve(kernel.array)
if correlate_off:
background = dataset.background * mask
background.data[dataset.acceptance_off == 0] = 0.0
background_conv = background.convolve(kernel.array)
n_off = n_off.convolve(kernel.array)
with np.errstate(invalid="ignore", divide="ignore"):
alpha = background_conv / n_off
else:
acceptance_on_convolve = acceptance_on.convolve(kernel.array)
with np.errstate(invalid="ignore", divide="ignore"):
alpha = acceptance_on_convolve / acceptance_off
return WStatCountsStatistic(
n_on_conv.data, n_off.data, alpha.data, npred_sig_convolve.data
)
else:
npred = dataset.npred() * mask
background_conv = npred.convolve(kernel.array)
return CashCountsStatistic(n_on_conv.data, background_conv.data)
[docs]class ExcessMapEstimator(Estimator):
"""Computes correlated excess, significance and error maps from a map dataset.
If a model is set on the dataset the excess map estimator will compute the
excess taking into account the predicted counts of the model.
.. note::
By default the excess estimator correlates the off counts as well to avoid
artifacts at the edges of the :term:`FoV` for stacked on-off datasets.
However when the on-off dataset has been derived from a ring background
estimate, this leads to the off counts being correlated twice. To avoid
artifacts and the double correlation, the `ExcessMapEstimator` has to
be applied per dataset and the resulting maps need to be stacked, taking
the :term:`FoV` cut into account.
Parameters
----------
correlation_radius : ~astropy.coordinate.Angle
correlation radius to use
n_sigma : float
Confidence level for the asymmetric errors expressed in number of sigma.
n_sigma_ul : float
Confidence level for the upper limits expressed in number of sigma.
selection_optional : list of str
Which additional maps to estimate besides delta TS, significance and symmetric error.
Available options are:
* "all": all the optional steps are executed
* "errn-errp": estimate asymmetric errors.
* "ul": estimate upper limits.
Default is None so the optional steps are not executed.
energy_edges : `~astropy.units.Quantity`
Energy edges of the target excess maps bins.
correlate_off : bool
Correlate OFF events. Default is True.
spectral_model : `~gammapy.modeling.models.SpectralModel`
Spectral model used for the computation of the flux map.
If None, a Power Law of index 2 is assumed (default).
Examples
--------
>>> from gammapy.datasets import MapDataset
>>> from gammapy.estimators import ExcessMapEstimator
>>> dataset = MapDataset.read("$GAMMAPY_DATA/cta-1dc-gc/cta-1dc-gc.fits.gz")
>>> estimator = ExcessMapEstimator(correlation_radius="0.1 deg")
>>> result = estimator.run(dataset)
>>> print(result)
FluxMaps
--------
<BLANKLINE>
geom : WcsGeom
axes : ['lon', 'lat', 'energy']
shape : (320, 240, 1)
quantities : ['npred', 'npred_excess', 'counts', 'ts', 'sqrt_ts', 'norm', 'norm_err'] # noqa: E501
ref. model : pl
n_sigma : 1
n_sigma_ul : 2
sqrt_ts_threshold_ul : 2
sed type init : likelihood
"""
tag = "ExcessMapEstimator"
_available_selection_optional = ["errn-errp", "ul"]
def __init__(
self,
correlation_radius="0.1 deg",
n_sigma=1,
n_sigma_ul=2,
selection_optional=None,
energy_edges=None,
correlate_off=True,
spectral_model=None,
):
self.correlation_radius = correlation_radius
self.n_sigma = n_sigma
self.n_sigma_ul = n_sigma_ul
self.selection_optional = selection_optional
self.energy_edges = energy_edges
self.correlate_off = correlate_off
if spectral_model is None:
spectral_model = PowerLawSpectralModel(index=2)
self.spectral_model = spectral_model
@property
def correlation_radius(self):
return self._correlation_radius
@correlation_radius.setter
def correlation_radius(self, correlation_radius):
"""Sets radius"""
self._correlation_radius = Angle(correlation_radius)
[docs] def run(self, dataset):
"""Compute correlated excess, Li & Ma significance and flux maps
If a model is set on the dataset the excess map estimator will compute
the excess taking into account the predicted counts of the model.
Parameters
----------
dataset : `~gammapy.datasets.MapDataset` or `~gammapy.datasets.MapDatasetOnOff`
Map dataset
Returns
-------
maps : `FluxMaps`
Flux maps
"""
if not isinstance(dataset, MapDataset):
raise ValueError(
"Unsupported dataset type. Excess map is not applicable to 1D datasets."
)
axis = self._get_energy_axis(dataset)
resampled_dataset = dataset.resample_energy_axis(
energy_axis=axis, name=dataset.name
)
if isinstance(dataset, MapDatasetOnOff):
resampled_dataset.models = dataset.models
else:
resampled_dataset.background = dataset.npred().resample_axis(axis=axis)
resampled_dataset.models = None
result = self.estimate_excess_map(resampled_dataset)
return result
[docs] def estimate_excess_map(self, dataset):
"""Estimate excess and ts maps for single dataset.
If exposure is defined, a flux map is also computed.
Parameters
----------
dataset : `MapDataset`
Map dataset
"""
pixel_size = np.mean(np.abs(dataset.counts.geom.wcs.wcs.cdelt))
size = self.correlation_radius.deg / pixel_size
kernel = Tophat2DKernel(size)
geom = dataset.counts.geom
if dataset.mask_fit:
mask = dataset.mask
elif dataset.mask_safe:
mask = dataset.mask_safe
else:
mask = Map.from_geom(geom, data=True, dtype=bool)
counts_stat = convolved_map_dataset_counts_statistics(
dataset, kernel, mask, self.correlate_off
)
maps = {}
maps["npred"] = Map.from_geom(geom, data=counts_stat.n_on)
maps["npred_excess"] = Map.from_geom(geom, data=counts_stat.n_sig)
maps["counts"] = maps["npred"]
maps["ts"] = Map.from_geom(geom, data=counts_stat.ts)
maps["sqrt_ts"] = Map.from_geom(geom, data=counts_stat.sqrt_ts)
if dataset.exposure:
reco_exposure = estimate_exposure_reco_energy(
dataset, self.spectral_model, normalize=False
)
with np.errstate(invalid="ignore", divide="ignore"):
reco_exposure = reco_exposure.convolve(kernel.array) / mask.convolve(
kernel.array
)
else:
reco_exposure = 1
with np.errstate(invalid="ignore", divide="ignore"):
maps["norm"] = maps["npred_excess"] / reco_exposure
maps["norm_err"] = (
Map.from_geom(geom, data=counts_stat.error * self.n_sigma)
/ reco_exposure
)
if "errn-errp" in self.selection_optional:
maps["norm_errn"] = (
Map.from_geom(geom, data=counts_stat.compute_errn(self.n_sigma))
/ reco_exposure
)
maps["norm_errp"] = (
Map.from_geom(geom, data=counts_stat.compute_errp(self.n_sigma))
/ reco_exposure
)
if "ul" in self.selection_optional:
maps["norm_ul"] = (
Map.from_geom(
geom, data=counts_stat.compute_upper_limit(self.n_sigma_ul)
)
/ reco_exposure
)
# return nan values outside mask
for name in maps:
maps[name].data[~mask] = np.nan
meta = {
"n_sigma": self.n_sigma,
"n_sigma_ul": self.n_sigma_ul,
"sed_type_init": "likelihood",
}
return FluxMaps.from_maps(
maps=maps,
meta=meta,
reference_model=SkyModel(self.spectral_model),
sed_type="likelihood",
)