Source code for gammapy.estimators.excess_map

# Licensed under a 3-clause BSD style license - see LICENSE.rst
import copy
import logging
import numpy as np
import astropy.units as u
from astropy.convolution import Tophat2DKernel
from astropy.coordinates import Angle
from gammapy.datasets import MapDataset, MapDatasetOnOff
from gammapy.maps import Map, MapAxis
from gammapy.stats import CashCountsStatistic, WStatCountsStatistic
from .core import Estimator
from .utils import estimate_exposure_reco_energy

__all__ = [

log = logging.getLogger(__name__)

def convolved_map_dataset_counts_statistics(dataset, kernel, mask):
    """Return CountsDataset objects containing smoothed maps from the MapDataset"""
    # Kernel is modified later make a copy here
    kernel = copy.deepcopy(kernel)

    # 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):
        background = dataset.background * mask[ == 0] = 0.0
        n_off = dataset.counts_off * mask

        background_conv = background.convolve(kernel.array)
        n_off_conv = n_off.convolve(kernel.array)

        npred_sig = dataset.npred_signal() * mask
        mu_sig = npred_sig.convolve(kernel.array)

        with np.errstate(invalid="ignore", divide="ignore"):
            alpha_conv = background_conv / n_off_conv

        return WStatCountsStatistic(

        npred = dataset.npred() * mask
        background_conv = npred.convolve(kernel.array)
        return CashCountsStatistic(,

[docs]class ExcessMapEstimator(Estimator): """Computes correlated excess, sqrt TS (i.e. Li-Ma significance) and errors for MapDatasets. 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 ---------- correlation_radius : ~astropy.coordinate.Angle correlation radius to use n_sigma : float Confidence level for the asymmetric errors expressed in number of sigma. Default is 1. n_sigma_ul : float Confidence level for the upper limits expressed in number of sigma. Default is 3. selection_optional : list of str Which additional maps to estimate besides delta TS, significance and symmetric error. Available options are: * "errn-errp": estimate asymmetric errors. * "ul": estimate upper limits. By default all additional quantities are estimated. energy_edges : `~astropy.units.Quantity` Energy edges of the target excess maps bins. apply_mask_fit : Bool Apply a mask for the computation. A `~gammapy.datasets.MapDataset.mask_fit` must be present on the input dataset """ tag = "ExcessMapEstimator" _available_selection_optional = ["errn-errp", "ul"] def __init__( self, correlation_radius="0.1 deg", n_sigma=1, n_sigma_ul=3, selection_optional="all", energy_edges=None, apply_mask_fit=False, ): self.correlation_radius = correlation_radius self.n_sigma = n_sigma self.n_sigma_ul = n_sigma_ul self.apply_mask_fit = apply_mask_fit self.selection_optional = selection_optional self.energy_edges = energy_edges @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` input dataset Returns ------- images : dict Dictionary containing result correlated maps. Keys are: * counts : correlated counts map * background : correlated background map * excess : correlated excess map * ts : TS map * sqrt_ts : sqrt(delta TS), or Li-Ma significance map * err : symmetric error map (from covariance) * flux : flux map. An exposure map must be present in the dataset to compute flux map * errn : negative error map * errp : positive error map * ul : upper limit map """ if not isinstance(dataset, MapDataset): raise ValueError("Unsupported dataset type") if self.energy_edges is None: energy_axis = dataset.counts.geom.axes["energy"] energy_edges = u.Quantity([energy_axis.edges[0], energy_axis.edges[-1]]) else: energy_edges = self.energy_edges axis = MapAxis.from_energy_edges(energy_edges) resampled_dataset = dataset.resample_energy_axis(energy_axis=axis) # Beware we rely here on the correct npred background in MapDataset.resample_energy_axis resampled_dataset.models = dataset.models 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 self.apply_mask_fit: mask = dataset.mask elif dataset.mask_safe: mask = dataset.mask_safe else: mask = np.ones(dataset.data_shape, dtype=bool) counts_stat = convolved_map_dataset_counts_statistics(dataset, kernel, mask) n_on = Map.from_geom(geom, data=counts_stat.n_on) bkg = Map.from_geom(geom, data=counts_stat.n_on - counts_stat.n_sig) excess = Map.from_geom(geom, data=counts_stat.n_sig) result = {"counts": n_on, "background": bkg, "excess": excess} tsmap = Map.from_geom(geom, data=counts_stat.ts) sqrt_ts = Map.from_geom(geom, data=counts_stat.sqrt_ts) result.update({"ts": tsmap, "sqrt_ts": sqrt_ts}) err = Map.from_geom(geom, data=counts_stat.error * self.n_sigma) result.update({"err": err}) if dataset.exposure: reco_exposure = estimate_exposure_reco_energy(dataset) flux = excess / reco_exposure flux.quantity ="1 / (cm2 s)") else: flux = Map.from_geom( geom=dataset.counts.geom, data=np.nan * np.ones(dataset.data_shape) ) result.update({"flux": flux}) if "errn-errp" in self.selection_optional: errn = Map.from_geom(geom, data=counts_stat.compute_errn(self.n_sigma)) errp = Map.from_geom(geom, data=counts_stat.compute_errp(self.n_sigma)) result.update({"errn": errn, "errp": errp}) if "ul" in self.selection_optional: ul = Map.from_geom( geom, data=counts_stat.compute_upper_limit(self.n_sigma_ul) ) result.update({"ul": ul}) # return nan values outside mask for key in result: result[key].data[~mask] = np.nan return result