Source code for gammapy.makers.background.fov

# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""FoV background estimation."""
import logging
from gammapy.maps import Map

__all__ = ["FoVBackgroundMaker"]

log = logging.getLogger(__name__)


[docs]class FoVBackgroundMaker: """Normalize template background on the whole field-of-view. The dataset background model can be simply scaled (method="scale") or fitted (method="fit") on the dataset counts. The normalization is performed outside the exclusion mask that is passed on init. If a SkyModel is set on the input dataset and method is 'fit', its are frozen during the fov normalization fit. Parameters ---------- method : str in ['fit', 'scale'] the normalization method to be applied. Default 'scale'. exclusion_mask : `~gammapy.maps.WcsNDMap` Exclusion mask """ def __init__(self, method="scale", exclusion_mask=None): if method in ["fit", "scale"]: self.method = method else: raise ValueError(f"Incorrect method for FoVBackgroundMaker: {method}.") self.exclusion_mask = exclusion_mask
[docs] def run(self, dataset): """Run FoV background maker. Fit the background model norm Parameters ---------- dataset : `~gammapy.cube.fit.MapDataset` Input map dataset. """ mask_fit = dataset.mask_fit dataset.mask_fit = self._reproject_exclusion_mask(dataset) if self.method is "fit": self._fit_bkg(dataset) else: self._scale_bkg(dataset) dataset.mask_fit = mask_fit return dataset
def _reproject_exclusion_mask(self, dataset): """Reproject the exclusion on the dataset geometry""" mask_map = Map.from_geom(dataset.counts.geom) if self.exclusion_mask is not None: coords = dataset.counts.geom.get_coord() vals = self.exclusion_mask.get_by_coord(coords) mask_map.data += vals else: mask_map.data[...] = 1 return mask_map.data.astype("bool") def _fit_bkg(self, dataset): """Fit the FoV background model on the dataset counts data""" from gammapy.modeling import Fit from gammapy.datasets import Datasets # freeze all model components not related to background model datasets = Datasets([dataset]) parameters_frozen = [] for par in datasets.parameters: parameters_frozen.append(par.frozen) if par not in dataset.background_model.parameters: par.frozen = True fit = Fit(datasets) fit_result = fit.run() if fit_result.success is False: log.info( f"FoVBackgroundMaker failed. No fit convergence for {dataset.name}." ) # Unfreeze parameters for idx, par in enumerate(datasets.parameters): par.frozen = parameters_frozen[idx] def _scale_bkg(self, dataset): """Fit the FoV background model on the dataset counts data""" mask = dataset.mask count_tot = dataset.counts.data[mask].sum() bkg_tot = dataset.background_model.map.data[mask].sum() if count_tot <= 0.0: log.info( f"FoVBackgroundMaker failed. No counts found outside exclusion mask for {dataset.name}." ) elif bkg_tot <= 0.0: log.info( f"FoVBackgroundMaker failed. No positive background found outside exclusion mask for {dataset.name}." ) else: scale = count_tot / bkg_tot dataset.background_model.norm.value = scale