gammapy/estimators/excess_map.py
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
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

        
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



Returns

images : dict
Dictionary containing result correlated maps. Keys are.


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 Returns images : dict Dictionary containing result correlated maps . Keys are .
def run(self, dataset):
        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)
        resampled_dataset.models = dataset.models
        result = self.estimate_excess_map(resampled_dataset)
        return result
def run ( self , dataset ) : 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 ) resampled_dataset . models = dataset . models result = self . estimate_excess_map ( resampled_dataset ) return result
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.
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 .
"""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

        """ # Beware we rely here on the correct npred background in MapDataset.resample_energy_axis
self
dataset