gammapy/estimators/parameter.py
def estimate_scan(self, datasets, parameter):
        """Estimate parameter stat scan.

        Parameters
        ----------
        datasets : `~gammapy.datasets.Datasets`
            The datasets used to estimate the model parameter
        parameter : `Parameter`
            For which parameter to get the value

        Returns
        -------
        result : dict
            Dict with the various parameter estimation values.

        """
        self._setup_fit(datasets)
        self._fit.optimize()

        if self.scan_min and self.scan_max:
            bounds = (self.scan_min, self.scan_max)
        else:
            bounds = self.scan_n_sigma

        profile = self._fit.stat_profile(
            parameter=parameter,
            values=self.scan_values,
            bounds=bounds,
            nvalues=self.scan_n_values,
            reoptimize=self.reoptimize,
        )

        return {
            f"{parameter.name}_scan": profile[f"{parameter.name}_scan"],
            "stat_scan": profile["stat_scan"],
        }
Estimate parameter stat scan.

        Parameters
        ----------
        datasets : `~gammapy.datasets.Datasets`
            The datasets used to estimate the model parameter
        parameter : `Parameter`
            For which parameter to get the value

        Returns
        -------
        result : dict
            Dict with the various parameter estimation values.

        
Estimate parameter stat scan.
Parameters

datasets : `~gammapy.datasets.Datasets`
The datasets used to estimate the model parameter
parameter : `Parameter`
For which parameter to get the value

Returns

result : dict
Dict with the various parameter estimation values.
Estimate parameter stat scan . Parameters datasets : ` ~gammapy . datasets . Datasets ` The datasets used to estimate the model parameter parameter : ` Parameter ` For which parameter to get the value Returns result : dict Dict with the various parameter estimation values .
def estimate_scan(self, datasets, parameter):
        self._setup_fit(datasets)
        self._fit.optimize()
        if self.scan_min and self.scan_max:
            bounds = (self.scan_min, self.scan_max)
        else:
            bounds = self.scan_n_sigma
        profile = self._fit.stat_profile(
            parameter=parameter,
            values=self.scan_values,
            bounds=bounds,
            nvalues=self.scan_n_values,
            reoptimize=self.reoptimize,
        )
        return {
            f"{parameter.name}_scan": profile[f"{parameter.name}_scan"],
            "stat_scan": profile["stat_scan"],
        }
def estimate_scan ( self , datasets , parameter ) : self . _setup_fit ( datasets ) self . _fit . optimize ( ) if self . scan_min and self . scan_max : bounds = ( self . scan_min , self . scan_max ) else : bounds = self . scan_n_sigma profile = self . _fit . stat_profile ( parameter = parameter , values = self . scan_values , bounds = bounds , nvalues = self . scan_n_values , reoptimize = self . reoptimize , ) return { f"{parameter.name}_scan" : profile [ f"{parameter.name}_scan" ] , "stat_scan" : profile [ "stat_scan" ] , }
Estimate parameter stat scan.
Estimate parameter stat scan .
"""Estimate parameter stat scan.

        Parameters
        ----------
        datasets : `~gammapy.datasets.Datasets`
            The datasets used to estimate the model parameter
        parameter : `Parameter`
            For which parameter to get the value

        Returns
        -------
        result : dict
            Dict with the various parameter estimation values.

        """
self
datasets
parameter