# LightCurveEstimator¶

class gammapy.time.LightCurveEstimator(datasets, source='', norm_min=0.2, norm_max=5, norm_n_values=11, norm_values=None, sigma=1, sigma_ul=2, reoptimize=False)[source]

Bases: object

Estimate flux points for a given list of datasets, each per time bin.

Parameters: datasets : Spectrum or Map datasets. source : str For which source in the model to compute the flux points. Default is ‘’ norm_min : float Minimum value for the norm used for the likelihood profile evaluation. norm_max : float Maximum value for the norm used for the likelihood profile evaluation. norm_n_values : int Number of norm values used for the likelihood profile. norm_values : numpy.ndarray Array of norm values to be used for the likelihood profile. sigma : int Sigma to use for asymmetric error computation. sigma_ul : int Sigma to use for upper limit computation. reoptimize : bool reoptimize other parameters during likelihod scan

Attributes Summary

Methods Summary

 estimate_counts(self, dataset) Estimate counts for the flux point. estimate_norm(self) Fit norm of the flux point. estimate_norm_err(self) Estimate covariance errors for a flux point. estimate_norm_errn_errp(self) Estimate asymmetric errors for a flux point. estimate_norm_scan(self) Estimate likelihood profile for the norm parameter. estimate_norm_ts(self) Estimate ts and sqrt(ts) for the flux point. estimate_norm_ul(self, dataset) Estimate upper limit for a flux point. estimate_time_bin_flux(self, dataset[, steps]) Estimate flux point for a single energy group. run(self, e_ref, e_min, e_max[, steps]) Run light curve extraction.

Attributes Documentation

ref_model

Methods Documentation

estimate_counts(self, dataset)[source]

Estimate counts for the flux point.

Parameters: dataset : Dataset the dataset object result : dict Dict with an array with one entry per dataset with counts for the flux point.
estimate_norm(self)[source]

Fit norm of the flux point.

Returns: result : dict Dict with “norm” and “loglike” for the flux point.
estimate_norm_err(self)[source]

Estimate covariance errors for a flux point.

Returns: result : dict Dict with symmetric error for the flux point norm.
estimate_norm_errn_errp(self)[source]

Estimate asymmetric errors for a flux point.

Returns: result : dict Dict with asymmetric errors for the flux point norm.
estimate_norm_scan(self)[source]

Estimate likelihood profile for the norm parameter.

Returns: result : dict Dict with norm_scan and dloglike_scan for the flux point.
estimate_norm_ts(self)[source]

Estimate ts and sqrt(ts) for the flux point.

Returns: result : dict Dict with ts and sqrt(ts) for the flux point.
estimate_norm_ul(self, dataset)[source]

Estimate upper limit for a flux point.

Returns: result : dict Dict with upper limit for the flux point norm.
estimate_time_bin_flux(self, dataset, steps='all')[source]

Estimate flux point for a single energy group.

Parameters: steps : list of str Which steps to execute. Available options are: “err”: estimate symmetric error. “errn-errp”: estimate asymmetric errors. “ul”: estimate upper limits. “ts”: estimate ts and sqrt(ts) values. “norm-scan”: estimate likelihood profiles. By default all steps are executed. result : dict Dict with results for the flux point.
run(self, e_ref, e_min, e_max, steps='all')[source]

Run light curve extraction.

Normalize integral and energy flux between emin and emax.

Parameters: e_ref : Quantity reference energy of dnde flux normalization e_min : Quantity minimum energy of integral and energy flux normalization interval e_max : Quantity minimum energy of integral and energy flux normalization interval steps : list of str Which steps to execute. Available options are: “err”: estimate symmetric error. “errn-errp”: estimate asymmetric errors. “ul”: estimate upper limits. “ts”: estimate ts and sqrt(ts) values. “norm-scan”: estimate likelihood profiles. By default all steps are executed. lightcurve : LightCurve the Light Curve object