LightCurveEstimator#
- class gammapy.estimators.LightCurveEstimator(time_intervals=None, atol='1e-6 s', **kwargs)[source]#
Bases:
gammapy.estimators.points.sed.FluxPointsEstimatorEstimate light curve.
The estimator will apply flux point estimation on the source model component to datasets in each of the provided time intervals. The normalization is the only parameter of the source model left free to vary. Other model components can be left free to vary with the reoptimize option.
If no time intervals are provided, the estimator will use the time intervals defined by the datasets GTIs.
To be included in the estimation, the dataset must have their GTI fully overlapping a time interval.
Time intervals without any dataset GTI fully overlapping will be dropped. They will not be stored in the final lightcurve
FluxPointsobject.- Parameters
- time_intervalslist of
astropy.time.Time Start and stop time for each interval to compute the LC
- sourcestr or int
For which source in the model to compute the flux points. Default is 0
- energy_edges
Quantity Energy edges of the light curve.
- atol
Quantity Tolerance value for time comparison with different scale. Default 1e-6 sec.
- norm_minfloat
Minimum value for the norm used for the fit statistic profile evaluation.
- norm_maxfloat
Maximum value for the norm used for the fit statistic profile evaluation.
- norm_n_valuesint
Number of norm values used for the fit statistic profile.
- norm_values
numpy.ndarray Array of norm values to be used for the fit statistic profile.
- n_sigmaint
Number of sigma to use for asymmetric error computation. Default is 1.
- n_sigma_ulint
Number of sigma to use for upper limit computation. Default is 2.
- selection_optionallist of str
Which steps to execute. Available options are:
“all”: all the optional steps are executed
“errn-errp”: estimate asymmetric errors.
“ul”: estimate upper limits.
“scan”: estimate fit statistic profiles.
Default is None so the optional steps are not executed.
- fit
Fit Fit instance specifying the backend and fit options.
- reoptimizebool
Re-optimize other free model parameters. Default is True.
- time_intervalslist of
Examples
For a usage example see Light curves tutorial.
Attributes Summary
Config parameters
Methods Summary
copy()Copy estimator
estimate_best_fit(datasets, parameter)Estimate parameter asymmetric errors
estimate_counts(datasets)Estimate counts for the flux point.
estimate_errn_errp(datasets, parameter)Estimate parameter asymmetric errors
estimate_flux_point(datasets, energy_min, ...)Estimate flux point for a single energy group.
estimate_npred(datasets)Estimate npred for the flux point.
estimate_npred_excess(datasets)Estimate npred excess for the source.
estimate_scan(datasets, parameter)Estimate parameter stat scan.
estimate_time_bin_flux(datasets)Estimate flux point for a single energy group.
estimate_ts(datasets, parameter)Estimate parameter ts
estimate_ul(datasets, parameter)Estimate parameter ul.
expand_map(m, dataset_names)Expand map in dataset axis
get_scale_model(models)Set scale model
run(datasets)Run light curve extraction.
Attributes Documentation
- config_parameters#
Config parameters
- selection_optional#
- tag = 'LightCurveEstimator'#
Methods Documentation
- copy()#
Copy estimator
- estimate_best_fit(datasets, parameter)#
Estimate parameter asymmetric errors
- Parameters
- datasets
Datasets Datasets
- parameter
Parameter For which parameter to get the value
- datasets
- Returns
- resultdict
Dict with the various parameter estimation values. Entries are:
parameter.name: best fit parameter value
“stat”: best fit total stat.
“success”: boolean flag for fit success
parameter.name_err: covariance-based error estimate on parameter value
- static estimate_counts(datasets)#
Estimate counts for the flux point.
- Parameters
- datasetsDatasets
Datasets
- Returns
- resultdict
Dict with an array with one entry per dataset with the sum of the masked counts.
- estimate_errn_errp(datasets, parameter)#
Estimate parameter asymmetric errors
- Parameters
- datasets
Datasets Datasets
- parameter
Parameter For which parameter to get the value
- datasets
- Returns
- resultdict
Dict with the parameter asymmetric errors. Entries are:
{parameter.name}_errp : positive error on parameter value
{parameter.name}_errn : negative error on parameter value
- estimate_flux_point(datasets, energy_min, energy_max)#
Estimate flux point for a single energy group.
- Parameters
- datasets
Datasets Datasets
- energy_min, energy_max
Quantity Energy bounds to compute the flux point for.
- datasets
- Returns
- resultdict
Dict with results for the flux point.
- static estimate_npred(datasets)#
Estimate npred for the flux point.
- Parameters
- datasetsDatasets
Datasets
- Returns
- resultdict
Dict with an array with one entry per dataset with the sum of the masked npred.
- estimate_npred_excess(datasets)#
Estimate npred excess for the source.
- Parameters
- datasetsDatasets
Datasets
- Returns
- resultdict
Dict with an array with one entry per dataset with the sum of the masked npred excess.
- estimate_scan(datasets, parameter)#
Estimate parameter stat scan.
- Parameters
- datasets
Datasets The datasets used to estimate the model parameter
- parameter
Parameter For which parameter to get the value
- datasets
- Returns
- resultdict
Dict with the parameter fit scan values. Entries are:
parameter.name_scan : parameter values scan
“stat_scan” : fit statistic values scan
- estimate_time_bin_flux(datasets)[source]#
Estimate flux point for a single energy group.
- Parameters
- datasets
Datasets List of dataset objects
- datasets
- Returns
- result
FluxPoints Resulting flux points.
- result
- estimate_ts(datasets, parameter)#
Estimate parameter ts
- Parameters
- datasets
Datasets Datasets
- parameter
Parameter For which parameter to get the value
- datasets
- Returns
- resultdict
Dict with the TS of the best fit value compared to the null hypothesis. Entries are:
“ts” : fit statistic difference with null hypothesis
“npred” : predicted number of counts per dataset
- estimate_ul(datasets, parameter)#
Estimate parameter ul.
- Parameters
- datasets
Datasets The datasets used to estimate the model parameter
- parameter
Parameter For which parameter to get the value
- datasets
- Returns
- resultdict
Dict with the parameter ULs. Entries are:
parameter.name_ul : upper limit on parameter value
- static expand_map(m, dataset_names)[source]#
Expand map in dataset axis
- Parameters
- map
Map Map to expand.
- dataset_nameslist of str
Dataset names
- map
- Returns
- map
Map Expanded map.
- map
- get_scale_model(models)#
Set scale model
- Parameters
- models
Models Models
- models
- Returns
- model
ScaleSpectralModel Scale spectral model
- model
- run(datasets)[source]#
Run light curve extraction.
Normalize integral and energy flux between emin and emax.
- Parameters
- datasetslist of
SpectrumDatasetorMapDataset Spectrum or Map datasets.
- datasetslist of
- Returns
- lightcurve
FluxPoints Light curve flux points
- lightcurve