estimators - High level estimators#

gammapy.estimators Package#



ASmoothMapEstimator([scales, kernel, ...])

Adaptively smooth counts image.


Abstract estimator base class.

ExcessMapEstimator([correlation_radius, ...])

Computes correlated excess, significance and error maps from a map dataset.

FluxMaps(data, reference_model[, meta, gti, ...])

A flux map / points container.

FluxPoints(data, reference_model[, meta, ...])

Flux points container.

FluxPointsEstimator([energy_edges, ...])

Flux points estimator.

FluxProfileEstimator(regions[, spectrum])

Estimate flux profiles.


Image profile class.

ImageProfileEstimator([x_edges, method, ...])

Estimate profile from image.

LightCurveEstimator([time_intervals, atol])

Estimate light curve.

ParameterEstimator([n_sigma, n_sigma_ul, ...])

Model parameter estimator.

SensitivityEstimator([spectrum, n_sigma, ...])

Estimate sensitivity.

TSMapEstimator([model, kernel_width, ...])

Compute test statistic map from a MapDataset using different optimization methods.


Test if there is any energy-dependent morphology in a map dataset for a given set of energy bins.

FluxMetaData(*, sed_type, sed_type_init, ...)

Metadata containing information about the FluxPoints and FluxMaps.



Registry of estimator classes in Gammapy.

gammapy.estimators.utils Module#


get_combined_significance_maps(estimator, ...)

Computes excess and significance for a set of datasets.

estimate_exposure_reco_energy(dataset[, ...])

Estimate an exposure map in reconstructed energy.

find_peaks(image, threshold[, min_distance])

Find local peaks in an image.

find_peaks_in_flux_map(maps, threshold[, ...])

Find local test statistic peaks for a given Map.

resample_energy_edges(dataset[, conditions])

Return energy edges that satisfy given condition on the per bin statistics.

get_rebinned_axis(fluxpoint[, axis_name, method])

Get the rebinned axis for resampling the flux point object along the mentioned axis.

compute_lightcurve_fvar(lightcurve[, ...])

Compute the fractional excess variance of the input lightcurve.

compute_lightcurve_fpp(lightcurve[, ...])

Compute the point-to-point excess variance of the input lightcurve.


Compute the minimum characteristic flux doubling and halving time for the input lightcurve.