estimators - High level estimators

Introduction

The gammapy.estimators submodule contains algorithms and classes for high level flux and significance estimation such as TS maps, flux points and light curves.

Getting Started

An Estimator takes a reduced dataset as input.

Reference/API

gammapy.estimators Package

Estimators.

Classes

ASmoothMapEstimator(scales[, kernel, …])

Adaptively smooth counts image.

Datasets([datasets])

Dataset collection.

ExcessMapEstimator([correlation_radius, …])

Computes correlated excess, significance and errors for MapDatasets.

Fit(datasets)

Fit class.

FluxEstimator(source, energy_range[, …])

Flux estimator.

FluxPoints(table)

Flux points container.

FluxPointsEstimator(e_edges[, source, …])

Flux points estimator.

ImageProfile(table)

Image profile class.

ImageProfileEstimator([x_edges, method, …])

Estimate profile from image.

LightCurve(table)

Lightcurve container.

LightCurveEstimator([time_intervals, …])

Compute light curve.

ParameterEstimator([sigma, sigma_ul, …])

Model parameter estimator.

ScaleSpectralModel(model[, norm])

Wrapper to scale another spectral model by a norm factor.

SensitivityEstimator([spectrum, sigma, …])

Estimate differential sensitivity.

TSMapEstimator([model, kernel_width, …])

Compute TS map from a MapDataset using different optimization methods.

Variables

log

Instances of the Logger class represent a single logging channel.