ParameterEstimator

class gammapy.estimators.ParameterEstimator(n_sigma=1, n_sigma_ul=2, n_sigma_sensitivity=5, null_value=1e-150, selection_optional=None, fit=None, reoptimize=True)

Bases: Estimator

Model parameter estimator.

Estimates a model parameter for a group of datasets. Compute best fit value, symmetric and delta(TS) for a given null value. Additionally asymmetric errors as well as parameter upper limit and fit statistic profile can be estimated.

Parameters:

n_sigmaint

Sigma to use for asymmetric error computation. Default is 1.

n_sigma_ulint

Sigma to use for upper limit computation. Default is 2.

n_sigma_sensitivityint

Sigma to use for sensitivity computation. Default is 5.

null_valuefloat

Which null value to use for the parameter.

selection_optionallist of str, optional

Which additional quantities to estimate. Available options are:

• “all”: all the optional steps are executed.

• “errn-errp”: estimate asymmetric errors on parameter best fit value.

• “ul”: estimate upper limits.

• “scan”: estimate fit statistic profiles.

Default is None so the optional steps are not executed.

fitFit

Fit instance specifying the backend and fit options.

reoptimizebool

Re-optimize other free model parameters. Default is True.

Examples

>>> from gammapy.datasets import SpectrumDatasetOnOff, Datasets
>>> from gammapy.modeling.models import SkyModel, PowerLawSpectralModel
>>> from gammapy.estimators import ParameterEstimator
>>>
>>> filename = "$GAMMAPY_DATA/joint-crab/spectra/hess/pha_obs23523.fits"
>>> dataset = SpectrumDatasetOnOff.read(filename)
>>> datasets = Datasets([dataset])
>>> spectral_model = PowerLawSpectralModel(amplitude="3e-11 cm-2s-1TeV-1", index=2.7)
>>>
>>> model = SkyModel(spectral_model=spectral_model, name="Crab")
>>> model.spectral_model.amplitude.scan_n_values = 10
>>>
>>> for dataset in datasets:
...     dataset.models = model
>>>
>>> estimator = ParameterEstimator(selection_optional="all")
>>> result = estimator.run(datasets, parameter="amplitude")

Attributes Summary

config_parameters	Configuration parameters.
selection_optional
tag

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_npred(datasets)	Estimate npred for the flux point.
estimate_scan(datasets, parameter)	Estimate parameter statistic scan.
estimate_sensitivity(datasets, parameter)	Estimate norm sensitivity for the flux point.
estimate_ts(datasets, parameter)	Estimate parameter ts.
estimate_ul(datasets, parameter)	Estimate parameter ul.
run(datasets, parameter)	Run the parameter estimator.

Attributes Documentation

config_parameters

Configuration parameters.

selection_optional

tag = 'ParameterEstimator'

Methods Documentation

copy()

Copy estimator.

estimate_best_fit(datasets, parameter)

Estimate parameter asymmetric errors.

Parameters:

datasetsDatasets

Datasets.

parameterParameter

For which parameter to get the value.

Returns:

resultdict

Dictionary 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

Dictionary 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:

datasetsDatasets

Datasets.

parameterParameter

For which parameter to get the value.

Returns:

resultdict

Dictionary with the parameter asymmetric errors. Entries are:

• {parameter.name}_errp : positive error on parameter value.

• {parameter.name}_errn : negative error on parameter value.

static estimate_npred(datasets)

Estimate npred for the flux point.

Parameters:

datasetsDatasets

Datasets.

Returns:

resultdict

Dictionary with an array with one entry per dataset with the sum of the masked npred.

estimate_scan(datasets, parameter)

Estimate parameter statistic scan.

Parameters:

datasetsDatasets

The datasets used to estimate the model parameter.

parameterParameter

For which parameter to get the value.

Returns:

resultdict

Dictionary with the parameter fit scan values. Entries are:

• parameter.name_scan : parameter values scan.

• “stat_scan” : fit statistic values scan.

estimate_sensitivity(datasets, parameter)

Estimate norm sensitivity for the flux point.

Parameters:

datasetsDatasets

Datasets.

Returns:

resultdict

Dictionary with an array with one entry per dataset with the sum of the masked npred.

estimate_ts(datasets, parameter)

Estimate parameter ts.

Parameters:

datasetsDatasets

Datasets.

parameterParameter

For which parameter to get the value.

Returns:

resultdict

Dictionary with the test statistic 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.

• “stat_null” : total stat corresponding to the null hypothesis

estimate_ul(datasets, parameter)

Estimate parameter ul.

Parameters:

datasetsDatasets

The datasets used to estimate the model parameter.

parameterParameter

For which parameter to get the value.

Returns:

resultdict

Dictionary with the parameter upper limits. Entries are:

• parameter.name_ul : upper limit on parameter value.

run(datasets, parameter)

Run the parameter estimator.

Parameters:

datasetsDatasets

The datasets used to estimate the model parameter.

parameterstr or Parameter

For which parameter to run the estimator.

Returns:

resultdict

Dictionary with the various parameter estimation values.