asgardpy.data.dl4 module

Main classes to define High-level Analysis Config and the Analysis Steps.

class asgardpy.data.dl4.FitAnalysisStep(config, log=None, overwrite=True)

Bases: AnalysisStepBase

Using the Fitting parameters as defined in the Config, with the given datasets perform the fit of the models to the updated list of datasets.

tag = 'fit'

pydantic model asgardpy.data.dl4.FitConfig

Bases: BaseConfig

Config section for parameters to use for Fit function.

Fields:

• backend (asgardpy.data.dl4.BackendEnum)

• confidence_opts (dict)

• covariance_opts (dict)

• fit_range (asgardpy.base.base.EnergyRangeConfig)

• optimize_opts (dict)

• store_trace (bool)

field backend: BackendEnum = BackendEnum.minuit

field confidence_opts: dict = {}

field covariance_opts: dict = {}

field fit_range: EnergyRangeConfig = EnergyRangeConfig(min=<Quantity 1. GeV>, max=<Quantity 1. TeV>)

field optimize_opts: dict = {}

field store_trace: bool = True

class asgardpy.data.dl4.FluxPointsAnalysisStep(config, log=None, overwrite=True)

Bases: AnalysisStepBase

Using the Flux Points Estimator parameters in the config, and the given datasets and instrument_spectral_info perform the Flux Points Estimation and store the result in a list of flux points for each dataset.

tag = 'flux-points'

pydantic model asgardpy.data.dl4.FluxPointsConfig

Bases: BaseConfig

Config section for parameters to use for FluxPointsEstimator function.

Fields:

• parameters (dict)

• reoptimize (bool)

field parameters: dict = {'selection_optional': 'all'}

field reoptimize: bool = False