AnalysisConfig#

class gammapy.analysis.AnalysisConfig(*, general: gammapy.analysis.config.GeneralConfig = GeneralConfig(log=LogConfig(level='info', filename=None, filemode=None, format=None, datefmt=None), outdir='.', n_jobs=1, datasets_file=None, models_file=None), observations: gammapy.analysis.config.ObservationsConfig = ObservationsConfig(datastore=PosixPath('$GAMMAPY_DATA/hess-dl3-dr1'), obs_ids=[], obs_file=None, obs_cone=SpatialCircleConfig(frame=None, lon=None, lat=None, radius=None), obs_time=TimeRangeConfig(start=None, stop=None), required_irf=[<RequiredHDUEnum.aeff: 'aeff'>, <RequiredHDUEnum.edisp: 'edisp'>, <RequiredHDUEnum.psf: 'psf'>, <RequiredHDUEnum.bkg: 'bkg'>]), datasets: gammapy.analysis.config.DatasetsConfig = DatasetsConfig(type=<ReductionTypeEnum.spectrum: '1d'>, stack=True, geom=GeomConfig(wcs=WcsConfig(skydir=SkyCoordConfig(frame=None, lon=None, lat=None), binsize=<Angle 0.02 deg>, width=WidthConfig(width=<Angle 5. deg>, height=<Angle 5. deg>), binsize_irf=<Angle 0.2 deg>), selection=SelectionConfig(offset_max=<Angle 2.5 deg>), axes=EnergyAxesConfig(energy=EnergyAxisConfig(min=<Quantity 1. TeV>, max=<Quantity 10. TeV>, nbins=5), energy_true=EnergyAxisConfig(min=<Quantity 0.5 TeV>, max=<Quantity 20. TeV>, nbins=16))), map_selection=[<MapSelectionEnum.counts: 'counts'>, <MapSelectionEnum.exposure: 'exposure'>, <MapSelectionEnum.background: 'background'>, <MapSelectionEnum.psf: 'psf'>, <MapSelectionEnum.edisp: 'edisp'>], background=BackgroundConfig(method=None, exclusion=None, parameters={}), safe_mask=SafeMaskConfig(methods=[<SafeMaskMethodsEnum.aeff_default: 'aeff-default'>], parameters={}), on_region=SpatialCircleConfig(frame=None, lon=None, lat=None, radius=None), containment_correction=True), fit: gammapy.analysis.config.FitConfig = FitConfig(fit_range=EnergyRangeConfig(min=None, max=None)), flux_points: gammapy.analysis.config.FluxPointsConfig = FluxPointsConfig(energy=EnergyAxisConfig(min=None, max=None, nbins=None), source='source', parameters={'selection_optional': 'all'}), excess_map: gammapy.analysis.config.ExcessMapConfig = ExcessMapConfig(correlation_radius=<Angle 0.1 deg>, parameters={}, energy_edges=EnergyAxisConfig(min=None, max=None, nbins=None)), light_curve: gammapy.analysis.config.LightCurveConfig = LightCurveConfig(time_intervals=TimeRangeConfig(start=None, stop=None), energy_edges=EnergyAxisConfig(min=None, max=None, nbins=None), source='source', parameters={'selection_optional': 'all'}))[source]#

Bases: gammapy.analysis.config.GammapyBaseConfig

Gammapy analysis configuration.

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

Methods Summary

`construct`([_fields_set])	Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
`copy`(*[, include, exclude, update, deep])	Duplicate a model, optionally choose which fields to include, exclude and change.
`dict`(*[, include, exclude, by_alias, ...])	Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
`from_orm`(obj)
`from_yaml`(config_str)	Create from YAML string.
`json`(*[, include, exclude, by_alias, ...])	Generate a JSON representation of the model, `include` and `exclude` arguments as per `dict()`.
`parse_file`(path, *[, content_type, ...])
`parse_obj`(obj)
`parse_raw`(b, *[, content_type, encoding, ...])
`read`(path)	Reads from YAML file.
`schema`([by_alias, ref_template])
`schema_json`(*[, by_alias, ref_template])
`set_logging`()	Set logging config.
`to_yaml`()	Convert to YAML string.
`update`([config])	Update config with provided settings.
`update_forward_refs`(**localns)	Try to update ForwardRefs on fields based on this Model, globalns and localns.
`validate`(value)
`write`(path[, overwrite])	Write to YAML file.

Methods Documentation

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model#: Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = 'allow' was set since it adds all passed values

copy(*, include: Union[AbstractSetIntStr, MappingIntStrAny] = None, exclude: Union[AbstractSetIntStr, MappingIntStrAny] = None, update: DictStrAny = None, deep: bool = False) → Model#

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters

include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data
deep – set to True to make a deep copy of the model

Returns

new model instance

dict(*, include: Union[AbstractSetIntStr, MappingIntStrAny] = None, exclude: Union[AbstractSetIntStr, MappingIntStrAny] = None, by_alias: bool = False, skip_defaults: bool = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny#: Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

classmethod from_orm(obj: Any) → Model#

classmethod from_yaml(config_str)[source]#: Create from YAML string.

json(*, include: Union[AbstractSetIntStr, MappingIntStrAny] = None, exclude: Union[AbstractSetIntStr, MappingIntStrAny] = None, by_alias: bool = False, skip_defaults: bool = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode#

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

classmethod parse_file(path: Union[str, pathlib.Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: pydantic.parse.Protocol = None, allow_pickle: bool = False) → Model#

classmethod parse_obj(obj: Any) → Model#

classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: pydantic.parse.Protocol = None, allow_pickle: bool = False) → Model#

classmethod read(path)[source]#: Reads from YAML file.

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny#

classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode#

set_logging()[source]#

Set logging config.

Calls logging.basicConfig, i.e. adjusts global logging state.

to_yaml()[source]#: Convert to YAML string.

update(config=None)[source]#

Update config with provided settings.

Parameters

configstring dict or AnalysisConfig object: Configuration settings provided in dict() syntax.

classmethod update_forward_refs(**localns: Any) → None#: Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) → Model#

write(path, overwrite=False)[source]#: Write to YAML file.