ObservationMetaData

class gammapy.data.ObservationMetaData(*, obs_info: Optional[gammapy.utils.metadata.ObsInfoMetaData] = None, pointing: Optional[gammapy.utils.metadata.PointingInfoMetaData] = None, target: Optional[gammapy.utils.metadata.TargetMetaData] = None, location: Optional[Union[astropy.coordinates.earth.EarthLocation, str]] = None, deadtime_fraction: gammapy.data.metadata.ConstrainedFloatValue = 0.0, time_start: Optional[Union[str, astropy.time.core.Time]] = None, time_stop: Optional[Union[str, astropy.time.core.Time]] = None, reference_time: Optional[Union[str, astropy.time.core.Time]] = None, creation: Optional[gammapy.utils.metadata.CreatorMetaData] = None, optional: Optional[dict] = None, **extra_data: Any)

Bases: gammapy.utils.metadata.MetaData

Metadata containing information about the Observation.

Parameters

obs_infoObsInfoMetaData

The general observation information.

pointing`~gammapy.utils.PointingInfoMetaData

The pointing metadata.

target`~gammapy.utils.TargetMetaData

The target metadata.

creationCreatorMetaData

The creation metadata.

locationEarthLocation or str, optional

The observatory location.

deadtime_fractionfloat

The observation deadtime fraction. Default is 0.

time_startTime or str

The observation start time.

time_stopTime or str

The observation stop time.

reference_timeTime or str

The observation reference time.

optionaldict, optional

Additional optional metadata.

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_header(header[, format])	Import MetaData from a FITS header.
from_orm(obj)
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, ...])
schema([by_alias, ref_template])
schema_json(*[, by_alias, ref_template])
to_header([format])	Export MetaData to a FITS header.
to_yaml()	Dump metadata content to yaml.
update_forward_refs(**localns)	Try to update ForwardRefs on fields based on this Model, globalns and localns.
validate(value)
validate_location(v)	Validate the location value.
validate_time(v)	Validate the time value.

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: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[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: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[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_header(header, format='gadf')

Import MetaData from a FITS header.

Conversion is performed following the definition in the METADATA_FITS_EXPORT_KEYS.

Parameters

headerdict

The header dictionary.

format{‘gadf’}, optional

Header format. Default is ‘gadf’.

classmethod from_orm(obj: Any) → Model

json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[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 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

to_header(format='gadf')

Export MetaData to a FITS header.

Conversion is performed following the definition in the METADATA_FITS_EXPORT_KEYS.

Parameters

format{‘gadf’}, optional

Header format. Default is ‘gadf’.

Returns

headerdict

The header dictionary.

to_yaml()

Dump metadata content to yaml.

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

classmethod validate_location(v)

Validate the location value.

classmethod validate_time(v)

Validate the time value.