FluxMetaData#

class gammapy.estimators.FluxMetaData(*, sed_type: typing.Optional[gammapy.estimators.metadata.SEDTYPEEnum] = None, sed_type_init: typing.Optional[gammapy.estimators.metadata.SEDTYPEEnum] = None, n_sigma: typing.Optional[float] = None, n_sigma_ul: typing.Optional[float] = None, sqrt_ts_threshold_ul: typing.Optional[float] = None, n_sigma_sensitivity: typing.Optional[float] = None, target: typing.Optional[gammapy.utils.metadata.TargetMetaData] = None, creation: typing.Optional[gammapy.utils.metadata.CreatorMetaData] = CreatorMetaData(creator='Gammapy 1.2', date=<Time object: scale='utc' format='datetime' value=2024-02-29 10:21:41.167622>, origin=None), optional: typing.Optional[dict] = None)[source]#

Bases: gammapy.utils.metadata.MetaData

Metadata containing information about the FluxPoints and FluxMaps.

Attributes

sed_type{“dnde”, “flux”, “eflux”, “e2dnde”, “likelihood”}, optional: SED type.
sed_type_init{“dnde”, “flux”, “eflux”, “e2dnde”, “likelihood”}, optional: SED type of the initial data.
n_sigmafloat, optional: Significance threshold above which upper limits should be used.
n_sigma_ulfloat, optional: Significance value used for the upper limit computation.
sqrt_ts_threshold_ulfloat, optional: Threshold on the square root of the likelihood value above which upper limits should be used.
n_sigma_sensitivityfloat, optional: Sigma number for which the flux sensitivity is computed
targetTargetMetaData, optional: General metadata information about the target.
creationCreatorMetaData, optional: The creation metadata.
optionaldict, optional: additional optional metadata.
Notethese quantities are serialized in FITS header with the keywords stored in the dictionary FLUX_METADATA_FITS_KEYS

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

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Attributes Summary

`model_computed_fields`	A dictionary of computed field names and their corresponding `ComputedFieldInfo` objects.
`model_config`	Configuration for the model, should be a dictionary conforming to [`ConfigDict`][pydantic.config.ConfigDict].
`model_extra`	Get extra fields set during validation.
`model_fields`	Metadata about the fields defined on the model, mapping of field names to [`FieldInfo`][pydantic.fields.FieldInfo].
`model_fields_set`	Returns the set of fields that have been explicitly set on this model instance.
`tag`	Returns MetaData tag.

Methods Summary

`construct`([_fields_set])
`copy`(*[, include, exclude, update, deep])	Returns a copy of the model.
`dict`(*[, include, exclude, by_alias, ...])
`from_header`(header[, format])	Import MetaData from a FITS header.
`from_orm`(obj)
`json`(*[, include, exclude, by_alias, ...])
`model_construct`([_fields_set])	Creates a new instance of the `Model` class with validated data.
`model_copy`(*[, update, deep])	Usage docs: https://docs.pydantic.dev/2.6/concepts/serialization/#model_copy
`model_dump`(*[, mode, include, exclude, ...])	Usage docs: https://docs.pydantic.dev/2.6/concepts/serialization/#modelmodel_dump
`model_dump_json`(*[, indent, include, ...])	Usage docs: https://docs.pydantic.dev/2.6/concepts/serialization/#modelmodel_dump_json
`model_json_schema`([by_alias, ref_template, ...])	Generates a JSON schema for a model class.
`model_parametrized_name`(params)	Compute the class name for parametrizations of generic classes.
`model_post_init`(_BaseModel__context)	Override this method to perform additional initialization after `__init__` and `model_construct`.
`model_rebuild`(*[, force, raise_errors, ...])	Try to rebuild the pydantic-core schema for the model.
`model_validate`(obj, *[, strict, ...])	Validate a pydantic model instance.
`model_validate_json`(json_data, *[, strict, ...])	Usage docs: https://docs.pydantic.dev/2.6/concepts/json/#json-parsing
`model_validate_strings`(obj, *[, strict, context])	Validate the given object contains string data against the Pydantic model.
`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)
`validate`(value)

Attributes Documentation

model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}#: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'use_enum_values': True, 'validate_assignment': True, 'validate_default': True}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_extra#

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[dict[str, FieldInfo]] = {'creation': FieldInfo(annotation=Union[CreatorMetaData, NoneType], required=False, default=CreatorMetaData(creator='Gammapy 1.2', date=<Time object: scale='utc' format='datetime' value=2024-02-29 10:21:41.167622>, origin=None)), 'n_sigma': FieldInfo(annotation=Union[float, NoneType], required=False), 'n_sigma_sensitivity': FieldInfo(annotation=Union[float, NoneType], required=False), 'n_sigma_ul': FieldInfo(annotation=Union[float, NoneType], required=False), 'optional': FieldInfo(annotation=Union[dict, NoneType], required=False), 'sed_type': FieldInfo(annotation=Union[SEDTYPEEnum, NoneType], required=False), 'sed_type_init': FieldInfo(annotation=Union[SEDTYPEEnum, NoneType], required=False), 'sqrt_ts_threshold_ul': FieldInfo(annotation=Union[float, NoneType], required=False), 'target': FieldInfo(annotation=Union[TargetMetaData, NoneType], required=False)}#

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

model_fields_set#

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

tag#: Returns MetaData tag.

Methods Documentation

classmethod construct(_fields_set: set[str] | None = None, **values: Any) → Model#

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Args:: include: Optional set or mapping specifying which fields to include in the copied model. exclude: Optional set or mapping specifying which fields to exclude in the copied model. update: Optional dictionary of field-value pairs to override field values in the copied model. deep: If True, the values of fields that are Pydantic models will be deep-copied.
Returns:: A copy of the model with included, excluded and updated fields as specified.

dict(*, include: IncEx = None, exclude: IncEx = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → Dict[str, Any]#

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’}: Header format. Default is ‘gadf’.

classmethod from_orm(obj: Any) → Model#

json(*, include: IncEx = None, exclude: IncEx = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Callable[[Any], Any] | None = PydanticUndefined, models_as_dict: bool = PydanticUndefined, **dumps_kwargs: Any) → str#

classmethod model_construct(_fields_set: set[str] | None = None, **values: Any) → Model#

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_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

Args:: _fields_set: The set of field names accepted for the Model instance. values: Trusted or pre-validated data dictionary.
Returns:: A new instance of the Model class with validated data.

model_copy(*, update: dict[str, Any] | None = None, deep: bool = False) → Model#

Usage docs: https://docs.pydantic.dev/2.6/concepts/serialization/#model_copy

Returns a copy of the model.

Args:

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.

model_dump(*, mode: Literal['json', 'python'] | str = 'python', include: IncEx = None, exclude: IncEx = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, round_trip: bool = False, warnings: bool = True) → dict[str, Any]#

Usage docs: https://docs.pydantic.dev/2.6/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Args:

mode: The mode in which to_python should run.: If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

include: A list of fields to include in the output. exclude: A list of fields to exclude from the output. by_alias: Whether to use the field’s alias in the dictionary key if defined. exclude_unset: Whether to exclude fields that have not been explicitly set. exclude_defaults: Whether to exclude fields that are set to their default value. exclude_none: Whether to exclude fields that have a value of None. round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T]. warnings: Whether to log warnings when invalid fields are encountered.

Returns:

A dictionary representation of the model.

model_dump_json(*, indent: int | None = None, include: IncEx = None, exclude: IncEx = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, round_trip: bool = False, warnings: bool = True) → str#

Usage docs: https://docs.pydantic.dev/2.6/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Args:: indent: Indentation to use in the JSON output. If None is passed, the output will be compact. include: Field(s) to include in the JSON output. exclude: Field(s) to exclude from the JSON output. by_alias: Whether to serialize using field aliases. exclude_unset: Whether to exclude fields that have not been explicitly set. exclude_defaults: Whether to exclude fields that are set to their default value. exclude_none: Whether to exclude fields that have a value of None. round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T]. warnings: Whether to log warnings when invalid fields are encountered.
Returns:: A JSON string representation of the model.

classmethod model_json_schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}', schema_generator: type[pydantic.json_schema.GenerateJsonSchema] = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: typing_extensions.Literal[validation, serialization] = 'validation') → dict[str, Any]#

Generates a JSON schema for a model class.

Args:

by_alias: Whether to use attribute aliases or not. ref_template: The reference template. schema_generator: To override the logic used to generate the JSON schema, as a subclass of

GenerateJsonSchema with your desired modifications

mode: The mode in which to generate the schema.

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params: tuple[type[Any], ...]) → str#

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Args:

params: Tuple of types of the class. Given a generic class: Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError: Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context: Any) → None#: Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

classmethod model_rebuild(*, force: bool = False, raise_errors: bool = True, _parent_namespace_depth: int = 2, _types_namespace: dict[str, Any] | None = None) → bool | None#

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Args:: force: Whether to force the rebuilding of the model schema, defaults to False. raise_errors: Whether to raise errors, defaults to True. _parent_namespace_depth: The depth level of the parent namespace, defaults to 2. _types_namespace: The types namespace, defaults to None.
Returns:: Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj: Any, *, strict: bool | None = None, from_attributes: bool | None = None, context: dict[str, Any] | None = None) → Model#

Validate a pydantic model instance.

Args:: obj: The object to validate. strict: Whether to enforce types strictly. from_attributes: Whether to extract data from object attributes. context: Additional context to pass to the validator.
Raises:: ValidationError: If the object could not be validated.
Returns:: The validated model instance.

classmethod model_validate_json(json_data: str | bytes | bytearray, *, strict: bool | None = None, context: dict[str, Any] | None = None) → Model#

Usage docs: https://docs.pydantic.dev/2.6/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Args:: json_data: The JSON data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator.
Returns:: The validated Pydantic model.
Raises:: ValueError: If json_data is not a JSON string.

classmethod model_validate_strings(obj: Any, *, strict: bool | None = None, context: dict[str, Any] | None = None) → Model#

Validate the given object contains string data against the Pydantic model.

Args:: obj: The object contains string data to validate. strict: Whether to enforce types strictly. context: Extra variables to pass to the validator.
Returns:: The validated Pydantic model.

classmethod parse_file(path: str | Path, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) → Model#

classmethod parse_obj(obj: Any) → Model#

classmethod parse_raw(b: str | bytes, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) → Model#

classmethod schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}') → Dict[str, Any]#

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

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’}: Header format. Default is ‘gadf’.

Returns

headerdict: The header dictionary.

to_yaml()#: Dump metadata content to yaml.

classmethod update_forward_refs(**localns: Any) → None#

classmethod validate(value: Any) → Model#