FluxMetaData#
- class gammapy.estimators.FluxMetaData(*, sed_type: ~gammapy.estimators.metadata.SEDTYPEEnum | None = None, sed_type_init: ~gammapy.estimators.metadata.SEDTYPEEnum | None = None, n_sigma: float | None = None, n_sigma_ul: float | None = None, sqrt_ts_threshold_ul: float | None = None, n_sigma_sensitivity: float | None = None, target: ~gammapy.utils.metadata.TargetMetaData | None = None, creation: ~gammapy.utils.metadata.CreatorMetaData | None = CreatorMetaData(creator='Gammapy 1.3.dev1373+ga65d9452c', date=<Time object: scale='utc' format='datetime' value=2024-11-07 12:44:36.105893>, origin=None), optional: dict | None = None)[source]#
Bases:
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
- target
TargetMetaData
, optional General metadata information about the target.
- creation
CreatorMetaData
, 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 allowself
as a field name.Attributes Summary
A dictionary of computed field names and their corresponding
ComputedFieldInfo
objects.Configuration for the model, should be a dictionary conforming to [
ConfigDict
][pydantic.config.ConfigDict].Get extra fields set during validation.
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo
][pydantic.fields.FieldInfo] objects.Returns the set of fields that have been explicitly set on this model instance.
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/2.9/concepts/serialization/#model_copy
model_dump
(*[, mode, include, exclude, ...])Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump
model_dump_json
(*[, indent, include, ...])Usage docs: https://docs.pydantic.dev/2.9/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__
andmodel_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/2.9/concepts/json/#json-parsing
model_validate_strings
(obj, *[, strict, context])Validate the given object with 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
ifconfig.extra
is not set to"allow"
.
- model_fields: ClassVar[Dict[str, FieldInfo]] = {'creation': FieldInfo(annotation=Union[CreatorMetaData, NoneType], required=False, default=CreatorMetaData(creator='Gammapy 1.3.dev1373+ga65d9452c', date=<Time object: scale='utc' format='datetime' value=2024-11-07 12:44:36.105893>, origin=None)), 'n_sigma': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'n_sigma_sensitivity': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'n_sigma_ul': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'optional': FieldInfo(annotation=Union[dict, NoneType], required=False, default=None), 'sed_type': FieldInfo(annotation=Union[SEDTYPEEnum, NoneType], required=False, default=None), 'sed_type_init': FieldInfo(annotation=Union[SEDTYPEEnum, NoneType], required=False, default=None), 'sqrt_ts_threshold_ul': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'target': FieldInfo(annotation=Union[TargetMetaData, NoneType], required=False, default=None)}#
Metadata about the fields defined on the model, mapping of field names to [
FieldInfo
][pydantic.fields.FieldInfo] objects.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
- copy(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, update: Dict[str, Any] | None = None, deep: bool = False) Self #
Returns a copy of the model.
- !!! warning “Deprecated”
This method is now deprecated; use
model_copy
instead.
If you need
include
orexclude
, 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 = None, exclude: IncEx | None = 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’.
- json(*, include: IncEx | None = None, exclude: IncEx | None = 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) Self #
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.- !!! note
model_construct()
generally respects themodel_config.extra
setting on the provided model. That is, ifmodel_config.extra == 'allow'
, then all extra passed values are added to the model instance’s__dict__
and__pydantic_extra__
fields. Ifmodel_config.extra == 'ignore'
(the default), then all extra passed values are ignored. Because no validation is performed with a call tomodel_construct()
, havingmodel_config.extra == 'forbid'
does not result in an error if extra values are passed, but they will be ignored.- Args:
- _fields_set: A set of field names that were originally explicitly set during instantiation. If provided,
this is directly used for the [
model_fields_set
][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from thevalues
argument will be used.
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) Self #
Usage docs: https://docs.pydantic.dev/2.9/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 = None, exclude: IncEx | None = None, context: Any | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, round_trip: bool = False, warnings: bool | Literal['none', 'warn', 'error'] = True, serialize_as_any: bool = False) dict[str, Any] #
Usage docs: https://docs.pydantic.dev/2.9/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 set of fields to include in the output. exclude: A set of fields to exclude from the output. context: Additional context to pass to the serializer. 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: How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors,“error” raises a [
PydanticSerializationError
][pydantic_core.PydanticSerializationError].serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.
- mode: The mode in which
- Returns:
A dictionary representation of the model.
- model_dump_json(*, indent: int | None = None, include: IncEx | None = None, exclude: IncEx | None = None, context: Any | None = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, round_trip: bool = False, warnings: bool | Literal['none', 'warn', 'error'] = True, serialize_as_any: bool = False) str #
Usage docs: https://docs.pydantic.dev/2.9/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. context: Additional context to pass to the serializer. 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: How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors,“error” raises a [
PydanticSerializationError
][pydantic_core.PydanticSerializationError].serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.
- 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 modificationsmode: 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 modelModel[str, int]
, the value(str, int)
would be passed toparams
.
- Returns:
String representing the new class where
params
are passed tocls
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__
andmodel_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 toTrue
. _parent_namespace_depth: The depth level of the parent namespace, defaults to 2. _types_namespace: The types namespace, defaults toNone
.- Returns:
Returns
None
if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returnsTrue
if rebuilding was successful, otherwiseFalse
.
- classmethod model_validate(obj: Any, *, strict: bool | None = None, from_attributes: bool | None = None, context: Any | None = None) Self #
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: Any | None = None) Self #
Usage docs: https://docs.pydantic.dev/2.9/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:
ValidationError: If
json_data
is not a JSON string or the object could not be validated.
- classmethod model_validate_strings(obj: Any, *, strict: bool | None = None, context: Any | None = None) Self #
Validate the given object with string data against the Pydantic model.
- Args:
obj: The object containing 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) Self #
- classmethod parse_raw(b: str | bytes, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) Self #
- 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.