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('/home/runner/work/gammapy-docs/gammapy-docs/gammapy-datasets/dev/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=['aeff', 'edisp', 'psf', 'bkg']), datasets: ~gammapy.analysis.config.DatasetsConfig = DatasetsConfig(type='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=['counts', 'exposure', 'background', 'psf', 'edisp'], background=BackgroundConfig(method=None, exclusion=None, parameters={}), safe_mask=SafeMaskConfig(methods=['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:
GammapyBaseConfig
Gammapy analysis configuration.
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.
Methods Summary
construct
([_fields_set])copy
(*[, include, exclude, update, deep])Returns a copy of the model.
dict
(*[, include, exclude, by_alias, ...])from_orm
(obj)from_yaml
(config_str)Create from YAML string.
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, ...])read
(path)Read from YAML file.
schema
([by_alias, ref_template])schema_json
(*[, by_alias, ref_template])Set logging config.
to_yaml
()Convert to YAML string.
update
([config])Update config with provided settings.
update_forward_refs
(**localns)validate
(value)write
(path[, overwrite])Write to YAML file.
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]] = {'datasets': FieldInfo(annotation=DatasetsConfig, required=False, default=DatasetsConfig(type='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=['counts', 'exposure', 'background', 'psf', 'edisp'], background=BackgroundConfig(method=None, exclusion=None, parameters={}), safe_mask=SafeMaskConfig(methods=['aeff-default'], parameters={}), on_region=SpatialCircleConfig(frame=None, lon=None, lat=None, radius=None), containment_correction=True)), 'excess_map': FieldInfo(annotation=ExcessMapConfig, required=False, default=ExcessMapConfig(correlation_radius=<Angle 0.1 deg>, parameters={}, energy_edges=EnergyAxisConfig(min=None, max=None, nbins=None))), 'fit': FieldInfo(annotation=FitConfig, required=False, default=FitConfig(fit_range=EnergyRangeConfig(min=None, max=None))), 'flux_points': FieldInfo(annotation=FluxPointsConfig, required=False, default=FluxPointsConfig(energy=EnergyAxisConfig(min=None, max=None, nbins=None), source='source', parameters={'selection_optional': 'all'})), 'general': FieldInfo(annotation=GeneralConfig, required=False, default=GeneralConfig(log=LogConfig(level='info', filename=None, filemode=None, format=None, datefmt=None), outdir='.', n_jobs=1, datasets_file=None, models_file=None)), 'light_curve': FieldInfo(annotation=LightCurveConfig, required=False, default=LightCurveConfig(time_intervals=TimeRangeConfig(start=None, stop=None), energy_edges=EnergyAxisConfig(min=None, max=None, nbins=None), source='source', parameters={'selection_optional': 'all'})), 'observations': FieldInfo(annotation=ObservationsConfig, required=False, default=ObservationsConfig(datastore=PosixPath('/home/runner/work/gammapy-docs/gammapy-docs/gammapy-datasets/dev/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=['aeff', 'edisp', 'psf', 'bkg']))}#
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.
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_yaml(config_str)[source]#
Create from YAML string.
- Parameters:
- config_strstr
yaml str
- 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 #
- set_logging()[source]#
Set logging config.
Calls
logging.basicConfig
, i.e. adjusts global logging state.
- update(config=None)[source]#
Update config with provided settings.
- Parameters:
- configstr or
AnalysisConfig
object, optional Configuration settings provided in dict() syntax. Default is None.
- configstr or
- write(path, overwrite=False)[source]#
Write to YAML file.
- Parameters:
- path
pathlib.Path
or str Path to write files.
- overwritebool, optional
Overwrite existing file. Default is False.
- path