AnalysisConfig#

class gammapy.analysis.AnalysisConfig[source]#

Bases: GammapyBaseConfig

Gammapy analysis configuration.

This class defines the full analysis configuration schema, organised into different sections. It can be read from or written to a YAML file using read() and write(), respectively.

Attributes:
generalGeneralConfig

General settings for output, logging, and file paths.

observationsObservationsConfig

Settings for the Observation selection including IDs, regions, and time filters.

datasetsDatasetsConfig

Settings for the Dataset Including but not limited to geometry (GeomConfig), background (BackgroundConfig), safe mask (SafeMaskConfig), and stacking.

fitFitConfig

Configuration for the Fit strategy and global fit energy range.

flux_pointsFluxPointsConfig

Configuration for the FluxPointsEstimator.

excess_mapExcessMapConfig

Configuration for the ExcessMapEstimator.

light_curveLightCurveConfig

Configuration for the LightCurveEstimator.

Examples

Read from a yaml file:

>>> from gammapy.analysis import AnalysisConfig
>>> config = AnalysisConfig.read("config.yaml")
>>> print(config.datasets.geom)

Create from scratch

>>> config = AnalysisConfig()
>>> config.observations.datastore = "$GAMMAPY_DATA/hess-dl3-dr1"
>>> config.observations.obs_cone = {"frame": "icrs", "lon": "83.633 deg", "lat": "22.014 deg", "radius": "5 deg"}
>>> print(config.observations.obs_cone.lat.deg)
22.014

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

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

model_fields_set

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])

!!! abstract "Usage Documentation"

model_dump(*[, mode, include, exclude, ...])

!!! abstract "Usage Documentation"

model_dump_json(*[, indent, include, ...])

!!! abstract "Usage Documentation"

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(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, ...])

!!! abstract "Usage Documentation"

model_validate_strings(obj, *[, strict, ...])

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()

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 = {}#
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 = {'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']))}#
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

classmethod construct(_fields_set=None, **values)#
Parameters:
Return type:

Self

copy(*, include=None, exclude=None, update=None, deep=False)#

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} 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.

Parameters:

  • include (AbstractSetIntStr | MappingIntStrAny | None)

  • exclude (AbstractSetIntStr | MappingIntStrAny | None)

  • update (Dict[str, Any] | None)

  • deep (bool)

Return type:

Self

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)#
Parameters:
Return type:

Dict[str, Any]

classmethod from_orm(obj)#
Parameters:

obj (Any)

Return type:

Self

classmethod from_yaml(config_str)[source]#

Create from YAML string.

Parameters:
config_strstr

yaml str

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)#
Parameters:
Return type:

str

classmethod model_construct(_fields_set=None, **values)#

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 the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_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 the values argument will be used.

values: Trusted or pre-validated data dictionary.

Returns:

A new instance of the Model class with validated data.

Parameters:
Return type:

Self

model_copy(*, update=None, deep=False)#
!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

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.

Parameters:
Return type:

Self

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)#
!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#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].

fallback: A function to call when an unknown value is encountered. If not provided,

a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.

Returns:

A dictionary representation of the model.

Parameters:
Return type:

dict[str, Any]

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)#
!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#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].

fallback: A function to call when an unknown value is encountered. If not provided,

a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.

Returns:

A JSON string representation of the model.

Parameters:
Return type:

str

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')#

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.

Parameters:

  • by_alias (bool)

  • ref_template (str)

  • schema_generator (type[GenerateJsonSchema])

  • mode (Literal['validation', 'serialization'])

Return type:

dict[str, Any]

classmethod model_parametrized_name(params)#

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.

Parameters:

params (tuple[type[Any], ...])

Return type:

str

model_post_init(context, /)#

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.

Parameters:

context (Any)

Return type:

None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=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.

Parameters:

  • force (bool)

  • raise_errors (bool)

  • _parent_namespace_depth (int)

  • _types_namespace (MappingNamespace | None)

Return type:

bool | None

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)#

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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError: If the object could not be validated.

Returns:

The validated model instance.

Parameters:

  • obj (Any)

  • strict (bool | None)

  • from_attributes (bool | None)

  • context (Any | None)

  • by_alias (bool | None)

  • by_name (bool | None)

Return type:

Self

classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)#
!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data.

Returns:

The validated Pydantic model.

Raises:

ValidationError: If json_data is not a JSON string or the object could not be validated.

Parameters:
Return type:

Self

classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)#

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. by_alias: Whether to use the field’s alias when validating against the provided input data. by_name: Whether to use the field’s name when validating against the provided input data.

Returns:

The validated Pydantic model.

Parameters:

  • obj (Any)

  • strict (bool | None)

  • context (Any | None)

  • by_alias (bool | None)

  • by_name (bool | None)

Return type:

Self

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)#
Parameters:

  • path (str | Path)

  • content_type (str | None)

  • encoding (str)

  • proto (DeprecatedParseProtocol | None)

  • allow_pickle (bool)

Return type:

Self

classmethod parse_obj(obj)#
Parameters:

obj (Any)

Return type:

Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)#
Parameters:

  • b (str | bytes)

  • content_type (str | None)

  • encoding (str)

  • proto (DeprecatedParseProtocol | None)

  • allow_pickle (bool)

Return type:

Self

classmethod read(path)[source]#

Read from YAML file.

Parameters:
pathstr

input filepath

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')#
Parameters:

  • by_alias (bool)

  • ref_template (str)

Return type:

Dict[str, Any]

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

  • by_alias (bool)

  • ref_template (str)

  • dumps_kwargs (Any)

Return type:

str

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:
configstr or AnalysisConfig object, optional

Configuration settings provided in dict() syntax. Default is None.

classmethod update_forward_refs(**localns)#
Parameters:

localns (Any)

Return type:

None

classmethod validate(value)#
Parameters:

value (Any)

Return type:

Self

write(path, overwrite=False)[source]#

Write to YAML file.

Parameters:
pathpathlib.Path or str

Path to write files.

overwritebool, optional

Overwrite existing file. Default is False.

__init__(**data)#

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.

Parameters:

data (Any)

Return type:

None

classmethod __new__(*args, **kwargs)#