jesterTOV.inference.config.schema.InferenceConfig

jesterTOV.inference.config.schema.InferenceConfig#

class InferenceConfig(**data)[source]#

Bases: JesterBaseModel

Top-level inference configuration.

Variables:

  • seed (int) – Random seed for reproducibility

  • eos (EOSConfig) – EOS configuration (discriminated union by type)

  • tov (TOVConfig) – TOV solver configuration (discriminated union by type)

  • prior (PriorConfig) – Prior configuration

  • likelihoods (list[LikelihoodConfig]) – List of likelihood configurations

  • sampler (SamplerConfig) – Sampler configuration

  • postprocessing (PostprocessingConfig) – Postprocessing configuration

  • data_paths (dict[str, str]) – Override default data paths

  • dry_run (bool) – Setup everything but don’t run sampler (default: False)

  • validate_only (bool) – Only validate configuration, don’t run inference (default: False)

  • debug_nans (bool) – Enable JAX NaN debugging for catching numerical issues (default: 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.

Methods

__init__(**data)

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

construct([_fields_set])

copy(*[, include, exclude, update, deep])

Returns a copy of the model.

dict(*[, include, exclude, by_alias, ...])

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])

!!! abstract "Usage Documentation"

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

!!! abstract "Usage Documentation"

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

!!! 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, extra, ...])

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, ...])

schema([by_alias, ref_template])

schema_json(*[, by_alias, ref_template])

update_forward_refs(**localns)

validate(value)

validate_likelihoods(v)

Validate that at least one likelihood is enabled.

validate_seed(v)

Validate that seed is non-negative.

Attributes

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.

seed

eos

tov

prior

likelihoods

sampler

postprocessing

data_paths

dry_run

validate_only

debug_nans

data_paths: dict[str, str]#
debug_nans: bool#
dry_run: bool#
eos: Annotated[MetamodelEOSConfig | MetamodelCSEEOSConfig | SpectralEOSConfig, Discriminator(discriminator=type, custom_error_type=None, custom_error_message=None, custom_error_context=None)]#
likelihoods: list[Annotated[GWLikelihoodConfig | GWResampledLikelihoodConfig | NICERLikelihoodConfig | NICERKDELikelihoodConfig | RadioLikelihoodConfig | ChiEFTLikelihoodConfig | EOSConstraintsLikelihoodConfig | TOVConstraintsLikelihoodConfig | GammaConstraintsLikelihoodConfig | DeprecatedConstraintsLikelihoodConfig | REXLikelihoodConfig | ZeroLikelihoodConfig, Discriminator(discriminator=type, custom_error_type=None, custom_error_message=None, custom_error_context=None)]]#
model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}#

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

postprocessing: PostprocessingConfig#
prior: PriorConfig#
sampler: Annotated[FlowMCSamplerConfig | BlackJAXNSAWConfig | SMCRandomWalkSamplerConfig | SMCNUTSSamplerConfig, Discriminator(discriminator=type, custom_error_type=None, custom_error_message=None, custom_error_context=None)]#
seed: int#
tov: Annotated[GRTOVConfig | AnisotropyTOVConfig, Discriminator(discriminator=type, custom_error_type=None, custom_error_message=None, custom_error_context=None)]#
classmethod validate_likelihoods(v)[source]#

Validate that at least one likelihood is enabled.

Return type:

list[Union[GWLikelihoodConfig, GWResampledLikelihoodConfig, NICERLikelihoodConfig, NICERKDELikelihoodConfig, RadioLikelihoodConfig, ChiEFTLikelihoodConfig, EOSConstraintsLikelihoodConfig, TOVConstraintsLikelihoodConfig, GammaConstraintsLikelihoodConfig, DeprecatedConstraintsLikelihoodConfig, REXLikelihoodConfig, ZeroLikelihoodConfig]]

validate_only: bool#
classmethod validate_seed(v)[source]#

Validate that seed is non-negative.

Return type:

int

Contents