jesterTOV.inference.config.schema.SpectralEOSConfig

class SpectralEOSConfig(**data)

Bases: BaseEOSConfig

Configuration for Spectral Decomposition EOS.

Variables:

• type (Literal["spectral"]) – EOS type identifier

• n_points_high (int) – Number of high-density points for spectral EOS (default: 500)

• nb_CSE (int) – Must be 0 for spectral (no CSE support)

• reparametrized (bool) – If False (default), sample directly in \((\gamma_0, \gamma_1, \gamma_2, \gamma_3)\). If True, sample in a whitened space \((\tilde{\gamma}_0, \tilde{\gamma}_1, \tilde{\gamma}_2, \tilde{\gamma}_3)\) centred on a Gaussian fit to a radio-timing inference result. The bijection \(\boldsymbol{\gamma} = \boldsymbol{\mu} + L_\text{wide}\,\tilde{\boldsymbol{\gamma}}\) maps the unit-normal tilde parameters back to physical spectral coefficients, where \(L_\text{wide} = \sigma_\text{scale}\,L\) and \(\boldsymbol{\mu}\) is the posterior mean. Use a MultivariateGaussianPrior with default (unit) parameters in the prior file when this option is enabled.

• sigma_scale (float) – Multiplicative factor applied to the base Cholesky factor \(L\) to form \(L_\text{wide} = \sigma_\text{scale}\,L\). Only used when reparametrized=True. Default 1.0 (exact radio posterior covariance). Increase to widen the prior around the radio posterior.

__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_nb_cse(v)	Validate that nb_CSE is 0 for spectral.

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.
type
n_points_high
nb_CSE
reparametrized
sigma_scale
crust_name

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

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

n_points_high: int

nb_CSE: int

reparametrized: bool

sigma_scale: float

type: Literal['spectral']

classmethod validate_nb_cse(v)

Validate that nb_CSE is 0 for spectral.

Return type:

int