bayesline.api.Settings

bayesline.api.Settings#

pydantic model bayesline.api.Settings#

Base class for all settings objects.

Show JSON schema
{
   "title": "Settings",
   "description": "Base class for all settings objects.",
   "type": "object",
   "properties": {},
   "additionalProperties": false
}

Config:

  • frozen: bool = True

  • extra: str = forbid

Validators:

  • _strip_unknown_on_load » all fields

abstract property menu_type: type[M]#

Get the menu type for this settings object.

Returns#

type[M]

The menu type for this settings object.

get_references() → Sequence[str | int]#

Get references for this settings object.

Returns#

Sequence[str | int]

A sequence of references (strings or integers) for this settings object.

map_references(fn: Callable[[str | int], str | int]) → Self#

Return a copy with every reference rewritten by fn.

The structural dual of get_references(): it applies fn to each reference this settings object holds — recursing into nested settings — and returns a rewritten copy, leaving all non-reference fields untouched. The base implementation has no references and returns self unchanged, mirroring get_references() (whose default is []); a subclass that overrides one must override the other in lockstep so the two stay consistent (a parity test enforces this).

fn is applied purely structurally — this object knows where its references live, not what a rewrite means. Any resolution context (e.g. a current user or a user directory) must be captured by the caller in the fn closure; the settings layer never learns of it.

Parameters#

fnCallable[[str | int], str | int]

Applied to each reference leaf. Must preserve a reference’s kind (a name stays a name, an id stays an id).

Returns#

Self

A copy with references rewritten.

validate_settings(menu: M | None) → None#

Validate the settings against the given menu.

Parameters#

menuM | None

The menu to validate against. If None, the settings are validated against without having the menu context. This means that less validation is done.

Raises#

AttributeError

If the menu type does not match the settings type.

describe(menu: M) → str#

Describe the settings.

Parameters#

menuM

The menu to use to describe the settings.

Returns#

str

A description of the settings.

with_dataset(dataset: str) → DatasetAwareSettings[Self]#

Bind this settings object to a dataset.

Parameters#

datasetstr

The dataset to scope the settings to.

Returns#

DatasetAwareSettings[Self]

A wrapper carrying both the dataset and this settings.