FrozenBaseModel

FrozenBaseModel#

pydantic model tollan.config.FrozenBaseModel[source]#

Base model class with immutability and YAML support.

Provides common configuration for astronomy data models: - Frozen by default - Strict type validation - YAML loading/dumping support - Custom JSON schema generation

Examples

>>> class MyConfig(FrozenBaseModel):
...     name: str
...     value: int
>>> config = MyConfig(name="test", value=42)
>>> yaml_str = config.model_dump_yaml()
>>> loaded = MyConfig.model_validate_yaml(yaml_str)

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.

Show JSON schema
{
   "title": "FrozenBaseModel",
   "description": "Base model class with immutability and YAML support.\n\nProvides common configuration for astronomy data models:\n- Frozen by default\n- Strict type validation\n- YAML loading/dumping support\n- Custom JSON schema generation\n\nExamples\n--------\n>>> class MyConfig(FrozenBaseModel):\n...     name: str\n...     value: int\n>>> config = MyConfig(name=\"test\", value=42)\n>>> yaml_str = config.model_dump_yaml()\n>>> loaded = MyConfig.model_validate_yaml(yaml_str)",
   "type": "object",
   "properties": {}
}

Config:

  • frozen: bool = True

  • validate_default: bool = True

  • ignored_types: tuple = (<class ‘functools.cached_property’>,)

  • strict: bool = True

classmethod model_json_schema(*args: Any, **kwargs: Any) → dict[str, Any][source]#

Generate JSON schema using custom schema generator.

This uses our custom GenerateJsonSchema which knows how to serialize astronomy types (Time, Quantity) in default values.

Parameters:

  • *args – Arguments passed to BaseModel.model_json_schema()

  • **kwargs – Keyword arguments passed to BaseModel.model_json_schema()

Returns:

JSON schema for the model

Return type:

dict

classmethod model_validate_yaml(yaml_source: str | Path, **kwargs: Any) → Self[source]#

Validate model from YAML.

Parameters:

  • yaml_source (str | Path) – YAML source to load

  • **kwargs – Arguments passed to model_validate()

Returns:

Validated model instance

Return type:

Self

static yaml_dump(data: object, output: str | os.PathLike | TextIOBase | None = None, **kwargs: object) → None | str#

Serialize data as YAML and write to a file, stream, or return as string.

Parameters:

  • data (object) – The data to serialize.

  • output (str or os.PathLike or TextIOBase or None, optional) – Output destination: - None: return YAML as string - str or PathLike: write to file - TextIOBase: write to stream

  • **kwargs – Additional keyword arguments passed to yaml.dump.

Returns:

YAML string if output is None, otherwise None.

Return type:

str or None

Raises:

TypeError – If output is not a valid type.

static yaml_load(source: str | os.PathLike | TextIOBase) → object#

Load YAML data from a file, stream, or string.

Parameters:

source (str or os.PathLike or TextIOBase) – The YAML source. Can be: - File path: read from file - File object: read from stream - String: parse as YAML

Returns:

Parsed YAML data.

Return type:

object

Raises:

TypeError – If source is not a valid type.

model_dump_yaml(**kwargs: Any) → str[source]#

Dump model as YAML.

Parameters:

**kwargs – Arguments passed to model_dump()

Returns:

YAML representation of model

Return type:

str

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