tsdat.config.utils

Classes

Overrideable	Abstract base class for generic types.
ParameterizedConfigClass
YamlModel

Functions

get_code_version
read_yaml
recursive_instantiate	Instantiates all ParametrizedClass components and subcomponents of a given model.

Function Descriptions

exception tsdat.config.utils.ConfigError

Bases: Exception

Common base class for all non-exit exceptions.

Initialize self. See help(type(self)) for accurate signature.

class tsdat.config.utils.Overrideable

Bases: YamlModel, pydantic.generics.GenericModel, Generic[Config]

Abstract base class for generic types.

A generic type is typically declared by inheriting from this class parameterized with one or more type variables. For example, a generic mapping type might be defined as:

class Mapping(Generic[KT, VT]):
    def __getitem__(self, key: KT) -> VT:
        ...
    # Etc.

This class can then be used as follows:

def lookup_name(mapping: Mapping[KT, VT], key: KT, default: VT) -> VT:
    try:
        return mapping[key]
    except KeyError:
        return default

overrides: Dict[str, Any]

path: pydantic.FilePath

class tsdat.config.utils.ParameterizedConfigClass

Bases: pydantic.BaseModel

classname: pydantic.StrictStr

parameters: Dict[str, Any]

Class Methods

classname_looks_like_a_module
instantiate	Instantiates and returns the class specified by the 'classname' parameter.

Method Descriptions

classmethod classname_looks_like_a_module(v: pydantic.StrictStr) → pydantic.StrictStr

instantiate() → Any

Instantiates and returns the class specified by the ‘classname’ parameter.

Returns:

Any – An instance of the specified class.

class tsdat.config.utils.YamlModel

Bases: pydantic.BaseModel

Class Methods

from_yaml	Creates a python configuration object from a yaml file.
generate_schema	Generates JSON schema from the model fields and type annotations.

Method Descriptions

classmethod from_yaml(filepath: pathlib.Path, overrides: Dict[str, Any] | None = None)

Creates a python configuration object from a yaml file.

Parameters:

• filepath (Path) – The path to the yaml file

• overrides (Optional[Dict[str, Any]], optional) – Overrides to apply to the yaml before instantiating the YamlModel object. Defaults to None.

Returns:

YamlModel – A YamlModel subclass

classmethod generate_schema(output_file: pathlib.Path)

Generates JSON schema from the model fields and type annotations.

Parameters:

output_file (Path) – The path to store the JSON schema.

tsdat.config.utils.get_code_version() → str

tsdat.config.utils.read_yaml(filepath: pathlib.Path) → Dict[Any, Any]

tsdat.config.utils.recursive_instantiate(model: Any) → Any

Instantiates all ParametrizedClass components and subcomponents of a given model.

Recursively calls model.instantiate() on all ParameterizedConfigClass instances under the the model, resulting in a new model which follows the same general structure as the given model, but possibly containing totally different properties and methods.

Note that this method does a depth-first traversal of the model tree to to instantiate leaf nodes first. Traversing breadth-first would result in new pydantic models attempting to call the __init__ method of child models, which is not valid because the child models are ParameterizedConfigClass instances. Traversing depth-first allows us to first transform child models into the appropriate type using the classname of the ParameterizedConfigClass.

This method is primarily used to instantiate a Pipeline subclass and all of its properties from a yaml pipeline config file, but it can be applied to any other pydantic model.

Parameters:

model (Any) – The object to recursively instantiate.

Returns:

Any – The recursively-instantiated object.