schemantic 0.1.1

Manipulate model schemas utilizing homologous, grouped, or cultured paradigms

Schemantic

Create schemas from models or classes with homologous, grouped, or cultured paradigms.

Best with pydantic.BaseModel instances, but works with any Python class and dataclass/pydantic.dataclass!

Classes of schemas

Homolog

from pydantic import BaseModel
from ordered_set import OrderedSet
from schemantic.schema import HomologSchema

class Thief(BaseModel):
    stolen_goods: int
    steals_only: str

my_homolog = HomologSchema.from_model(Thief, instance_names=OrderedSet(["copycat", "pink_panther"]))

Grouped

You can manage multiple schemas as a group:

from pydantic import BaseModel
from schemantic.schema import GroupSchema

class Baker(BaseModel):
    baked_goods: int
    citizen_of: str

class Cop(BaseModel):
    years_of_service: int
    citizen_of: str

group_schema = GroupSchema.from_originating_types([Baker, Cop], )

Culture

You can also manage multiple types of schemas under one culture:

from ordered_set import OrderedSet
from schemantic.schema import CultureSchema

CultureSchema(source_schemas=OrderedSet([homolog_schema, group_schema]))

Methods

HomologSchema, GroupSchema, and CultureSchema have the following methods.

.schema()

Creates a dictionary, which represents the schema of the origin class/model.

my_homolog.schema()

Output:

"class_name": "Thief"
"common": {
    steals_only: "jewelry"
}
"copycat": {}
"pink_panther": {}
"required": ["stolen_goods", "steals_only"]
"field_to_info": {"stolen_goods": "integer"}

.dump()

Dump the dictionary from .schema() to a yaml or toml file.

my_schema.dump("my/path/schema.yaml")
# There is also toml support
my_schema.dump("my/path/schema.toml")

.parse()

"class_name": "Thief"
"common": {
    steals_only: "jewelry"
}
"copycat": {
    stolen_goods: 10
}
"pink_panther": {
    stolen_goods: 14
}
"required": ["stolen_goods", "steals_only"]
"field_to_info": {"stolen_goods": "integer"}

parsed = my_homolog.parse_schema("my/path/schema.yaml")

# parsed["copycat"].stolen_goods == 10
# parsed["pink_panther"].stolen_goods == 14

# Both -> steals_only == "jewelry"

Class configuration

Use schemantic.project module to control schemantic processing from the origin class/model side.

Classes and dataclasses

from dataclasses import dataclass
from typing import Optional, Union

from schemantic.project import SchemanticProjectMixin


@dataclass
class TestDataclass(SchemanticProjectMixin):
    must_be: int
    we: str = "n"

    n: None = None
    age: Optional[int] = None
    new_age: Union[int, str, None] = None

    exclude_me: Optional[int] = None
    _exclude_me_too: Optional[float] = None

    @classmethod
    @property
    def fields_to_exclude_from_single_schema(cls) -> set[str]:
        upstream = super().fields_to_exclude_from_single_schema
        upstream.update(("exclude_me",))
        return upstream

This will exclude exclude_me (defined in fields_to_exclude_from_single_schema) and _exclude_me_too (private).

Same for pydantic.BaseModel:

from typing import Optional, Union

from pydantic import BaseModel, computed_field

from schemantic.project import SchemanticProjectModelMixin


class TestModel(SchemanticProjectModelMixin, BaseModel):
    must_be: int
    we: str = "n"

    n: None = None
    age: Optional[int] = None
    new_age: Union[int, str, None] = None

    exclude_me: Optional[int] = None
    _exclude_me_too: Optional[float] = None

    @classmethod  # type: ignore[misc]
    @computed_field(return_type=set[str])
    @property
    def fields_to_exclude_from_single_schema(cls) -> set[str]:
        upstream = super().fields_to_exclude_from_single_schema
        upstream.update(("exclude_me",))
        return upstream

Install

pip install schemantic

For toml or yaml dumping and parsing

pip install schemantic[toml]
pip install schemantic[yaml]