Skip to main content

Library for creating partial pydantic models (automatic converters) from different mappings

Project description

Pydantic Marshals

Library for creating partial pydantic models (automatic converters) from different mappings. Currently, it consists of basic boilerplate parts and functional implementation for sqlalchemy 2.0+ (included via extra)

Base Interface

TBA

Implementations

TBA

SQLAlchemy: Basic usage

# sqlalchemy 2.0+ is required
from sqlalchemy import ForeignKey, String, Text
from sqlalchemy.orm import Mapped, mapped_column, relationship

from pydantic_marshals.sqlalchemy import MappedModel

class Avatar(Base):
    __tablename__ = "avatars"
    id: Mapped[int] = mapped_column(primary_key=True)
    IdModel = MappedModel.create(columns=[id])

class User(Base):
    __tablename__ = "users"
    id: Mapped[int] = mapped_column(primary_key=True)
    name: Mapped[str] = mapped_column(String(100))
    description: Mapped[str | None] = mapped_column(Text())
    admin: Mapped[bool] = mapped_column()  # empty `mapped_column()` is required for models

    avatar_id: Mapped[int] = mapped_column(ForeignKey("avatars.id"))
    avatar: Mapped[Avatar] = relationship()

    @property
    def representation(self) -> str:
        return f"User #{self.id}: {self.name}"

    BaseModel = MappedModel.create(columns=[id])
    CreateModel = MappedModel.create(columns=[name, description])
    PatchModel = CreateModel.as_patch()
    IndexModel = MappedModel.create(properties=[representation])
    FullModel = BaseModel.extend(
        columns=[admin],
        relationships=[(avatar, Avatar.IdModel)],
        includes=[CreateModel, IndexModel],
    )


with sessionmaker.begin() as session:
    user = User(name="alex", description="cool person", avatar=Avatar(), admin=False)
    session.add(user)
    session.flush()

    print(User.BaseModel.model_validate(user).model_dump())
    # {"id": 0}
    print(User.PatchModel.model_validate({}).model_dump(exclude_defaults=True))
    # {}
    print(User.PatchModel.model_validate({"description": None}).model_dump(exclude_defaults=True))
    # {"description": None}
    print(User.CreateModel.model_validate(user).model_dump())
    # {"name": "alex", "description": "cool person"}
    print(User.IndexModel.model_validate(user).model_dump())
    # {"representation": "User #0: alex"}
    print(User.FullModel.model_validate(user).model_dump())
    # {
    #   "id": 0,
    #   "name": "alex",
    #   "description": "cool person",
    #   "representation": "User #0: alex",
    #   "avatar": {"id": 0},
    #   "admin": False
    # }

Assert Contains

The "assert contains" is an interface for validating data, mainly used in testing. Use "assert-contains" extra to install this module:

pip install pydantic-marshals[assert-contains]

Documentation:

Local development

  1. Clone the repository
  2. Setup python (the library is made with python 3.10+)
  3. Install poetry (should work with v1.4.1)
  4. Install dependencies
  5. Install pre-commit hooks

Commands to use:

pip install poetry==1.4.1
poetry install
pre-commit install

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pydantic_marshals-0.3.11.tar.gz (11.7 kB view details)

Uploaded Source

Built Distribution

pydantic_marshals-0.3.11-py3-none-any.whl (20.7 kB view details)

Uploaded Python 3

File details

Details for the file pydantic_marshals-0.3.11.tar.gz.

File metadata

  • Download URL: pydantic_marshals-0.3.11.tar.gz
  • Upload date:
  • Size: 11.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.4.1 CPython/3.11.1 Windows/10

File hashes

Hashes for pydantic_marshals-0.3.11.tar.gz
Algorithm Hash digest
SHA256 5d2b1ae7b2f201625cf23a8c3421ffa7805030ba54d9a5378fc66a1dc629f465
MD5 6328addc6bd63b4c79660f3409a9f545
BLAKE2b-256 cf1402775a2c46b6c7f666c4088d721e19444565820d25b14b33c033df24356b

See more details on using hashes here.

Provenance

File details

Details for the file pydantic_marshals-0.3.11-py3-none-any.whl.

File metadata

File hashes

Hashes for pydantic_marshals-0.3.11-py3-none-any.whl
Algorithm Hash digest
SHA256 2454ae6c0f61f7a6f2d54f70f77a32247aef42f527e5d59184fcbc757e964342
MD5 dd4c986097188593a7597123029dcfb7
BLAKE2b-256 56315c3395a89ea1dc46a932a7286439401bd269d1ccf1c5a1a901d5628fbb9e

See more details on using hashes here.

Provenance

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page