t-object

Overview

This library streamlines the process of creating data classes and offers a versatile configuration model. It also enables the dumping and restoration of data. This library is built on Pydantic.

Installation

You can add it to your project using the following command:

pip install t-object

After installing, you can import it into your project using the command

from t_object import ThoughtfulObject.

Make sure to check the documentation for detailed usage instructions and examples.

Key features you need to know

• All features of the original Pydantic library are available.

• Creating models

.. code-block:: python

class Driver(ThoughtfulObject):
    name: str
    age: int
    driving_experience: timedelta
    last_driving_date: datetime

class Car(ThoughtfulObject):
    model: str
    car_release_date: date
    price: float
    driver: list[Driver]

and

.. code-block:: python

class Patient(ThoughtfulObject):
    name: str
    age: int
    birth_date: datetime

• creating instanse of model

.. code-block:: python

car = Car(
    model="Tesla Model S",
    car_release_date=date(2012, 6, 22),
    price=79999.99,
    driver=[
            Driver(
                name="Elon Musk",
                age=49,
                driving_experience=timedelta(days=365 * 30),
                last_driving_date=datetime(2021, 1, 1)
    ),
        Driver(
              name="Jeff Bezos",
              age=57,
              driving_experience=timedelta(days=365 * 20),
              last_driving_date=datetime(2021, 1, 1)
     )]
)

and

.. code-block:: python

patient = Patient(
    name="John Doe",
    age=42,
    birth_date=datetime(1979, 1, 1)
)

2. Configuration

• Default configuration of the T-Object. Simply import it using from t_object import ThoughtfulObject. Default configuration listed below.

.. code-block:: python

validate_assignment=True,
extra=Extra.forbid,
frozen=False,
populate_by_name=False,
arbitrary_types_allowed=True,
allow_inf_nan=True,
strict=True,
revalidate_instances=RevalidateInstances.always,
validate_default=False,
coerce_numbers_to_str=True,
validation_error_cause=True,
str_strip_whitespace=True,

• For custom configuration, use the build_custom_t_object function. You can find all configuration flags at https://docs.pydantic.dev/2.6/api/config/. Here is how to use it

.. code-block:: python

ResponseObject = build_custom_t_object(
      extra=Extra.allow,
      frozen=True,
      allow_inf_nan=True,
      strict=False,
)
class UserResponse(ResponseObject):
    name: str
    age: int
    dob: datetime

3. Exporting the model to JSON format

• To export data, use the save_to_json_file() method. You can either define the file path manually or leave it blank for automatic naming.

• Importing JSON into the Model

• To import data from a JSON file, use the load_from_json_file(file_path: str) class method. This method validates the data against your model automatically. The file_path attribute is required, which is the path to the JSON file.

.. code-block:: python

patient = Patient.load_from_json_file("patient.json")

5. Pretty String

It is possible to print any instance in a more readable and attractive format. This formatting can be achieved by employing the pretty_string() method. This method allows for the effortless transformation of the raw data into a format that is easier on the eyes,