t-object 0.1.8

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

1. All features of the original Pydantic library are available.

• Creating models

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

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

• creating instanse of model

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

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.

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

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.

4. 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.

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,