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
.. 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.
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.
.. 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,
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"description": "Overview\n=============\n\n\nThis 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.\n\nInstallation\n=============\n\n\nYou can add it to your project using the following command:\n\n`pip install t-object`\n\nAfter installing, you can import it into your project using the command\n\n`from t_object import ThoughtfulObject`.\n\nMake sure to check the documentation for detailed usage instructions and examples.\n\n\nKey features you need to know\n=============================\n\n\n1. All features of the original Pydantic library are available.\n---------------------------------------------------------------\n\n- Creating models\n\n.. code-block:: python\n\n class Driver(ThoughtfulObject):\n name: str\n age: int\n driving_experience: timedelta\n last_driving_date: datetime\n\n class Car(ThoughtfulObject):\n model: str\n car_release_date: date\n price: float\n driver: list[Driver]\n\n\nand\n\n.. code-block:: python\n\n class Patient(ThoughtfulObject):\n name: str\n age: int\n birth_date: datetime\n\n- creating instanse of model\n\n.. code-block:: python\n\n car = Car(\n model=\"Tesla Model S\",\n car_release_date=date(2012, 6, 22),\n price=79999.99,\n driver=[\n Driver(\n name=\"Elon Musk\",\n age=49,\n driving_experience=timedelta(days=365 * 30),\n last_driving_date=datetime(2021, 1, 1)\n ),\n Driver(\n name=\"Jeff Bezos\",\n age=57,\n driving_experience=timedelta(days=365 * 20),\n last_driving_date=datetime(2021, 1, 1)\n )]\n )\n\nand\n\n.. code-block:: python\n\n patient = Patient(\n name=\"John Doe\",\n age=42,\n birth_date=datetime(1979, 1, 1)\n )\n\n2. Configuration\n-----------------\n\n- Default configuration of the T-Object. Simply import it using `from t_object import ThoughtfulObject`. Default configuration listed below.\n\n.. code-block:: python\n\n validate_assignment=True,\n extra=Extra.forbid,\n frozen=False,\n populate_by_name=False,\n arbitrary_types_allowed=True,\n allow_inf_nan=True,\n strict=True,\n revalidate_instances=RevalidateInstances.always,\n validate_default=False,\n coerce_numbers_to_str=True,\n validation_error_cause=True,\n str_strip_whitespace=True,\n\n\n- 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\n\n.. code-block:: python\n\n ResponseObject = build_custom_t_object(\n extra=Extra.allow,\n frozen=True,\n allow_inf_nan=True,\n strict=False,\n )\n class UserResponse(ResponseObject):\n name: str\n age: int\n dob: datetime\n\n3. Exporting the model to JSON format\n--------------------------------------\n\n\n- 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.\n\n\n4. Importing JSON into the Model\n---------------------------------\n\n\n- 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.\n\n.. code-block:: python\n\n patient = Patient.load_from_json_file(\"patient.json\")\n\n5. Pretty String\n-----------------\n\n\nIt 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,\n",
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