statica 1.2.0

A minimalistic data validation library

Statica

Statica is a Python library for defining and validating structured data with type annotations and constraints. It provides an easy-to-use framework for creating type-safe models with comprehensive validation for both types and constraints.

Why Statica?

Statica was created to address the need for a lightweight, flexible, and dependency-free alternative to libraries like pydantic. While pydantic is a powerful tool for data validation and parsing, Statica offers some distinct advantages in specific situations:

1. Lightweight: Statica does not rely on any third-party dependencies, making it ideal for projects where minimizing external dependencies is a priority.
2. Performance: For use cases where performance is critical. Pydantic needs 3x more memory than Statica for the same models.
3. Ease of Use: With its simple, Pythonic design, Statica is intuitive for developers already familiar with Python's dataclasses and type hinting. It avoids much of the magic that pydantic employs.
4. Customizable Validation: Statica allows fine-grained control over type and constraint validation through customizable field descriptors (Field) and error classes.

Features

• Type Validation: Automatically validates types for attributes based on type hints.
• Constraint Validation: Define constraints like minimum/maximum length, value ranges, and more.
• Customizable Error Handling: Use custom exception classes for type and constraint errors.
• Flexible Field Descriptors: Add constraints, casting, and other behaviors to your fields.
• Optional Fields: Support for optional fields with default values.
• Automatic Initialization: Automatically generate constructors (__init__) for your models.
• String Manipulation: Strip whitespace from string fields if needed.
• Casting: Automatically cast values to the desired type.

Installation

You can install Statica via pip:

pip install statica

Getting Started

Basic Usage

Define a model with type annotations and constraints:

from statica.core import Statica, Field

class Payload(Statica):
    name: str = Field(min_length=3, max_length=50, strip_whitespace=True)
    description: str | None = Field(max_length=200)
    num: int | float
    float_num: float | None

Instantiate the model using a dictionary:

data = {
    "name": "Test Payload",
    "description": "A short description.",
    "num": 42,
    "float_num": 3.14,
}

payload = Payload.from_map(data)
print(payload.name)  # Output: "Test Payload"

Or instantiate directly:

payload = Payload(
    name="Test",
    description="This is a test description.",
    num=42,
    float_num=3.14,
)

Validation

Statica automatically validates attributes based on type annotations and constraints:

from statica.core import ConstraintValidationError, TypeValidationError

try:
    payload = Payload(name="Te", description="Valid", num=42)
except ConstraintValidationError as e:
    print(e)  # Output: "name: length must be at least 3"

try:
    payload = Payload(name="Test", description="Valid", num="Invalid")
except TypeValidationError as e:
    print(e)  # Output: "num: expected type 'int | float', got 'str'"

Optional Fields

Fields annotated with | None are optional and default to None:

class OptionalPayload(Statica):
    name: str | None

payload = OptionalPayload()
print(payload.name)  # Output: None

Field Constraints

You can specify constraints on fields:

• String Constraints: min_length, max_length, strip_whitespace
• Numeric Constraints: min_value, max_value
• Casting: cast_to

class StringTest(Statica):
    name: str = Field(min_length=3, max_length=5, strip_whitespace=True)

class IntTest(Statica):
    num: int = Field(min_value=1, max_value=10, cast_to=int)

Custom Error Classes

You can define custom error classes for type and constraint validation:

class CustomError(Exception):
    pass

class CustomPayload(Statica):
    constraint_error_class = CustomError

    num: int = Field(min_value=1, max_value=10)

try:
    payload = CustomPayload(num=0)
except CustomError as e:
    print(e)  # Output: "num: must be at least 1"

Advanced Usage

Short Syntax for Fields

You can use simple type annotations without explicitly defining Field descriptors:

class ShortSyntax(Statica):
    name: str

short = ShortSyntax(name="Test")
print(short.name)  # Output: "Test"

Custom Initialization

Statica automatically generates an __init__ method based on type annotations, ensuring that all required fields are provided during initialization.

Casting

You can automatically cast input values to the desired type:

class CastingExample(Statica):
    num: int = Field(cast_to=int)

instance = CastingExample(num="42")
print(instance.num)  # Output: 42

Contributing

We welcome contributions to Statica! To contribute:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Write tests for your changes.
4. Submit a pull request.

License

Statica is licensed under the MIT License. See the LICENSE file for more details.

Acknowledgments

Statica was built to simplify data validation and provide a robust and simple framework for type-safe models in Python, inspired by pydantic and dataclasses.