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 has zero 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 and complexity of Pydantic.
4. Customizable: Statica allows fine-grained control over type and constraint validation through customizable fields 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.
• Field Aliasing: Support for field aliases for parsing and serialization.

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"

Or, define a BaseClass which configures the error classes globally:

from statica.core import Statica, ConstraintValidationError, TypeValidationError

class BaseClass(Statica):
    constraint_error_class = ConstraintValidationError
    type_error_class = TypeValidationError

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

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

Aliasing

Statica supports field aliasing, allowing you to map different field names for parsing and serialization. This is particularly useful when working with external APIs that use different naming conventions.

Basic Aliases

Use the alias parameter to define an alternative name for both parsing and serialization:

class User(Statica):
    full_name: str = Field(alias="fullName")
    age: int = Field(alias="userAge")

# Parse data with aliases
data = {"fullName": "John Doe", "userAge": 30}
user = User.from_map(data)
print(user.full_name)  # Output: "John Doe"
print(user.age)        # Output: 30

# Serialize back with aliases
result = user.to_dict()
print(result)  # Output: {"fullName": "John Doe", "userAge": 30}

Separate Parsing and Serialization Aliases

You can define different aliases for parsing and serialization:

class APIModel(Statica):
    user_name: str = Field(
        alias_for_parsing="userName",
        alias_for_serialization="username"
    )
    user_id: int = Field(alias_for_parsing="userId")

# Parse from camelCase API response
api_data = {"userName": "alice", "userId": 123}
model = APIModel.from_map(api_data)

# Serialize to snake_case for internal use
internal_data = model.to_dict()
print(internal_data)  # Output: {"username": "alice", "user_id": 123}

Alias Priority

When multiple alias types are defined, the priority is:

1. alias_for_parsing for parsing operations
2. alias_for_serialization for serialization operations
3. alias as a fallback for both operations

class PriorityExample(Statica):
    field_name: str = Field(
        alias="generalAlias",
        alias_for_parsing="parseAlias",
        alias_for_serialization="serializeAlias"
    )

# Uses alias_for_parsing
instance = PriorityExample.from_map({"parseAlias": "value"})

# Uses alias_for_serialization
result = instance.to_dict()
print(result)  # Output: {"serializeAlias": "value"}

Advanced Usage

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.