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 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.
• Default Field Values: Set default values for fields that are used when not explicitly provided.
• 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

Default Field Values

You can specify default values for fields in two ways:

Using Field() with default parameter

class User(Statica):
    name: str
    age: int = Field(default=25)
    active: bool = Field(default=True)

# Using direct initialization
user = User(name="John")
print(user.name)    # Output: "John"
print(user.age)     # Output: 25
print(user.active)  # Output: True

# Using from_map
user = User.from_map({"name": "Jane"})
print(user.age)     # Output: 25 (default used)

# Explicit values override defaults
user = User(name="Bob", age=30, active=False)
print(user.age)     # Output: 30

Direct assignment (without Field)

You can also assign default values directly to fields without using Field():

class Config(Statica):
    name: str
    timeout: float = 30.0      # Direct assignment
    retries: int = 3           # Direct assignment
    debug: bool = False        # Direct assignment

config = Config(name="server")
print(config.timeout)  # Output: 30.0
print(config.retries)   # Output: 3
print(config.debug)     # Output: False

# Works with from_map too
config = Config.from_map({"name": "api-server"})
print(config.timeout)  # Output: 30.0 (default used)

Both approaches work identically and can be mixed within the same class. Use Field(default=...) when you need additional constraints or options, and direct assignment for simple defaults.

Validation and Safety

Default values are validated against any constraints you've defined:

class Config(Statica):
    timeout: float = Field(default=30.0, min_value=1.0, max_value=120.0)
    retries: int = Field(default=3, min_value=1)

config = Config()  # Uses defaults: timeout=30.0, retries=3

For mutable default values (like lists, dicts, sets), Statica automatically creates copies to prevent shared state issues:

class UserProfile(Statica):
    name: str
    tags: list[str] = Field(default=[])  # or tags: list[str] = []

user1 = UserProfile(name="Alice")
user2 = UserProfile(name="Bob")

user1.tags.append("admin")
print(user1.tags)  # Output: ["admin"]
print(user2.tags)  # Output: [] (not affected)

Field Constraints

You can specify constraints and options on fields:

• Default Values: default (using Field()) or direct assignment
• String Constraints: min_length, max_length, strip_whitespace
• Numeric Constraints: min_value, max_value
• Casting: cast_to
• Aliasing: alias

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)

class DefaultTest(Statica):
    # Using Field() for defaults with constraints
    status: str = Field(default="active")
    priority: int = Field(default=1, min_value=1, max_value=5)

    # Direct assignment for simple defaults
    timeout: float = 30.0
    retries: int = 3

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.

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", default=25)

# Parse data with aliases
data = {"fullName": "John Doe"}  # userAge not provided, uses default
user = User.from_map(data)
print(user.full_name)  # Output: "John Doe"
print(user.age)        # Output: 25

# Serialize back with aliases (uses the alias for serialization by default)
result = user.to_dict()
print(result)  # Output: {"fullName": "John Doe", "userAge": 25}

# Serialize without aliases
result_no_alias = user.to_dict(with_aliases=False)
print(result_no_alias)  # Output: {"full_name": "John Doe", "age": 25}

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

Design Decisions

• Inheritance over Decorators

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.