python-type 1.1.0

A powerful and flexible Python decorator library for runtime type validation using type hints and custom type specifications.

TypedPython

A powerful and flexible Python decorator library for runtime type validation using type hints and custom type specifications.

Features

• Automatic Type Validation: Leverages Python type hints for seamless parameter validation
• Custom Type Overrides: Override specific parameter types without modifying function signatures
• Complex Type Support: Validates generic types like List[int], Dict[str, int], Union[int, str], etc.
• Flexible Modes: Choose between strict validation (all parameters) or selective validation
• Return Type Validation: Validate function return types with a separate decorator
• Detailed Error Messages: Clear, formatted error messages with file location and parameter details
• Class Method Support: Works with both functions and class methods (self and cls parameters)
• Multiple Type Support: Allow parameters to accept multiple types using tuples or Union types
• Custom Validators: Create reusable validators with specific type configurations

Installation

pip install python-type==1.0.0

Requirements

• Python 3.7+
• No external dependencies (uses only standard library)

Quick Start

Basic Usage with Type Hints

from python_type import validate_data

@validate_data()
def greet(name: str, age: int, active: bool = True) -> str:
    return f"Hello {name}, you are {age} years old"

# This will work
greet("John", 25)

# This will raise ValidationError
greet(123, "twenty")  # Wrong types

Custom Type Overrides

@validate_data(age=(int, str))  # Allow both int and str for age
def process_age(name: str, age: int) -> str:
    return f"{name} is {age} years old"

# Both of these work
process_age("Alice", 30)     # int
process_age("Bob", "25")     # str

Complex Type Validation

from typing import List, Dict, Union

@validate_data()
def process_data(
    users: List[str], 
    config: Dict[str, int], 
    status: Union[str, int] = "active"
) -> bool:
    return len(users) > 0

# Valid calls
process_data(["user1", "user2"], {"setting": 1})
process_data(["user1"], {"setting": 1}, "inactive")
process_data(["user1"], {"setting": 1}, 404)

Selective Validation (Non-Strict Mode)

@validate_data(strict=False, age=int)  # Only validate 'age' parameter
def flexible_function(name, age, other_param):
    return f"{name} is {age} years old"

# Only 'age' is validated, other parameters can be any type
flexible_function("John", 25, ["any", "type", "here"])

Return Type Validation

from python_type import validate_return_type

@validate_return_type
def calculate_sum(a: int, b: int) -> int:
    return a + b  # Must return an int

@validate_return_type
def get_user_data(user_id: int) -> Dict[str, str]:
    return {"name": "John", "email": "[e-mail]"}

Custom Validators

from python_type import create_validator

# Create a reusable validator
user_validator = create_validator({
    'email': str,
    'age': int,
    'active': bool
})

@user_validator
def process_user(email, age, active):
    return f"User {email} is {age} years old and {'active' if active else 'inactive'}"

Advanced Features

Supported Generic Types

• List[T] - Validates list elements
• Dict[K, V] - Validates dictionary keys and values
• Tuple[T1, T2, ...] - Validates tuple elements by position
• Set[T] - Validates set elements
• Union[T1, T2, ...] - Allows multiple types
• Custom generic types that follow typing conventions

Class Method Support

class UserManager:
    @validate_data()
    def create_user(self, name: str, age: int) -> str:
        return f"Created user {name}, age {age}"
    
    @validate_data()
    @classmethod
    def from_config(cls, config: Dict[str, str]) -> 'UserManager':
        return cls()

Error Messages

The library provides detailed, formatted error messages that include:

======================================================================
TYPE VALIDATION ERROR
======================================================================
📁 File: /path/to/your/file.py
📍 Line: 42
🔧 Function: my_function()
❌ Errors found: 1
======================================================================

💥 ERROR 1:
   Parameter: 'age' (position 2)
   ✅ Expected: int (from type hint)
   ❌ Received: str
   📦 Value: str('twenty')
======================================================================

Override Parameter Validation

The decorator validates that all override parameters exist in the function:

# This will raise a configuration error
@validate_data(non_existent_param=int)
def my_function(name: str, age: int):
    pass

Exception Handling

The library raises ValidationError (a subclass of ValueError) when validation fails:

from python_type import ValidationError

try:
    my_function("wrong", "types")
except ValidationError as e:
    print(e)  # Detailed error message with emojis and formatting

API Reference

validate_data(*, strict: bool = True, **types_override)

Main decorator for parameter validation.

Parameters:

• strict (bool): If True, validates all parameters with type hints. If False, only validates parameters with overrides.
• **types_override: Keyword arguments to override specific parameter types.

Returns:

• Configured decorator function

validate_return_type(func)

Decorator to validate function return types based on type hints.

Parameters:

• func: Function to decorate

Returns:

• Wrapped function with return type validation

create_validator(custom_types: Dict[str, Any])

Creates a custom validator with specific types.

Parameters:

• custom_types: Dictionary mapping parameter names to expected types

Returns:

• Configured decorator function

ValidationError

Custom exception class for type validation errors. Inherits from ValueError.

Use Cases

• API Development: Validate request parameters automatically
• Data Processing: Ensure data types are correct before processing
• Function Contracts: Enforce type contracts in critical functions
• Debugging: Catch type-related bugs early in development
• Testing: Validate that functions receive expected types

Why TypedPython?

• Zero Runtime Overhead: Only validates when decorators are applied
• Non-Intrusive: Works with existing code without modification
• Flexible: Choose between automatic type hint validation or manual specification
• Production Ready: Comprehensive error handling and edge case coverage
• Developer Friendly: Clear error messages with emojis help debug issues quickly
• Comprehensive: Supports complex generic types, class methods, and custom validators

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License.