taph 0.1.2

Taph creates objects that are guaranteed to be unchangeable, enabling predictable, pure functional programming patterns in Python.

Taph: Zero-Overhead Immutability for Python

Taph is a minimalist Python package for enforcing deep, zero-overhead immutability. Taph creates objects that are guaranteed to be unchangeable, enabling predictable, pure functional programming patterns in Python.

Taph's core value proposition: Fast & Reliable Immutability.

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Key Features

• Zero-Overhead: Achieves immutability using Python's Method Resolution Order (MRO) and metaclass injection.
• Memory Efficiency: Enforces __slots__ usage, eliminating the memory footprint of __dict__ for every instance.
• Deep Immutability: Recursively transforms nested mutable structures (like list, dict) into immutable counterparts (tuple, MappingProxyType) during class creation.
• Zero Dependencies: A single-file core module built using the Python Standard Library.

Installation

pip install taph

Usage

Taph provides two classes of immutable objects:

• Immutable for instantiable data objects
• Namespace for static constants.

1. The Immutable Base Class

Use Immutable for creating value objects (data structures) whose state must never change after initialization.

Contract: Subclasses must define __slots__.

from taph import Immutable, ImmutableError

class Point(Immutable):
    __slots__ = ('x', 'y')

    def __init__(self, x: int, y: int):
        # IMPORTANT: Use super().__setattr__ for initialization!
        super().__setattr__('x', x)
        super().__setattr__('y', y)

p = Point(10, 20)

# Fails (ImmutableError)
try:
    p.x = 30
except ImmutableError as e:
    print(f"Success: {e}")

# Fails (ImmutableError)
try:
    del p.y
except ImmutableError as e:
    print(f"Success: {e}")

2. The Namespace Static Container

Use Namespace for static configuration, constants, or utility groups. Namespace classes are non-instantiable and their class attributes are ** frozen** at creation time.

from taph import Namespace, ImmutableError

class AppConfig(Namespace):
    __slots__ = () # Required, must be empty
    VERSION = "1.0.0"
    HOSTS = ["server-a", "server-b"] # Deeply frozen into a tuple

# Access attributes directly
print(f"Version: {AppConfig.VERSION}")
print(f"Hosts Type: {type(AppConfig.HOSTS)}") # <class 'tuple'>

# Fails (ImmutableError) - Cannot modify class attributes
try:
    AppConfig.TIMEOUT = 60
except ImmutableError as e:
    print(f"Success: {e}")

# Fails (ImmutableError) - Cannot instantiate
try:
    _ = AppConfig()
except ImmutableError as e:
    print(f"Success: {e}")

Deep Freezing

Taph's freeze utility ensures deep immutability by recursively converting mutable collections during class construction:

Mutable Type	Taph Equivalent
list	tuple
set	frozenset
dict	types.MappingProxyType

Custom objects must inherit from Immutable or Namespace, or freeze will raise an ImmutableError at class definition time.

Functional Style

Taph provides a solid foundation for functional programming in Python:

1. Pure Functions:

Pass Taph Immutable objects into functions with confidence that no side effects can occur.

from taph import Immutable

class User(Immutable):
    __slots__ = ('user_id', 'name', 'is_active')

    def __init__(self, user_id: int, name: str, is_active: bool = True):
        super().__setattr__('user_id', user_id)
        super().__setattr__('name', name)
        super().__setattr__('is_active', is_active)

# PURE FUNCTION: No side effects. Takes a User, returns a NEW User.
def deactivate_user(user: User) -> User:
    # This is the "copy-on-write" pattern.
    return User(
        user_id=user.user_id,
        name=user.name,
        is_active=False
    )

# --- Caller ---
user1 = User(101, 'Alice')
user2 = deactivate_user(user1)

# The original object is completely untouched. The system is predictable.
print(user1.is_active)  # -> True
print(user2.is_active)  # -> False
assert user1 is not user2

2. Stateless Systems:

Use Namespace to provide verifiably safe, global constants that cannot be accidentally mutated by any function.

from taph import Namespace

class Config(Namespace):
    __slots__ = ()
    TIMEOUT_SECONDS = 30
    SUPPORTED_METHODS = ('GET', 'POST')

def is_request_valid(request_duration: int, method: str) -> bool:
    # This function is predictable. Its behavior depends on its inputs and
    # constants that are guaranteed to be immutable.
    if method not in Config.SUPPORTED_METHODS:
        return False
    return request_duration < Config.TIMEOUT_SECONDS

# Config.TIMEOUT_SECONDS = 1 # Raises ImmutableError, protecting the function.

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License

Taph is licensed under the Apache License 2.0. See the LICENSE file for details.