extension.decorator

Decorators for registering axisfuzzy extension functions.

Provides the extension and batch_extension decorators which register callables into the ExtensionRegistry. Registered callables are later injected into target classes (e.g. Fuzznum, Fuzzarray) or the top-level axisfuzzy namespace by the injector at library initialization.

Examples

Simple specialized extension registration:

from axisfuzzy.extension import extension
from axisfuzzy.core import Fuzznum

@extension('distance', mtype='qrofn', target_classes=['Fuzznum'])
def qrofn_distance(f1: Fuzznum, f2: Fuzznum, p: int = 2) -> float:
    # qrofn-specific implementation
    ...

axisfuzzy.extension.decorator.batch_extension(registrations)

Decorator to register a single function with multiple extension configurations.

This is useful when a single function needs to serve different roles or be registered under various conditions (e.g., as a specialized implementation for one mtype and a default for another, or with different injection types).

Parameters:

registrations (list of dict) – Each dict supplies keyword arguments for the extension decorator, e.g. {'name': 'normalize', 'mtype': 'qrofn', 'target_classes': ['Fuzznum']}.

Returns:

A decorator which registers the decorated function multiple times.

Return type:

callable

Examples

Registering a function that serves as a specialized ‘normalize’ for ‘qrofn’ and a default ‘normalize’ for other types:

# In axisfuzzy/fuzzy/qrofs/_func.py
from axisfuzzy.extension import batch_extension
from axisfuzzy.core import Fuzznum

@batch_extension([
    {'name': 'normalize', 'mtype': 'qrofn', 'target_classes': ['Fuzznum']},
    {'name': 'normalize', 'is_default': True, 'target_classes': ['Fuzznum']}
])
def qrofn_normalize(fuzz: Fuzznum) -> Fuzznum:
    # QROFN specific normalization logic
    # ...
    return fuzz

axisfuzzy.extension.decorator.extension(name, mtype=None, target_classes=None, injection_type='both', is_default=False, priority=0, **kwargs)

Register an extension function in the ExtensionRegistry.

This decorator simplifies the process of adding new functionalities to FuzzLab’s Fuzznum and Fuzzarray objects, or as top-level functions. It acts as a wrapper around ExtensionRegistry.register(), providing a declarative way to define extension properties.

Parameters:

• name (str) – Extension name (e.g. distance).

• mtype (str or None, optional) – Target fuzzy-number type (e.g. qrofn). If None the registration is considered a general/default implementation.

• target_classes (str or list of str or None, optional) – Class name or list of class names to inject into (e.g. Fuzznum or ["Fuzznum", "Fuzzarray"]). If None, library conventions are used.

• injection_type ({instance_method, top_level_function, instance_property, both}, optional) – Injection mode. Default is both.

• is_default (bool, optional) – Whether this registration is a fallback when no mtype-specific implementation exists. Default is False.

• priority (int, optional) – Resolution priority when multiple candidates match. Higher values take precedence. Default is 0.

• **kwargs – Additional metadata forwarded to the registry.

Returns:

A decorator that accepts the implementation function and registers it.

Return type:

callable

Examples

Specialized distance for qrofn:

from axisfuzzy.extension import extension
from axisfuzzy.core import Fuzznum

@extension('distance', mtype='qrofn', target_classes=['Fuzznum'])
def qrofn_distance(f1: Fuzznum, f2: Fuzznum, p: int = 2) -> float:
    # qrofn-specific implementation
    ...

Default distance fallback:

from axisfuzzy.extension import extension
from axisfuzzy.core import Fuzznum

@extension('distance', is_default=True, target_classes=['Fuzznum'])
def default_distance(f1: Fuzznum, f2: Fuzznum) -> float:
    # generic fallback implementation
    ...