Date: 2025 04-April 23

Singleton
Passing variables between modules

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You're on the right track with using getters and setters for encapsulation, but when you start relying on class attributes for global state management, it can become tricky in terms of code maintainability and testing. Here's a closer look at some alternatives that may better preserve encapsulation and maintainability:

🚧 Challenges with Class Attributes

Class attributes that store global values can introduce issues like:

• Global state: Changing a class attribute anywhere in the code can impact other parts of the system.

• Testing difficulty: Tests might need to manipulate global state, making it harder to isolate tests.

• Coupling: Different parts of the codebase end up depending on the class attributes, making the system less flexible.

1. Singleton Pattern

Instead of using class attributes to manage global state, you can use the Singleton pattern to ensure a single instance of a class that handles global data.

Example: Active Project Singleton

class ActiveProject:
    _instance = None
    
    def __new__(cls):
        if cls._instance is None:
            cls._instance = super(ActiveProject, cls).__new__(cls)
            cls._instance.project = None
        return cls._instance
    
    @property
    def project(self):
        return self._instance.project
    
    @project.setter
    def project(self, value):
        self._instance.project = value


# Usage
project1 = ActiveProject()
project1.project = "Project A"
print(project1.project)  # Project A

project2 = ActiveProject()
print(project2.project)  # Project A (same instance)

Why use it?

• Encapsulates global state: The singleton encapsulates the global project state.

• Ensures one instance: Only one instance of ActiveProject exists, preventing inconsistent data across the application.

• Still accessible globally: You can access it from anywhere using the ActiveProject class, but you maintain control over the instantiation and data.

2. Dependency Injection

Instead of using class attributes or singletons, you can inject the active project or global settings as dependencies into classes that need them. This can be done manually or via a dependency injection framework.

Example: Manually Injecting Dependencies

class ProjectManager:
    def __init__(self, active_project):
        self.active_project = active_project

    def get_active_project(self):
        return self.active_project

# Usage
active_project = "Project A"
manager = ProjectManager(active_project)
print(manager.get_active_project())  # Project A

Why use it?

• Loose coupling: Classes don’t rely on global state; they receive what they need explicitly.

• Easier testing: You can easily mock or pass different projects to test different behaviors.

• Improved encapsulation: Each class only gets what it needs, improving maintainability and clarity.

3. Context Managers

If you need to manage temporary global states, like an "active" project for a scope of time, you can use a context manager to temporarily manage the state, ensuring that values are set and cleaned up properly.

Example: Context Manager for Active Project

from contextlib import contextmanager

@contextmanager
def set_active_project(project):
    old_project = ActiveProject().project
    ActiveProject().project = project
    try:
        yield
    finally:
        ActiveProject().project = old_project

# Usage
with set_active_project("Project B"):
    print(ActiveProject().project)  # Project B
print(ActiveProject().project)  # Default (or previously set) project

Why use it?

• Scoped management: Useful for temporary changes that should only persist within a context (e.g., a function or block of code).

• Clean-up: Ensures that the global state is reverted back to its original value, which prevents side effects.

4. Global State with a Dedicated Manager

Instead of using a class to store global data, you could create a dedicated manager or a settings class that manages all global state in a controlled way. This manager can be used to retrieve and modify global settings (like the active project).

Example: Global State Manager

class GlobalStateManager:
    def __init__(self):
        self._settings = {}

    def set(self, key, value):
        self._settings[key] = value

    def get(self, key):
        return self._settings.get(key)

# Usage
state_manager = GlobalStateManager()
state_manager.set("active_project", "Project A")
print(state_manager.get("active_project"))  # Project A

Why use it?

• Centralized control: A manager class centralizes access and modification of global state.

• Encapsulation: All global state management logic is contained in a single class, making it easier to change or extend.

• Flexibility: You can add methods for validation, logging, or other features that interact with the global state.

Conclusion

• Class attributes are fine for shared data when used sparingly, but they can lead to problems if abused.

• Singleton pattern offers a controlled way to manage a single global instance.

• Dependency injection makes your code more testable and decoupled.

• Context managers are great for temporary global states.

• Global state managers centralize access to shared data without relying on class attributes.

Best Practice

For global state management (like the active project), consider dependency injection or a dedicated manager class. This keeps your code more modular, testable, and maintainable while avoiding the pitfalls of global state or overuse of class attributes.

Let me know if you’d like a deeper dive into any of these!