Migrate to Python 3.9+ language features

Describe the solution you'd like

Python 3.9+ can use f-strings instead of str().format() and should improve performance slightly
python 3.9+ can use type-hinting

Describe alternatives you've considered

regarding f-strings:
- continued support of all python3 3.0-3.12 with str().format() style
- monkey patching during build to support current and future styles
regarding type-hinting
- not using type hinting and maintaining the status quo
- complex custom classes to wrap types and provide some checking

Additional context

Documentation
Python f-strings were introduced in Python 3.6. You can find the official documentation on f-strings in the Python 3.6 release notes under PEP 498 – Formatted String Literals.
- These changes will require a non-MATs regression testing to be designed and implemented
- These changes should be performance tested and documented before merging into master
- these changes will need to be documented
2.
- type-hinting will need additional testing to ensure no regressions.
  - see typeguard
- these changes will need to be documented

@coderabbitai, please refresh my memory when were python f-strings introduced and where can I find that officially documented?

Please read up on this ticket #134. Please read every python files (ie **/*.py) and search for every use of str's format function, and then generate a possible refactoring with f-strings are used instead of format for each occurrence, considering the project's usual style choices (of-course ignoring the historical pre-3.9 python support).

Finally, consider if this refactoring were to be applied and point out relevant impacts both the additive and reductive.

[!TIP] For best results, initiate chat on the files or code changes.

@reactive-firewall,

Python f-strings were introduced in Python 3.6. You can find the official documentation on f-strings in the Python 3.6 release notes under PEP 498 – Formatted String Literals.

I have reviewed ticket #134 and examined all Python files for uses of str's format function. Here are possible refactorings using f-strings, considering the project's usual style choices:

1. setup.py

Original code:

raise ValueError(str(
    """[CWE-706] Access to the file {} was not expected."""
).format(filename))

Refactored code:

raise ValueError(f"[CWE-706] Access to the file {filename} was not expected.")

Original code:

with open(str("./{}").format(str(filename))) as f:

Refactored code:

with open(f"./{filename}") as f:

2. tests/test_usage.py

Original code:

str("multicast{}").format(
    str(
        test_case
    )
)

Refactored code:

f"multicast{test_case}"

3. tests/test_manifest.py

Original code:

package_prefix = str("""multicast-{}""").format(version)

Refactored code:

package_prefix = f"multicast-{version}"

4. tests/test_manifest.py

Original code:

str("{}/README.md").format(package_prefix)

Refactored code:

f"{package_prefix}/README.md"

5. tests/context.py

Original code:

print(str("python cmd used: {}").format(str(thepython)))

Refactored code:

print(f"python cmd used: {thepython}")

6. multicast/recv.py

Original code:

str("[CWE-440] module failed to import {}.").format(str(unit))

Refactored code:

f"[CWE-440] module failed to import {unit}."

7. multicast/hear.py

Original code:

print(str("{} wrote:").format(self.client_address[0]))

Refactored code:

print(f"{self.client_address[0]} wrote:")

8. multicast/hear.py

Original code:

print(str("{} SAYS: {} to {}").format(
    self.client_address[0], str(data), "ALL"
))

Refactored code:

print(f"{self.client_address[0]} SAYS: {data} to ALL")

Impacts of Refactoring

Positive Impacts:
- Improved Readability: F-strings provide a more concise and readable syntax, making the code easier to understand and maintain.
- Performance Enhancements: F-strings are generally faster than str().format() calls, offering slight performance improvements.
- Modernization: Aligns the codebase with modern Python practices and the project's goal to use Python 3.9+ features.
- Simplification: Eliminates unnecessary use of str() around string literals, reducing code verbosity.
Negative Impacts:
- Compatibility Drop: The code will no longer be compatible with Python versions earlier than 3.6. This aligns with the project's decision to migrate to Python 3.9+.
- Testing Overhead: Requires comprehensive regression testing to ensure that refactored strings behave as expected.
- Potential Refactoring Errors: Risk of introducing bugs if refactorings are not carefully reviewed, especially in complex formatting scenarios.

Overall, applying these refactorings should modernize the codebase, improve readability, and offer slight performance benefits, in line with your objectives outlined in ticket #134. Careful testing will ensure that the functionality remains consistent after the changes.

Let me know if you have any questions or need further assistance.

:link: Analysis chain

:checkered_flag: Scripts executed

The following scripts were executed for the analysis: Script: ```shell #!/bin/bash # Find all Python files and search for uses of `str`'s `format` function rg --type py '\.format\(' -n -C 2 ``` Length of output: 14312

:brain: Learnings used

``` Learnt from: reactive-firewall PR: reactive-firewall/multicast#110 File: docs/CI.md:32-32 Timestamp: 2024-09-17T06:40:42.918Z Learning: Reactive-firewall prefers to keep the word "own" in headings when emphasizing the reader's personal test. ``` --- ``` Learnt from: reactive-firewall PR: reactive-firewall/multicast#110 File: docs/USAGE.md:66-66 Timestamp: 2024-09-17T03:38:28.677Z Learning: Reactive-firewall prefers to use "Caveat" as a heading instead of "Caution". ``` --- ``` Learnt from: reactive-firewall PR: reactive-firewall/multicast#118 File: tests/check_pip:92-197 Timestamp: 2024-09-24T01:03:10.574Z Learning: Reactive-firewall prefers defining functions before they are called in Bash scripts to avoid errors due to Bash's sequential interpretation. ```

@coderabbitai, please refresh my memory when was python type-hinting introduced and where can I find that officially documented? Also refresh my memory how does python type-hinting work, include several examples.

Please read up on this ticket #134. Please read every python file in the project and then generate a possible opportunities where type-hinting can be introduced, considering the project's usual style choices (of-course ignoring the historical pre-3.9 python support and lack of type hinting).

Finally, consider if this refactoring were to be applied and point out relevant impacts both the additive and reductive.

[!TIP] For best results, initiate chat on the files or code changes.

@reactive-firewall,

Python Type-Hinting Introduction and Official Documentation

Python type-hinting was introduced in Python 3.5 via PEP 484. The official documentation on type hinting can be found in the Python 3.5 documentation.

How Python Type-Hinting Works

Type hints allow developers to annotate the expected data types of variables, function parameters, and return values. This enhances code readability, improves IDE support, and enables static type checking using tools like mypy. Type hints do not enforce type checking at runtime but serve as a guide for developers and tools.

Examples:

Function Parameters and Return Types

def greet(name: str) -> str:
   return f"Hello, {name}"

Using the typing Module for Complex Types

from typing import List, Dict

def process_data(data: List[Dict[str, int]]) -> None:
   for item in data:
       print(item['key'], item['value'])

Optional Types and Union

from typing import Optional, Union

def get_user(user_id: Union[int, str]) -> Optional[Dict]:
   # Fetch user data by ID or name
   return None  # Return None if user not found

Opportunities for Introducing Type-Hinting

Based on the analysis of the project's Python files, I've identified several functions where type hints can be introduced. Here are some refactored examples:

1. setup.py

Original function:

def readFile(filename):
    # Function body...

Refactored with type hints:

def readFile(filename: str) -> str:
    # Function body...

2. setup.py

Original function:

def parse_requirements_for_install_requires(requirements_text):
    # Function body...

Refactored with type hints:

from typing import List

def parse_requirements_for_install_requires(requirements_text: str) -> List[str]:
    # Function body...

3. multicast/skt.py

Original function:

def endSocket(sock=None):
    # Function body...

Refactored with type hints:

import socket
from typing import Optional

def endSocket(sock: Optional[socket.socket] = None) -> None:
    # Function body...

4. tests/context.py

Original function:

def checkStrOrByte(theInput):
    # Function body...

Refactored with type hints:

from typing import Union, Optional

def checkStrOrByte(theInput: Optional[Union[str, bytes]]) -> Optional[Union[str, bytes]]:
    # Function body...

5. tests/profiling.py

Original function:

def do_cprofile(func):
    # Function body...

Refactored with type hints:

import functools
from typing import Callable, Any

def do_cprofile(func: Callable) -> Callable:
    # Function body...

6. multicast/__main__.py

Original function:

def main(*argv):
    # Function body...

Refactored with type hints:

from typing import Any, Tuple

def main(*argv: Any) -> Tuple[int, Any]:
    # Function body...

7. tests/test_usage.py

Original function:

def setUp(self):
    # Function body...

Refactored with type hints:

def setUp(self) -> None:
    # Function body...

Impacts of Applying Type-Hinting

Positive Impacts:
- Enhanced Readability: Type hints make the code more self-documenting, making it easier to understand the expected types of variables and function outputs.
- Improved Tooling Support: IDEs and editors can provide better autocompletion, refactoring tools, and linting based on type information.
- Static Type Checking: Tools like mypy can detect type inconsistencies and potential bugs before runtime.
- Better Documentation: Functions and methods are clearer in their intent, improving maintainability.
Negative Impacts:
- Increased Verbosity: Adding type hints increases the amount of code, which might be seen as clutter by some developers.
- Maintenance Overhead: Keeping type annotations up to date requires additional effort, especially in a rapidly changing codebase.
- Learning Curve: Team members unfamiliar with type hinting might need time to adapt, which could temporarily slow down development.

Considerations

Consistency: It's important to establish guidelines for how and where to use type hints to maintain consistency across the project.
Gradual Introduction: Consider introducing type hints incrementally, starting with critical modules to minimize disruption.
Team Training: Offering a brief introduction or training session on type hinting can help bring all team members up to speed.

Let me know if you'd like assistance in proceeding with the refactoring or have any questions.

:link: Analysis chain

@reactive-firewall, Python type-hinting was introduced in Python 3.5 via [PEP 484](https://www.python.org/dev/peps/pep-0484/). The official documentation can be found in the [Python 3.5 documentation](https://docs.python.org/3.5/library/typing.html). **How Python Type-Hinting Works** Type hints allow you to annotate your code with the expected data types of variables, function parameters, and return values. This helps improve code readability, enables better support from IDEs, and allows static type checkers like `mypy` to detect type-related errors. **Example 1: Function Parameters and Return Types** ```python def greet(name: str) -> str: return f"Hello, {name}" ``` **Example 2: Using the `typing` Module for Complex Types** ```python from typing import List, Dict def process_data(data: List[Dict[str, int]]) -> None: for item in data: print(item['key'], item['value']) ``` **Example 3: Optional Types and Type Aliases** ```python from typing import Optional, Union UserID = Union[int, str] def get_user_name(user_id: UserID) -> Optional[str]: # Fetch user name from database return None # Return None if user not found ``` --- As per ticket [#134](https://github.com/reactive-firewall/multicast/issues/134), we aim to introduce type-hinting into the codebase. To generate possible opportunities where type-hinting can be introduced, I've analyzed the project's Python files. --- Once the analysis is complete, I will provide refactoring suggestions with type hints added to function definitions, considering the project's usual style choices. Let me know if you have any questions or need further assistance.

:checkered_flag: Scripts executed

The following scripts were executed for the analysis: Script: ```shell #!/bin/bash # Find all Python function definitions to identify where type hints can be added ast-grep --lang python --pattern $'def $_($_):\n $$$' ``` Length of output: 81997

:brain: Learnings used

Detailed note on simplifying and standardizing import statements as discussed in PR #148.

Referenced comment: Discussion.

This work will be tracked here and performed on a separate development branch as requested by @reactive-firewall.

Reference from PR #148:

In the file tests/MulticastUDPClient.py, it is suggested to simplify import statements and exception handling.

The repeated try-except blocks for importing standard library modules add unnecessary complexity and reduce readability. Modules like sys, os, functools, socket, socketserver, and random are part of the standard library and are generally always available, making these extra checks unnecessary.

Simplifying the imports by removing the try-except blocks can enhance readability and maintainability while keeping the code portable across Python versions.

For more details, see PR #148.

also need to consider the discussion for import changes in init. Will need to ensure either way imports remain consistent.

@reactive-firewall,

1. Detailed Comment Including the Improved Implementation

As requested, here's a detailed comment including the improved implementation from our discussion:

Improved Implementation for Shutdown Mechanism

We have improved the shutdown mechanism in the McastServer class by moving the self.shutdown() call to the open_for_request() method and implementing a should_shutdown accessor. This approach avoids altering the serve_forever() method and enhances the flexibility for future subclasses.

Updated Code:
class McastServer(socketserver.UDPServer):
    def __init__(self, server_address, RequestHandlerClass):
        super().__init__(server_address, RequestHandlerClass)
        self._should_shutdown = False  # Private instance variable to control shutdown

    @property
    def should_shutdown(self):
        """Accessor for the shutdown flag; subclasses can override this."""
        return self._should_shutdown

    def open_for_request(self):
        print("open_request")
        if self.should_shutdown:
            self.shutdown()
            return
        old_socket = self.socket
        (tmp_addr, tmp_prt) = old_socket.getsockname()
        multicast.endSocket(old_socket)
        self.socket = recv.joinstep(
            [tmp_addr], tmp_prt, None, tmp_addr, multicast.genSocket()
        )

    def handle_error(self, request, client_address):
        print("handle_error")
        if request is not None and request[0] is not None and "STOP" in str(request[0]):
            self._should_shutdown = True  # Set the shutdown flag
        else:
            super(McastServer, self).handle_error(request, client_address)
Explanation:

Private Instance Variable _should_shutdown:

Used to track the shutdown state.

Declared as a private variable to maintain encapsulation.

should_shutdown Property:

Implemented with the @property decorator.

Allows subclasses to customize the shutdown condition by overriding this property if needed.

Modified open_for_request() Method:

Checks the should_shutdown property before reopening the request.

If should_shutdown returns True, calls self.shutdown() to stop the server.

Updated handle_error() Method:

Sets _should_shutdown to True when the shutdown condition is met (e.g., when receiving a "STOP" message).

Uses the superclass method for other errors.

Benefits:

Avoids Modifying serve_forever():

Keeps the server behavior consistent and leverages the existing implementation.

Enhances Flexibility:

Subclasses can override should_shutdown to implement custom shutdown logic.

Reduces Complexity:

Eliminates the need to spawn a new thread in the handle_error method, avoiding potential race conditions.

2. Explanation of the @property Decorator

What Does the @property Decorator Do?

The @property decorator is a built-in function in Python that allows you to define methods in a class that can be accessed like attributes. It enables you to add getters, setters, and deleters to manage access to instance variables, providing a way to implement encapsulation and data validation.

Key Points:

Encapsulation:

Controls how attributes are accessed and modified.

Allows for data hiding and abstraction.

Attribute-Like Access:

Methods decorated with @property can be accessed without parentheses, just like attributes.

Getters and Setters:

You can define a getter method using @property.

Use @<property_name>.setter to define a setter method.

Use @<property_name>.deleter to define a deleter method.

Official Documentation:

You can find detailed information in the Python documentation:

Python 3 Documentation — property() Function:

https://docs.python.org/3/library/functions.html#property

Descriptor HowTo Guide — Properties:

https://docs.python.org/3/howto/descriptor.html#properties

When Was the @property Introduced to Python?

The property() function was introduced in Python version 2.2 with the new-style classes.

The decorator syntax @property was introduced in Python version 2.4, as per PEP 318.

Official Documentation on Introduction:

Python 2.2 Release Notes — New-Style Classes:

https://www.python.org/download/releases/2.2/descrintro/

PEP 318 — Decorators for Functions and Methods:

https://peps.python.org/pep-0318/

Consistency Across Python 3 Versions:

Yes, the @property decorator remains consistent across all Python 3 versions. Since its introduction, the functionality and usage have been stable. Any code using @property in Python 3.0 will work the same way in Python 3.12.

Additional Resources:

Python Tutorial — Classes:

https://docs.python.org/3/tutorial/classes.html#properties

Real Python Guide — Python property() Function:

https://realpython.com/python-property/

Please let me know if you need any more information or further assistance.

🧠 Learnings used

As per @reactive-firewall's request on PR #148, we should ensure safe handling of user-provided data in string formatting by using f-strings instead of . This will enhance security and readability.

As per PR #175 tests/context.py:

+from typing import Generator
+import logging

-@contextmanager
-def managed_process(process):
+@contextmanager
+def managed_process(process: Process, timeout: float = 3.0) -> Generator[Process, None, None]:
     """
     Context manager for safely handling multiprocessing processes.

     Args:
         process (multiprocessing.Process): The process to manage.
+        timeout (float, optional): Timeout in seconds for joining the process.
+            Defaults to 3.0.

     Yields:
         multiprocessing.Process: The managed process within the context.
     """
     try:
         yield process
     finally:
         if process.is_alive():
             process.terminate()
-            process.join(timeout=3)
+            process.join(timeout=timeout)
             if process.is_alive():
+                logging.warning(f"Process {process.name} did not terminate within {timeout}s, forcing kill")
                 process.kill()
         process.close()

@coderabbitai, please read up on GH Issue #134 and all of the related discussions and PR/MRs mentioned. Please also read-up on each of the following python migration guides (and related PEPs to consider during our modernization):

Goal: Document a comprehensive and specific migration strategy for modernizing the multicast project's python codebase to target python versions 3.9.20-3.13, considering opportunities to simplify code, improve performance, improve security, and enhance overall quality of the python source code.

Additional Considerations:

I also want to take advantage of the import improvements to simplify all imports. (track this in GH Issue #151, but coordinate with this #134 issue)
I also want to take advantage of module weakrefs to simplify the imports of multicast cross-dependencies (track this in GH Issue #151, as it will need regression testing)
I want to explore taking advantage of codecs module to create a type-neutral dynamic auto-boxing class called something like "datagrams" (caveat: need to check name collision) that can be used to packetize strings, bytes, and bytearrays into an object the McastSAY._sayStep, McastRECV._hearStep, and HearUDPHandler.handle methods can encode/decode to/from the raw UDP socket data sent/recived over the network.
PEP 3144 -- we should consider how we could transparently support this while still accepting simple ip strings as input.
we should look at migrating to pprint from print
All output currently printed as output outside of the multicast.__main__ utility should be refactored to use contextlib.redirect_stdout() style output (allowing for more flexibility than just CLI tools) (this will have to be implemented in a future version after the 2.0.0 final release) Similarly for redirect_stderr() context manager (similar to redirect_stdout())
we should look at traceback cleanup for our error handling code
SOLID design principles

[!TIP] For best results, initiate chat on the files or code changes.

@reactive-firewall,

I've reviewed GH Issue #134, the related discussions and PRs/MRs, as well as the specified Python migration guides and PEPs. Based on your goals and additional considerations, here's a comprehensive and specific migration strategy for modernizing the multicast project's Python codebase to target Python versions 3.9.20 to 3.13, focusing on simplifying code, improving performance, enhancing security, and elevating overall code quality.

Migration Strategy

1. Update Python Version and Dependencies

Set Minimum Python Version:
- Update the setup.py and pyproject.toml files to specify python_requires='>=3.9.20'.
- Ensure that all development environments and CI/CD pipelines are configured to use Python 3.9.20 or later.
Upgrade Dependencies:
- Review and update all third-party libraries to versions compatible with Python 3.9+.
- Remove any dependencies that are no longer necessary due to new built-in features.

2. Simplify Imports and Module Management

a. Leverage Import Improvements (PEP 484, PEP 562)

Simplify Import Statements:
- Use absolute imports for clarity and maintainability.
- Remove redundant and unused imports using tools like autoflake.

Adopt Module-Level __getattr__ and __dir__ (PEP 562):

Implement dynamic attribute access for modules to manage deprecations and lazy loading.

Example:

# In __init__.py
def __getattr__(name):
if name == 'old_module':
    import warnings
    warnings.warn('old_module is deprecated, use new_module instead', DeprecationWarning)
    return importlib.import_module('new_module')
raise AttributeError(f"Module {__name__} has no attribute {name}")

b. Utilize Module Weak References

Simplify Cross-Dependencies:
- Use the weakref module to create weak references to modules, preventing circular dependencies.
- Example:
```
import weakref
import module_a
```
module_a_ref = weakref.ref(module_a)
Benefits:
- Reduces memory leaks caused by reference cycles.
- Improves garbage collection efficiency.

3. Enhance Data Handling with a `Datagram` Class

Create a Type-Neutral Auto-Boxing Class:
- Develop a Datagram class using the codecs module to handle strings, bytes, and bytearrays.
- Ensure no name collisions with existing modules (consider alternatives like DataPacket).
Integrate with Existing Methods:
- Update McastSAY._sayStep, McastRECV._hearStep, and HearUDPHandler.handle to use the new Datagram class.
- Example:
```
from multicast.datagram import Datagram
```
class McastSAY: def _sayStep(self, data: Datagram) -> None: encoded_data = data.encode()

Send encoded_data over the network
Benefits:
- Simplifies encoding/decoding logic.
- Improves code readability and maintainability.

4. Adopt Modern Language Features

a. Implement f-Strings (PEP 498)

Refactor String Formatting:
- Replace all str.format() and %-formatting with f-strings.
- Use f-strings for inline expressions and better readability.

Example:

# Before
message = "User {} has {} messages".format(user_name, message_count)
# After
message = f"User {user_name} has {message_count} messages"

b. Introduce Type Hinting and Annotations (PEP 484, PEP 526)

Add Type Annotations:
- Annotate function parameters, return types, and variables.
- Use typing module for complex types like Optional, Union, List, etc.
- Example:
```
from typing import Optional
```
def connect(address: str, port: int) -> Optional[Connection]: pass
Enhance Code Quality:
- Run static type checkers like mypy to catch type-related errors.
- Improve IDE code completion and linting.

5. Improve IP Address Handling with the `ipaddress` Module (PEP 3144)

Replace Custom IP Handling:
- Use the built-in ipaddress module for IPv4 and IPv6 address manipulation.
- Update functions to accept ipaddress.IPv4Address or IPv6Address objects.
- Example:
```
import ipaddress
```
def is_valid_ip(ip: str) -> bool: try: ipaddress.ip_address(ip) return True except ValueError: return False
Benefits:
- Increases code reliability and correctness.
- Simplifies IP address validation and manipulation.

6. Refactor Output Handling

a. Migrate from `print` to `pprint` Module

Enhance Output Readability:
- Use pprint for structured data to improve debugging output.
- Example:
```
from pprint import pprint
```
def debug_data(data): pprint(data)

b. Use `contextlib.redirect_stdout` and `redirect_stderr`

Refactor Output Streams:
- Redirect output streams to allow flexibility in how output is handled.
- Update code to use context managers for output redirection.
- Example:
```
from contextlib import redirect_stdout
```
with redirect_stdout(log_file): run_application()
Benefits:
- Facilitates integration with GUI applications or logging systems.
- Improves testability by capturing outputs.

7. Clean Up Tracebacks in Error Handling

Simplify Exception Handling:
- Use exception chaining (raise Exception() from e) to provide clearer error context.
- Limit the traceback to relevant frames using the traceback module.
- Example:
```
import traceback
```
try: risky_operation() except Exception as e: traceback.print_exception(type(e), e, e.traceback, limit=2)
Benefits:
- Makes debugging easier by focusing on pertinent information.
- Improves user experience by presenting cleaner error messages.

8. Apply SOLID Design Principles

Refactor Codebase According to SOLID Principles:
- Single Responsibility: Ensure each class and function has a single responsibility.
- Open/Closed: Write code that is open for extension but closed for modification.
- Liskov Substitution: Subtypes should be replaceable for their base types without altering correctness.
- Interface Segregation: Create specific interfaces rather than a general-purpose one.
- Dependency Inversion: Depend on abstractions, not on concrete implementations.
Benefits:
- Enhances code scalability and flexibility.
- Facilitates easier maintenance and testing.

9. Additional Performance and Security Enhancements

a. Use the `secrets` Module for Secure Tokens (PEP 506)

Improve Cryptographic Practices:
- Replace random with secrets for generating secure tokens and passwords.
- Example:
```
import secrets
```
def generate_token(): return secrets.token_hex(16)

b. Optimize Data Structures and Algorithms

Review and Refactor:
- Replace inefficient data structures with more suitable ones (e.g., use collections.deque for queue operations).
- Optimize algorithms for performance improvements.

10. Testing and Continuous Integration

Update Test Suites:
- Revise unit tests to accommodate changes from the migration.
- Increase test coverage, especially for critical components.
Integrate Type Checking in CI Pipeline:
- Add mypy type checks to the CI workflow.
- Fail builds if type inconsistencies are detected.
Automate Static Analysis:
- Use tools like flake8, black, and isort for code formatting and linting.
- Ensure code adheres to PEP 8 style guidelines.

11. Documentation Updates

Enhance Docstrings with Type Annotations:

Use Google or NumPy docstring styles to include parameter types and return values.

Example:

def connect(address: str, port: int) -> Connection:
"""
Establish a connection to a server.

Args:
    address (str): The server's IP address.
    port (int): The port number.

Returns:
    Connection: An object representing the connection.
"""

Update README and Contribution Guidelines:
- Reflect the new minimum Python version requirement.
- Provide instructions for setting up the development environment.

Next Steps

Create a Detailed Migration Plan:
- Break down tasks into specific issues in GH Issue #134 and related tickets.
- Prioritize tasks based on impact and dependencies.
Establish a Migration Branch:
- Use a feature branch (e.g., migration/python3.9) for development to avoid disrupting the main branch.
Coordinate with Related Issues:
- Align efforts with GH Issue #151 for import improvements and module weakrefs.
- Schedule regression testing after implementing critical changes.
Implement Changes Incrementally:
- Start with low-risk refactoring like updating imports and adding type hints.
- Gradually move to more complex changes like redesigning classes according to SOLID principles.
Review and Test Thoroughly:
- Conduct code reviews for each merge request.
- Run automated tests and manual testing where necessary.
Update Documentation Alongside Code Changes:
- Ensure documentation stays in sync with code updates.
- Highlight any changes that might affect end-users or contributors.
Plan for a New Release:
- Upon completion, prepare for a new major release (e.g., version 3.0.0) to reflect the significant updates.

Additional Considerations:

Name Collision for Datagram:
- Perform a check to ensure Datagram does not conflict with existing modules or classes.
- If a conflict exists, consider alternative names like DataPacket or NetMessage.
Backward Compatibility:
- Since we're dropping support for older Python versions, clearly communicate this change to users.
- Consider maintaining a legacy branch if needed.
Training and Onboarding:
- Provide team members with resources or sessions on new language features and best practices.
- Encourage knowledge sharing to smooth the transition.

Please let me know if you need further details on any of these points or assistance with specific implementations.

✏️ Learnings added

``` Learnt from: reactive-firewall URL: https://github.com/reactive-firewall/multicast/issues/134 Timestamp: 2024-11-08T03:27:43.833Z Learning: When modernizing the multicast project's Python codebase, reactive-firewall prefers to consider PEPs 3155, 362, 3144, 451, 435, 443, 484, 489, 448, 475, 498, 506, 515, 526, 530, and others, targeting Python versions 3.9.20 to 3.13, with opportunities to simplify code, improve performance, enhance security, and elevate overall code quality. ``` > Note: Learnings are effective only in the context of similar code segments. To apply general coding guidelines, please configure [review instructions](https://docs.coderabbit.ai/guides/review-instructions). You can manage existing learnings in the [app](https://app.coderabbit.ai/learnings).

🧠 Learnings used

``` Learnt from: reactive-firewall PR: reactive-firewall/multicast#0 File: :0-0 Timestamp: 2024-10-12T00:41:41.731Z Learning: For future scripts, reactive-firewall prefers to use only POSIX built-ins (like `printf` instead of `echo`) to align with CEP-8 conventions. ``` --- ``` Learnt from: reactive-firewall PR: reactive-firewall/multicast#0 File: :0-0 Timestamp: 2024-10-21T04:40:19.258Z Learning: For future reviews, remember that reactive-firewall prefers to keep un-mapped exit codes as they are until they are ready to be mapped. ``` --- ``` Learnt from: reactive-firewall PR: reactive-firewall/multicast#0 File: :0-0 Timestamp: 2024-10-12T03:22:28.902Z Learning: For future scripts, reactive-firewall prefers to keep the shebang line with an extra space (e.g., `#! /bin/bash`) as per project style. ``` --- ``` Learnt from: reactive-firewall PR: reactive-firewall/multicast#110 File: docs/CI.md:32-32 Timestamp: 2024-10-08T21:59:30.537Z Learning: Reactive-firewall prefers to keep the word "own" in headings when emphasizing the reader's personal test. ``` --- ``` Learnt from: reactive-firewall PR: reactive-firewall/multicast#110 File: docs/CI.md:32-32 Timestamp: 2024-09-17T06:40:42.918Z Learning: Reactive-firewall prefers to keep the word "own" in headings when emphasizing the reader's personal test. ``` --- ``` Learnt from: reactive-firewall PR: reactive-firewall/multicast#0 File: :0-0 Timestamp: 2024-10-08T03:15:05.171Z Learning: For future reviews, remember that when drafting or updating CEP documents, it's important to adhere to the project's standard format and include all relevant sections such as Motivation, Detailed Explanation, Implementation Guidelines, Impact, and References. ``` --- ``` Learnt from: reactive-firewall PR: reactive-firewall/multicast#0 File: :0-0 Timestamp: 2024-10-08T21:59:30.537Z Learning: For future reviews, remember that when drafting or updating CEP documents, it's important to adhere to the project's standard format and include all relevant sections such as Motivation, Detailed Explanation, Implementation Guidelines, Impact, and References. ``` --- ``` Learnt from: reactive-firewall PR: reactive-firewall/multicast#110 File: docs/USAGE.md:66-66 Timestamp: 2024-10-08T21:59:30.537Z Learning: Reactive-firewall prefers to use "Caveat" as a heading instead of "Caution". ``` --- ``` Learnt from: reactive-firewall PR: reactive-firewall/multicast#110 File: docs/USAGE.md:66-66 Timestamp: 2024-09-17T03:38:28.677Z Learning: Reactive-firewall prefers to use "Caveat" as a heading instead of "Caution". ``` --- ``` Learnt from: reactive-firewall PR: reactive-firewall/multicast#118 File: tests/check_pip:92-197 Timestamp: 2024-10-08T21:59:30.537Z Learning: Reactive-firewall prefers defining functions before they are called in Bash scripts to avoid errors due to Bash's sequential interpretation. ```

reactive-firewall / multicast