Numpydoc Sphinx Dependency Discussion Refactoring For Docstring Parsing

by StackCamp Team 72 views

Hey guys, let's dive into an interesting discussion about making Sphinx a softer dependency for Numpydoc or even factoring out docstring parsing altogether. You might think, "Whoa, hold on! Numpydoc is a Sphinx extension, right?" And you're totally correct! However, there are some cool use cases where libraries rely on the NumpyDocString class to programmatically handle Numpydoc-style docstrings. Think of projects like Napari and Scipy – they're doing some nifty things with docstrings.

Background on the Issue

So, what's the fuss about the Sphinx dependency? Well, the core issue lies in Numpydoc's hard dependency on Sphinx. This means that if you want to use Numpydoc, you're also pulling in Sphinx, whether you need the full Sphinx functionality or not. This can lead to bloat and unnecessary overhead for projects that only need the docstring parsing capabilities.

For example, Napari, a powerful multi-dimensional image viewer, and Scipy, the go-to library for scientific computing in Python, both leverage the NumpyDocString class. Napari uses it to process and display docstrings in its user interface, making it easier for users to understand the functionality of different components. Scipy, on the other hand, has even gone so far as to vendor the NumpyDocString class, meaning they've essentially copied the code into their own codebase to avoid the Sphinx dependency. This highlights the importance of docstring parsing and why these projects are taking such measures.

The Fundamental Blocker

The fundamental blocker here is that transitive Sphinx dependency. Imagine you're building a small library that just needs to parse docstrings. You don't want to drag in the entire Sphinx ecosystem just for that! It's like needing a single wrench but having to carry around the whole toolbox.

Exploring Potential Solutions

To address this, we've got a couple of interesting paths to consider. Let's break them down and see what makes sense:

Option 1: Soften the Sphinx Dependency Within Numpydoc

One approach is to see if we can make Sphinx a soft dependency within Numpydoc. This means that Sphinx would only be required if you're using the parts of Numpydoc that directly rely on it. If you're just using the docstring parsing functionality, you wouldn't need to install Sphinx. At first glance, this seems pretty doable! The Sphinx-dependent stuff, like SphinxDocString, is relatively self-contained. It's like having a separate module that handles the Sphinx-specific tasks. However, as always, the devil's in the details. We'd need to carefully refactor the code to ensure that the dependency is truly optional and doesn't break anything.

To make Sphinx a soft dependency, we'd likely need to use conditional imports and check for the presence of Sphinx before using any Sphinx-related functionality. This might involve wrapping the Sphinx-dependent code in try...except blocks and providing fallback mechanisms if Sphinx isn't available. For instance, if Sphinx isn't installed, we might provide a simplified docstring output or skip certain processing steps.

Option 2: Split Out NumpyDocString into a Separate Library

Another option is to split the NumpyDocString class into a separate, dependency-less, pure Python library. This would create a dedicated package specifically for docstring parsing, without any ties to Sphinx. This approach is more straightforward in terms of isolating the functionality, but it does involve setting up a new project and moving a decent chunk of code. Think of it like creating a specialized tool just for docstring parsing, instead of trying to adapt an existing tool.

This approach would involve creating a new Python package, potentially named something like docstring-parser or numpydoc-parser, and moving the relevant code from Numpydoc into this new package. We'd need to carefully define the API of the new package and ensure that it's compatible with existing users of NumpyDocString. This might also involve creating a migration path for users who are currently relying on the class within Numpydoc.

Deep Dive into the Options

Let's get into the nitty-gritty of each option. What are the pros and cons? What challenges might we face?

Option 1: Soften the Sphinx Dependency

Pros:

  • Minimal Code Movement: This option keeps the core functionality within Numpydoc, minimizing disruption to existing users and developers.
  • Preserves Numpydoc Identity: Numpydoc remains the central hub for all things related to Numpydoc-style docstrings.

Cons:

  • Refactoring Complexity: Making Sphinx a soft dependency requires careful refactoring and conditional logic, which can be tricky to implement and maintain.
  • Potential for Breakage: Changes within Numpydoc could inadvertently affect the optional Sphinx dependency, leading to unexpected issues.

Challenges:

  • Conditional Imports: We'd need to use conditional imports extensively, which can make the code harder to read and understand.
  • Testing: Thorough testing is crucial to ensure that the optional Sphinx dependency works correctly in different scenarios.
  • Maintenance: Maintaining the conditional logic and ensuring compatibility with future Sphinx versions could be challenging.

Option 2: Split Out NumpyDocString

Pros:

  • Clear Separation of Concerns: A separate library provides a clear separation between docstring parsing and Sphinx integration.
  • Reduced Dependencies: Projects that only need docstring parsing won't have to install Sphinx.
  • Simplified Development: The new library can focus solely on docstring parsing, making development and maintenance easier.

Cons:

  • Code Duplication: Some code might need to be duplicated between Numpydoc and the new library.
  • Increased Maintenance Overhead: Maintaining two separate projects requires more effort.
  • Migration Complexity: Users who are currently using NumpyDocString within Numpydoc would need to migrate to the new library.

Challenges:

  • API Design: Designing a clear and consistent API for the new library is crucial.
  • Code Movement: Moving the code from Numpydoc to the new library requires careful planning and execution.
  • Migration Path: Providing a smooth migration path for existing users is essential.

Real-World Use Cases: Napari and Scipy

Let's take a closer look at how Napari and Scipy are using NumpyDocString and why this issue is important to them.

Napari

Napari uses NumpyDocString to parse docstrings and display them in its user interface. This allows users to quickly understand the functionality of different components and how to use them. By making Sphinx a soft dependency, Napari could potentially reduce its overall dependency footprint and improve its performance.

Scipy

Scipy has gone as far as vendoring the NumpyDocString class to avoid the Sphinx dependency. This highlights the importance of docstring parsing for Scipy and the lengths they're willing to go to avoid unnecessary dependencies. By splitting out NumpyDocString into a separate library, Scipy could potentially remove the vendored code and rely on a dedicated package for docstring parsing.

Conclusion: What's the Best Path Forward?

So, what's the best way to tackle this Sphinx dependency issue? Both options have their merits and drawbacks. Softening the Sphinx dependency within Numpydoc keeps the functionality in one place but introduces complexity. Splitting out NumpyDocString creates a cleaner separation but requires more code movement and maintenance.

Ultimately, the best approach depends on the priorities and resources of the Numpydoc community. We need to weigh the benefits of each option against the costs and challenges involved. It's crucial to consider the impact on existing users, the maintainability of the code, and the overall goals of the project.

Call to Action

I'm really interested to hear your thoughts and opinions on this! Have you encountered similar dependency issues in your projects? Which approach do you think is more viable? Let's discuss and figure out the best way to move forward!

Anyways, just raising this here so folks are aware of the motivating use-cases. If anyone has ideas/opinions about this scenario I'd be interested to hear them!