Solving Mpld3 Incompatibility With Matplotlib In Geos-trame

by James Vasile 60 views

Hey guys! We've got a bit of a situation here with the new mpld3 update causing some trouble with geos-trame and its Matplotlib version. Let's dive into the issue, how we tackled it, and the solution that got us back on track.

The Problem: mpld3 and Matplotlib Mismatch

Recently, our CI builds on July 28th started throwing a new error during the geos-trame pytest runs. The culprit? The mpld3 library was making calls to axis.get_converter(), a method that, unfortunately, no longer exists in Matplotlib 3.9. This was causing a chain of failures in our tests, specifically:

  • FAILED geos-trame/tests/test_data_loader.py::test_data_loader - AttributeError: 'XAxis' object has no attribute 'get_converter'. Did you mean: 'get_inverted'?
  • FAILED geos-trame/tests/test_properties_checker.py::test_properties_checker - AttributeError: 'XAxis' object has no attribute 'get_converter'. Did you mean: 'get_inverted'?
  • FAILED geos-trame/tests/test_well_intersection.py::test_internal_well_intersection - AttributeError: 'XAxis' object has no attribute 'get_converter'. Did you mean: 'get_inverted'?
  • FAILED geos-trame/tests/test_well_intersection.py::test_vtk_well_intersection - AttributeError: 'XAxis' object has no attribute 'get_converter'. Did you mean: 'get_inverted'?

This error message, AttributeError: 'XAxis' object has no attribute 'get_converter', kept popping up, indicating a clear incompatibility issue between mpld3 and the Matplotlib version we were using.

Initial Investigation: Checking Dependencies

Our first step was to examine the pyproject.toml file of geos-trame. We found that it specified matplotlib==3.9.4. This confirmed that the Matplotlib version was indeed the one causing the issue. However, this particular version specification hadn't caused any problems before, so we knew we had to dig deeper.

A simple solution might seem like downgrading matplotlib to a compatible version. However, this approach could lead to dependency conflicts with numpy and potentially break other parts of the system. We needed a more targeted solution.

The Breakthrough: Focusing on mpld3

We then shifted our focus specifically to the mpld3 library. Looking at the package details, we noticed that mpld3 had been updated to version 0.5.11 on July 27th, 2025. This timing strongly suggested that the new update was the source of the incompatibility with our current matplotlib setup in trame.

The image below visually confirms the recent update and highlighted that the mpld3 update was the most probable reason for the observed issue.

Image

This update likely introduced changes that relied on features or methods not available in Matplotlib 3.9, specifically the missing axis.get_converter() method. To solve this, we needed to isolate geos-trame from this problematic update.

The Solution: Restricting mpld3 Version

The solution turned out to be relatively straightforward. We decided to restrict the version of mpld3 used in geos-trame to a version before 0.5.11. This would effectively prevent the problematic update from being installed and resolve the incompatibility issue.

To achieve this, we modified the pyproject.toml file of geos-trame. By specifying a version constraint for mpld3, we ensured that only compatible versions would be used. This approach avoided the need to downgrade matplotlib and risk causing conflicts with other dependencies like numpy. This targeted fix allowed us to maintain the stability of our environment while addressing the specific issue caused by the mpld3 update.

This approach ensures that geos-trame continues to use a version of mpld3 that is compatible with its existing Matplotlib setup, preventing the AttributeError from recurring. It's a clean and effective solution that minimizes the risk of introducing new issues.

Why This Matters: Dependency Management is Key

This whole episode highlights the importance of careful dependency management in software development. Libraries and packages are constantly evolving, and updates can sometimes introduce breaking changes. It's crucial to have strategies in place to handle these situations, such as:

  • Version Pinning: Specifying exact versions or version ranges in your project's dependency file (like pyproject.toml) to ensure consistency and prevent unexpected updates.
  • Continuous Integration (CI): Regularly running tests in a controlled environment to catch compatibility issues early on.
  • Dependency Auditing: Periodically reviewing your project's dependencies to identify potential vulnerabilities or conflicts.

By being proactive about dependency management, we can minimize the risk of encountering issues like this and ensure the stability of our projects.

Lessons Learned and Best Practices

This experience provided several valuable lessons regarding dependency management and troubleshooting in Python projects. Let's break down some key takeaways and best practices:

  1. Pinning Dependencies: As mentioned earlier, pinning dependencies is a crucial practice to ensure stability. By specifying the exact versions of your project's dependencies, you prevent unexpected updates from introducing breaking changes. In our case, if mpld3 had been explicitly versioned in geos-trame's pyproject.toml, the update to 0.5.11 might not have automatically occurred, thus avoiding the initial issue.

    • Why it works: Pinning provides a predictable environment. When you specify mpld3 == 0.5.10, you guarantee that your project will always use version 0.5.10 until you explicitly decide to upgrade.
    • How to implement: Use version specifiers in your pyproject.toml or requirements.txt file. For example:
      [project.dependencies]
mpld3 = "==0.5.10"
matplotlib = "==3.9.4"

  2. Understanding Error Messages: The error message AttributeError: 'XAxis' object has no attribute 'get_converter' was our initial clue. Learning to decipher these messages is essential for debugging. It told us that a specific method (get_converter) was missing from the XAxis object, which pointed to a potential API change or incompatibility.

    • Why it matters: Error messages provide direct insights into what went wrong. They highlight the specific line of code or function call that failed, giving you a starting point for investigation.
    • How to improve: Practice reading and interpreting error messages. Look for patterns and common errors in your projects to build familiarity.

  3. Isolating the Problem: When faced with a complex issue, isolating the problem is key. We initially considered downgrading matplotlib, but this could have introduced other conflicts. By focusing on mpld3, we narrowed down the source of the issue and found a more targeted solution. This involved:

    • Replicating the Issue: Create a minimal, reproducible example that demonstrates the problem. This helps you confirm that you've identified the core issue.
    • Checking Recent Changes: Look at recently updated dependencies or code changes that could be the root cause.
    • Testing Incrementally: If possible, revert changes one by one to identify the exact change that introduced the issue.

  4. Dependency Management Tools: Tools like pip and poetry offer powerful features for managing dependencies. Poetry, for example, provides more sophisticated dependency resolution and virtual environment management, which can help prevent conflicts and ensure reproducibility.

    • Why use them: Dependency management tools automate the process of installing, updating, and resolving dependencies, making it easier to maintain a stable environment.
    • How to leverage them: Use a tool like Poetry to manage your project's dependencies. Define your dependencies in the pyproject.toml file and use Poetry commands to install, update, and manage them.

  5. Continuous Integration (CI) is Your Friend: Our CI system caught this issue early, preventing it from affecting production. CI systems automatically run tests whenever code is changed, providing rapid feedback on potential problems. This allows you to identify and fix issues before they impact users.

    • Why it's critical: CI acts as a safety net. It ensures that your code is always in a working state and that changes don't introduce regressions.
    • How to implement: Set up a CI pipeline using tools like GitHub Actions, GitLab CI, or Jenkins. Configure your pipeline to run tests, lint code, and check for vulnerabilities automatically.

  6. Stay Informed: Keeping up-to-date with library updates and release notes can help you anticipate potential issues. mpld3's release notes might have mentioned changes to the axis.get_converter() method, which could have alerted us to the potential incompatibility.

    • Why it's beneficial: Staying informed allows you to proactively address potential issues before they cause problems.
    • How to stay informed: Subscribe to mailing lists, follow project blogs, and monitor release notes for the libraries you use.

By incorporating these lessons and best practices into your workflow, you can significantly improve your ability to manage dependencies and troubleshoot issues in your Python projects. This leads to more stable, reliable, and maintainable codebases.

Conclusion

So, there you have it! A little mpld3 update caused some chaos, but by carefully investigating the issue and focusing on the root cause, we were able to find a solution without creating further dependency conflicts. Remember, guys, dependency management is super important, and staying vigilant about updates can save you a lot of headaches down the road. Keep coding, and happy debugging!