Refactoring MyocardialMesh.py For Modularity And GPU Readiness

by Felix Dubois 63 views

Hey guys! Today, we're diving deep into a significant refactoring effort focused on the MyocardialMesh.py file. Currently, this file is a bit of a mixed bag, handling everything from input/output operations to numerical computations and visualization tasks. Our goal is to split it up into smaller, more manageable modules, which will not only improve readability but also pave the way for future GPU-accelerated computations. Let's break down why this is important and how we plan to tackle it.

Why Refactor MyocardialMesh.py?

The current structure of MyocardialMesh.py combines several distinct responsibilities, which can lead to a few key issues. First and foremost, it makes the code harder to read and maintain. When a single file handles multiple tasks, it becomes challenging to navigate and understand the interactions between different parts of the code. Secondly, this monolithic structure hinders our ability to optimize specific components. For instance, if we want to offload the numerical computations to a GPU, we need to isolate those parts of the code. Lastly, a clear separation of concerns allows for more focused testing and reduces the risk of introducing bugs when making changes.

The Need for Modularity

In software development, modularity is a critical principle. It involves breaking down a system into smaller, independent modules, each with a specific responsibility. This approach offers numerous benefits:

  • Improved Readability: Modules are easier to understand and work with since they focus on a single task.
  • Enhanced Maintainability: Changes in one module are less likely to affect others, reducing the risk of introducing bugs.
  • Increased Reusability: Modules can be reused in different parts of the system or even in other projects.
  • Easier Testing: Individual modules can be tested in isolation, making it easier to ensure they function correctly.
  • Better Collaboration: Multiple developers can work on different modules simultaneously, speeding up development.

Preparing for GPU Acceleration

One of the key motivations behind this refactoring is to prepare for future GPU acceleration. Numerical computations, such as those involved in solving lead field equations, can greatly benefit from the parallel processing capabilities of GPUs. However, to leverage GPUs effectively, we need to isolate the computational kernels into separate modules that can be easily ported to GPU-friendly frameworks like CUDA or OpenCL. This separation ensures that the rest of the codebase remains unaffected by the GPU-specific code, maintaining a clean and modular architecture.

The Plan: Splitting MyocardialMesh.py

Our strategy involves dividing MyocardialMesh.py into three new modules, each responsible for a specific set of tasks:

1. mesh_io.py – Handling Input/Output

The first module, mesh_io.py, will be dedicated to handling input and output operations. This includes reading and writing mesh data from various file formats, such as VTK and OBJ. By centralizing these operations, we can ensure consistency in how mesh data is handled throughout the system. This module will contain helper functions for reading and writing mesh data in VTK and OBJ formats, making it easier to load and save myocardial mesh data. The goal is to create a set of reusable functions that can be used across the project, ensuring consistent handling of mesh data.

  • Reading Mesh Data: Functions to load mesh data from VTK and OBJ files.
  • Writing Mesh Data: Functions to save mesh data to VTK and OBJ files.
  • Error Handling: Robust error handling to gracefully manage issues during file I/O.

2. lead_field.py – Numerical Solvers and Data Structures

Next up is lead_field.py, which will house the numerical solvers and data structures related to lead field computations. This is where the heavy lifting happens in terms of simulating electrical activity in the heart. By isolating these computations, we can optimize them independently and, as mentioned earlier, potentially offload them to a GPU in the future. This module will encapsulate the core numerical methods used for lead field calculations, along with the necessary data structures to represent the problem. By isolating these computations, we can optimize them independently and potentially offload them to a GPU in the future.

  • Lead Field Solvers: Implementations of numerical methods for solving lead field equations.
  • Data Structures: Definitions of data structures to represent meshes, electrodes, and other relevant data.
  • Optimization: Strategies for optimizing the performance of the solvers.

3. visualization.py – PyVista Helpers

Finally, visualization.py will contain helper functions for visualizing myocardial meshes using PyVista. This module will provide tools for creating informative and visually appealing representations of the heart's electrical activity. The goal here is to create a set of reusable functions that simplify the process of visualizing myocardial mesh data using PyVista. This module will provide tools for creating informative and visually appealing representations of the heart’s electrical activity.

  • Mesh Visualization: Functions to render myocardial meshes using PyVista.
  • Data Overlay: Functions to overlay simulation results (e.g., electrical potentials) onto the mesh.
  • Interactive Tools: Tools for interacting with the visualization, such as zooming and rotating.

4. MyocardialMesh.py – The Slim Facade

After the split, MyocardialMesh.py will become a slim facade. This means it will primarily serve as an entry point, importing functionality from the other modules. This approach keeps the main interface clean and easy to use while delegating the actual work to the specialized modules. The refactored MyocardialMesh.py will act as a central entry point, importing functionalities from the other modules. This approach keeps the main interface clean and easy to use while delegating the actual work to the specialized modules.

  • Import Statements: Import necessary functions and classes from mesh_io.py, lead_field.py, and visualization.py.
  • Simplified Interface: Provide a clean and intuitive interface for users to interact with the mesh functionality.
  • Delegation: Delegate tasks to the appropriate modules.

Tasks Breakdown

To make this refactoring manageable, we've broken it down into several key tasks:

  1. Create mesh_io.py: Implement the VTK/OBJ read-write helpers.
  2. Create lead_field.py: Develop the numerical solvers and data structures.
  3. Create visualization.py: Build the PyVista helpers.
  4. Keep a slim MyocardialMesh facade: Ensure MyocardialMesh.py is mostly a facade, importing from the other modules.
  5. Update imports: Adjust imports across the package and tests, avoiding circular dependencies.
  6. Ensure existing tests pass: Verify that all existing tests still pass after the refactoring.

Avoiding Circular Imports

One important consideration during this refactoring is to avoid circular imports. A circular import occurs when two or more modules depend on each other, creating a circular dependency. This can lead to issues during import and runtime errors. To prevent circular imports, we need to carefully plan the dependencies between modules and ensure that no module directly or indirectly imports itself.

  • Careful Planning: Analyze the dependencies between modules to identify potential circular imports.
  • Dependency Inversion: Use dependency inversion principles to break circular dependencies.
  • Import Strategies: Employ different import strategies (e.g., lazy imports) to resolve circular import issues.

Acceptance Criteria

To ensure we've successfully refactored MyocardialMesh.py, we've defined a set of acceptance criteria:

  • MyocardialMesh.py ≤ 100 LOC (mostly facade).
  • No linter errors (ruff) or mypy issues.
  • CI green on Windows + Linux.

Ensuring Code Quality

Maintaining code quality is paramount throughout this refactoring process. We'll be using several tools and techniques to ensure our code is clean, maintainable, and bug-free.

  • Linters: Tools like ruff help us identify and fix style issues and potential errors in our code.
  • Type Checkers: Mypy helps us catch type-related errors early in the development process.
  • Continuous Integration (CI): CI systems automatically run tests and linters whenever changes are made, ensuring that our code remains in good shape.

Conclusion

This refactoring of MyocardialMesh.py is a significant step towards improving the modularity, maintainability, and performance of our codebase. By splitting the file into specialized modules, we're not only making the code easier to work with but also paving the way for future optimizations, such as GPU acceleration. Stay tuned for updates as we progress with this exciting project!