Optimize Performance for Large Datasets

[Phillyclause89]

Optimize Performance for Large Datasets

Description:
Analyze and improve the performance of the chess move heatmap generation, particularly when working with large datasets such as games with hundreds or thousands of moves. Identify bottlenecks and optimize the code to improve efficiency and scalability.

Scope of Work:

1. Profiling:
   • Use profiling tools like cProfile or line_profiler to pinpoint bottlenecks in the code.
   • Focus on methods related to heatmap generation and data processing in heatmaps/__init__.py.
     • Example: Review potential inefficiencies in __truediv__ (Code Reference).
2. Optimization:
   • Optimize data structures and algorithms, especially those handling large heatmaps or move data.
   • Explore parallel processing or vectorized operations for heavy calculations.
   • Implement caching mechanisms to avoid redundant computations.
3. Testing:
   • Benchmark the performance before and after optimizations to measure improvements.
   • Test the library with datasets of varying sizes to ensure scalability and robustness.
4. Documentation:
   • Document profiling results and the changes made to improve performance.
   • Provide guidelines for handling large datasets with the library.

Acceptance Criteria:

• Profiling results identify key bottlenecks in the codebase.
• Optimizations result in measurable improvements (e.g., reduced runtime, lower memory usage).
• The library handles large datasets efficiently without significant slowdowns.
• All tests pass successfully after changes.
• No breaking changes are introduced.