OPTIMIZATION_SUMMARY.md

Performance Optimization Summary

Quick Summary

This PR implements performance optimizations across the OpenPIV codebase to reduce execution time and memory usage.

Files Changed

• openpiv/pyprocess.py - Vectorized array operations, reduced copies
• openpiv/validation.py - Eliminated unnecessary masked array copies
• openpiv/filters.py - Conditional masked array creation
• openpiv/test/test_performance.py - New performance validation tests (NEW)
• PERFORMANCE_IMPROVEMENTS.md - Detailed documentation (NEW)

Key Optimizations

1. Vectorized Operations: Replaced Python loops and list comprehensions with NumPy operations
2. Reduced Array Copies: Eliminated unnecessary copy operations, especially with masked arrays
3. Conditional Conversions: Only convert dtypes when necessary
4. Optimized Border Checking: Use np.maximum/np.minimum instead of array indexing

Performance Gains

• find_all_first_peaks: Fully vectorized, < 10ms for 100 windows
• normalize_intensity: Conditional conversion, < 50ms for 50 windows
• global_std: No copies for non-masked input, < 10ms for 100x100 arrays
• replace_outliers: Conditional masking, < 100ms for 50x50 arrays

Testing

✅ All 198 existing tests pass ✅ 5 new performance tests added ✅ Total: 203 tests pass in ~8 seconds ✅ Tutorial scripts verified working

Backward Compatibility

✅ 100% backward compatible

• Function signatures unchanged
• Return types unchanged
• Numerical results unchanged

Documentation

See PERFORMANCE_IMPROVEMENTS.md for:

• Detailed before/after code comparisons
• Performance metrics
• Future optimization opportunities
• General optimization principles

Impact

These optimizations particularly benefit:

• Large PIV analysis with many interrogation windows
• Iterative refinement algorithms
• High-resolution image processing
• Batch processing workflows