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Add optimization summary and finalize performance improvements
Co-authored-by: alexlib <[email protected]>
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OPTIMIZATION_SUMMARY.md

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# Performance Optimization Summary
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## Quick Summary
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This PR implements performance optimizations across the OpenPIV codebase to reduce execution time and memory usage.
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## Files Changed
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- `openpiv/pyprocess.py` - Vectorized array operations, reduced copies
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- `openpiv/validation.py` - Eliminated unnecessary masked array copies
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- `openpiv/filters.py` - Conditional masked array creation
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- `openpiv/test/test_performance.py` - New performance validation tests (NEW)
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- `PERFORMANCE_IMPROVEMENTS.md` - Detailed documentation (NEW)
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## Key Optimizations
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1. **Vectorized Operations**: Replaced Python loops and list comprehensions with NumPy operations
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2. **Reduced Array Copies**: Eliminated unnecessary copy operations, especially with masked arrays
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3. **Conditional Conversions**: Only convert dtypes when necessary
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4. **Optimized Border Checking**: Use np.maximum/np.minimum instead of array indexing
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## Performance Gains
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- `find_all_first_peaks`: Fully vectorized, < 10ms for 100 windows
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- `normalize_intensity`: Conditional conversion, < 50ms for 50 windows
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- `global_std`: No copies for non-masked input, < 10ms for 100x100 arrays
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- `replace_outliers`: Conditional masking, < 100ms for 50x50 arrays
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## Testing
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✅ All 198 existing tests pass
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✅ 5 new performance tests added
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✅ Total: 203 tests pass in ~8 seconds
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✅ Tutorial scripts verified working
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## Backward Compatibility
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✅ 100% backward compatible
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- Function signatures unchanged
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- Return types unchanged
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- Numerical results unchanged
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## Documentation
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See `PERFORMANCE_IMPROVEMENTS.md` for:
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- Detailed before/after code comparisons
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- Performance metrics
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- Future optimization opportunities
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- General optimization principles
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## Impact
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These optimizations particularly benefit:
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- Large PIV analysis with many interrogation windows
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- Iterative refinement algorithms
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- High-resolution image processing
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- Batch processing workflows

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