# 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
