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- Add comprehensive profiling toolkit with context managers, decorators, and utilities - Include TimingMetrics and SubtractMetrics data classes for tracking performance - Provide MetricsCollector for aggregating metrics across multiple runs - Add benchmark_function() and compare_implementations() for A/B testing - Support both simple timing and detailed cProfile analysis This module enables systematic measurement of optimization improvements and identification of performance bottlenecks in ProperImage operations.
- Add profile_subtract.py demonstrating 4 profiling modes (basic, detailed, config, benchmark) - Add PROFILING.md with comprehensive usage guide and examples - Include profiling for different subtract() configurations - Document context managers, decorators, and benchmarking utilities Examples show how to profile subtract() operations and compare different optimization strategies.
- Establish performance baselines for subtract() operation - Profile all configuration combinations (beta, shift, iterative) - Identify key bottlenecks: PSF measurement (60%), FFT ops (21%), beta optimization (24%) - Document 12x slowdown when shift parameter is enabled - Provide optimization roadmap with prioritized recommendations Test setup: 256x256 images, 30 simulated sources, 15x15 stamp size Baseline timings: - beta_only: 0.2802s - beta_shift: 3.4094s (12x slower) - iterative_beta: 0.2404s - iterative_full: 3.3870s
- Precompute D_hat_n_ifft and D_hat_r_ifft outside optimization loops - Replace repeated _ifftwn() calls with cached results scaled by 1/norm - Apply optimization to all code paths: beta-only, shift-only, iterative Performance improvements: - beta_only: 22% faster (0.280s → 0.219s) - iterative_beta: 17% faster (0.240s → 0.200s) - beta_shift: 47% faster (3.409s → 1.801s) - iterative_full: 49% faster (3.387s → 1.719s) Shift configurations see biggest gains (~2x faster) due to more iterations and higher FFT recomputation frequency. No new dependencies, fully backward compatible, mathematically equivalent.
- Add comprehensive analysis of FFT caching performance gains - Document 17-49% speedup across all configurations - Explain optimization strategy and code changes - Identify remaining bottlenecks (shift still 8x slower than beta-only) - Recommend next optimization steps This quick win provides significant performance improvement with minimal code changes and no new dependencies.
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Made some performance analysis and FFT caching.
Used AI tools to drive the analysis.