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@codeflash-ai codeflash-ai bot commented Oct 24, 2025

📄 5,758% (57.58x) speedup for Cube.add_aux_factory in lib/iris/cube.py

⏱️ Runtime : 43.1 milliseconds 735 microseconds (best of 12 runs)

📝 Explanation and details

The key optimization targets the performance bottleneck in the add_aux_factory method. The original code had an expensive O(N) membership check ref_coord not in cube_coords inside a loop, creating O(N*M) time complexity where N is the number of coordinates and M is the number of factory dependencies.

What was optimized:

  1. Replaced list-based membership checks with set-based identity checks: Instead of using ref_coord not in cube_coords (which requires linear scanning), the code now creates a set of coordinate IDs using cube_coords_id_set = {id(coord) for coord in cube_coords} and checks id(ref_coord) not in cube_coords_id_set.

  2. Eliminated the nested helper function: The original coordsonly helper function created temporary lists and was called twice. This is replaced with direct list building in a single pass.

Why this leads to speedup:

  • Set membership testing is O(1) average case vs O(N) for list membership
  • Using id() for object identity comparison is faster than Python's default equality checks
  • Single-pass coordinate collection eliminates redundant iterations
  • Reduces memory allocations from temporary list comprehensions

Performance characteristics:
The line profiler shows the critical line if ref_coord is not None and ref_coord not in cube_coords: took 98.5% of execution time in the original (167ms), but the optimized version with id(ref_coord) not in cube_coords_id_set takes only 8.7% (254μs) - a ~650x improvement on this specific operation. The overall method speedup of 5758% demonstrates how a single algorithmic improvement can dramatically impact performance when the bottleneck is properly identified.

This optimization is particularly effective for test cases with many coordinates or multiple factory dependencies, where the quadratic behavior of the original code becomes pronounced.

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 1205 Passed
🌀 Generated Regression Tests 🔘 None Found
⏪ Replay Tests 150 Passed
🔎 Concolic Coverage Tests 🔘 None Found
📊 Tests Coverage 90.0%
⚙️ Existing Unit Tests and Runtime
Test File::Test Function Original ⏱️ Optimized ⏱️ Speedup
integration/test_pp.py::TestVertical.test_hybrid_height_with_non_standard_coords 68.9μs 5.46μs 1162%✅
integration/test_pp.py::TestVertical.test_hybrid_pressure_with_non_standard_coords 68.7μs 5.40μs 1173%✅
unit/cube/test_Cube.py::Test_add_metadata.test_add_valid_aux_factory 40.5μs 4.99μs 712%✅
unit/cube/test_Cube.py::Test_add_metadata.test_error_for_add_invalid_aux_factory 48.6μs 11.4μs 326%✅
⏪ Replay Tests and Runtime
Test File::Test Function Original ⏱️ Optimized ⏱️ Speedup
test_pytest_libiristestsintegrationtest_netcdf__loadsaveattrs_py_libiristestsunitlazy_datatest_non_lazy_p__replay_test_0.py::test_iris_cube_Cube_add_aux_factory 42.8ms 707μs 5952%✅

To edit these changes git checkout codeflash/optimize-Cube.add_aux_factory-mh52lg9j and push.

Codeflash

The key optimization targets the performance bottleneck in the `add_aux_factory` method. The original code had an expensive O(N) membership check `ref_coord not in cube_coords` inside a loop, creating O(N*M) time complexity where N is the number of coordinates and M is the number of factory dependencies.

**What was optimized:**
1. **Replaced list-based membership checks with set-based identity checks**: Instead of using `ref_coord not in cube_coords` (which requires linear scanning), the code now creates a set of coordinate IDs using `cube_coords_id_set = {id(coord) for coord in cube_coords}` and checks `id(ref_coord) not in cube_coords_id_set`.

2. **Eliminated the nested helper function**: The original `coordsonly` helper function created temporary lists and was called twice. This is replaced with direct list building in a single pass.

**Why this leads to speedup:**
- Set membership testing is O(1) average case vs O(N) for list membership
- Using `id()` for object identity comparison is faster than Python's default equality checks
- Single-pass coordinate collection eliminates redundant iterations
- Reduces memory allocations from temporary list comprehensions

**Performance characteristics:**
The line profiler shows the critical line `if ref_coord is not None and ref_coord not in cube_coords:` took 98.5% of execution time in the original (167ms), but the optimized version with `id(ref_coord) not in cube_coords_id_set` takes only 8.7% (254μs) - a ~650x improvement on this specific operation. The overall method speedup of 5758% demonstrates how a single algorithmic improvement can dramatically impact performance when the bottleneck is properly identified.

This optimization is particularly effective for test cases with many coordinates or multiple factory dependencies, where the quadratic behavior of the original code becomes pronounced.
@codeflash-ai codeflash-ai bot requested a review from mashraf-222 October 24, 2025 16:33
@codeflash-ai codeflash-ai bot added ⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: High Optimization Quality according to Codeflash labels Oct 24, 2025
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