⚡️ Speed up method Cube.cell_measure_dims by 49%
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The optimization adds a fast-path lookup in `cell_measure_dims` when the input is already a `CellMeasure` object. Instead of always calling `self.cell_measure()` first (which performs a slower search through all cell measures), the optimized version directly searches `_cell_measures_and_dims` using identity comparison (`is`) when given a `CellMeasure` object. **Key changes:** - Added an `isinstance(cell_measure, CellMeasure)` check at the start of `cell_measure_dims` - Used `next()` with a generator expression for efficient single-match lookup: `next((dims for cm_, dims in self._cell_measures_and_dims if cm_ is cell_measure), None)` - Only falls back to the original `self.cell_measure()` call when the fast path fails **Why this is faster:** The original code always called `self.cell_measures()` which searches through all cell measures using equality comparison. The optimized version uses identity comparison (`is`) which is much faster in Python - it's just a pointer comparison rather than potentially expensive metadata equality checks. The `next()` function also short-circuits on the first match instead of building a full list. **Performance characteristics:** Based on the profiler results, this optimization is particularly effective when `cell_measure_dims` is called with `CellMeasure` objects (the common case), reducing execution time from ~1.1ms to ~0.4ms (63% improvement). The fast path handles 103 out of 106 calls in the test cases, demonstrating its effectiveness for the typical usage pattern where existing `CellMeasure` objects are passed to determine their dimensions.
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📄 49% (0.49x) speedup for
Cube.cell_measure_dimsinlib/iris/cube.py⏱️ Runtime :
251 microseconds→169 microseconds(best of6runs)📝 Explanation and details
The optimization adds a fast-path lookup in
cell_measure_dimswhen the input is already aCellMeasureobject. Instead of always callingself.cell_measure()first (which performs a slower search through all cell measures), the optimized version directly searches_cell_measures_and_dimsusing identity comparison (is) when given aCellMeasureobject.Key changes:
isinstance(cell_measure, CellMeasure)check at the start ofcell_measure_dimsnext()with a generator expression for efficient single-match lookup:next((dims for cm_, dims in self._cell_measures_and_dims if cm_ is cell_measure), None)self.cell_measure()call when the fast path failsWhy this is faster:
The original code always called
self.cell_measures()which searches through all cell measures using equality comparison. The optimized version uses identity comparison (is) which is much faster in Python - it's just a pointer comparison rather than potentially expensive metadata equality checks. Thenext()function also short-circuits on the first match instead of building a full list.Performance characteristics:
Based on the profiler results, this optimization is particularly effective when
cell_measure_dimsis called withCellMeasureobjects (the common case), reducing execution time from ~1.1ms to ~0.4ms (63% improvement). The fast path handles 103 out of 106 calls in the test cases, demonstrating its effectiveness for the typical usage pattern where existingCellMeasureobjects are passed to determine their dimensions.✅ Correctness verification report:
⚙️ Existing Unit Tests and Runtime
unit/cube/test_Cube.py::TestCellMeasures.test_cell_measure_dimsunit/cube/test_Cube.py::TestCellMeasures.test_cell_measure_dims_by_nameunit/cube/test_Cube.py::TestCellMeasures.test_fail_cell_measure_dimsunit/cube/test_Cube.py::TestCellMeasures.test_fail_cell_measure_dims_by_name⏪ Replay Tests and Runtime
test_pytest_libiristestsintegrationtest_netcdf__loadsaveattrs_py_libiristestsunitlazy_datatest_non_lazy_p__replay_test_0.py::test_iris_cube_Cube_cell_measure_dimsTo edit these changes
git checkout codeflash/optimize-Cube.cell_measure_dims-mh53jxgtand push.