⚡️ Speed up method _Distplot.make_normal by 34%
#81
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📄 34% (0.34x) speedup for
_Distplot.make_normalinplotly/figure_factory/_distplot.py⏱️ Runtime :
28.1 milliseconds→21.0 milliseconds(best of220runs)📝 Explanation and details
The optimized code achieves a 33% speedup by reducing attribute access overhead and optimizing mathematical computations.
Key optimizations:
Local variable caching: The optimized version pulls frequently accessed instance attributes (
self.histnorm,self.bin_size, etc.) into local variables at the start of the method. This eliminates repeated attribute lookups during loop execution, which is particularly beneficial since Python's attribute access has overhead.Function reference caching:
scipy_stats.norm.fitandscipy_stats.norm.pdfare cached as local variables (norm_fit,norm_pdf) to avoid repeated module attribute lookups in the tight loop.Optimized x-coordinate generation: Instead of the original list comprehension that repeatedly accessed
self.start[index]andself.end[index], the optimized version pre-computesstep = (e0 - s0) / 500and uses local variables, reducing arithmetic operations per iteration.Vectorized operations: The optimized code leverages NumPy's vectorized multiplication when
histnorm == ALTERNATIVE_HISTNORM, operating on the entire arrayy *= bin_size[index]instead of element-wise operations.Performance impact by test case:
The optimizations are particularly effective for the common use case of processing multiple statistical distributions, where the nested loops amplify the benefits of reduced attribute access overhead.
✅ Correctness verification report:
🌀 Generated Regression Tests and Runtime
🔎 Concolic Coverage Tests and Runtime
codeflash_concolic_grpsys06/tmpkm9rcoch/test_concolic_coverage.py::test__Distplot_make_normalTo edit these changes
git checkout codeflash/optimize-_Distplot.make_normal-mhg72arxand push.