⚡️ Speed up function is_tex_string by 84%#153
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The optimization achieves an **83% speedup** by **precompiling the regex pattern** at module load time instead of recompiling it on every function call. **Key optimization**: The original code compiled a new regex pattern (`re.compile`) each time `is_tex_string()` was called, which is expensive. The optimized version moves this compilation outside the function, storing the precompiled pattern in the module-level variable `_pat`. Now each function call only performs the fast pattern matching operation. **Performance impact**: Line profiler results show the dramatic improvement - the original version spent 73.3% of execution time (806μs out of 1100μs total) just compiling the regex pattern on each call. The optimized version eliminates this overhead entirely, reducing total execution time from 1100μs to 220μs. **Why this works**: Regex compilation involves parsing the pattern string, building a finite state machine, and optimizing it - operations that don't need to be repeated since the MathJax delimiter patterns are constants. Python's `re.compile` returns an optimized pattern object that can be reused indefinitely. **Test case benefits**: The optimization provides consistent speedups across all test scenarios: - **Small strings**: 90-230% faster (most common case) - **Large strings (1000+ chars)**: 10-25% faster (regex matching dominates over compilation) - **Edge cases**: 100-200% faster for invalid patterns that fail quickly This optimization is particularly valuable if `is_tex_string()` is called frequently in text processing pipelines, as the compilation overhead elimination scales linearly with call frequency.
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📄 84% (0.84x) speedup for
is_tex_stringinsrc/bokeh/embed/util.py⏱️ Runtime :
276 microseconds→150 microseconds(best of250runs)📝 Explanation and details
The optimization achieves an 83% speedup by precompiling the regex pattern at module load time instead of recompiling it on every function call.
Key optimization: The original code compiled a new regex pattern (
re.compile) each timeis_tex_string()was called, which is expensive. The optimized version moves this compilation outside the function, storing the precompiled pattern in the module-level variable_pat. Now each function call only performs the fast pattern matching operation.Performance impact: Line profiler results show the dramatic improvement - the original version spent 73.3% of execution time (806μs out of 1100μs total) just compiling the regex pattern on each call. The optimized version eliminates this overhead entirely, reducing total execution time from 1100μs to 220μs.
Why this works: Regex compilation involves parsing the pattern string, building a finite state machine, and optimizing it - operations that don't need to be repeated since the MathJax delimiter patterns are constants. Python's
re.compilereturns an optimized pattern object that can be reused indefinitely.Test case benefits: The optimization provides consistent speedups across all test scenarios:
This optimization is particularly valuable if
is_tex_string()is called frequently in text processing pipelines, as the compilation overhead elimination scales linearly with call frequency.✅ Correctness verification report:
⚙️ Existing Unit Tests and Runtime
unit/bokeh/embed/test_util__embed.py::Test__tex_helpers.test_is_tex_string🌀 Generated Regression Tests and Runtime
🔎 Concolic Coverage Tests and Runtime
codeflash_concolic_sstvtaha/tmpuf1h6l1m/test_concolic_coverage.py::test_is_tex_stringTo edit these changes
git checkout codeflash/optimize-is_tex_string-mhwtgnzaand push.