.cursorrules

# Python Performance Testing Repository Rules

## Project Overview

This repository is for testing Python performance between versions 3.13, 3.14, and 3.14 threadfree. Focus on creating comprehensive benchmarks and performance analysis tools.

## Code Style & Standards

- Follow PEP 8 Python style guidelines
- Use type hints for all function parameters and return values
- Write comprehensive docstrings for all functions and classes
- Use meaningful variable and function names
- Prefer f-strings over .format() or % formatting
- Use pathlib for file path operations
- Handle exceptions explicitly with try/except blocks

## Performance Testing Guidelines

- Create separate test modules for each Python version comparison
- Use appropriate benchmarking libraries (timeit, cProfile, memory_profiler)
- Include both CPU-bound and I/O-bound performance tests
- Test various data structures and algorithms
- Measure execution time, memory usage, and CPU utilization
- Generate detailed performance reports with visualizations

## File Organization

- Create separate directories for each Python version's tests
- Use descriptive filenames that indicate the test purpose
- Group related tests in modules
- Create a main runner script to execute all benchmarks
- Include configuration files for test parameters

## Dependencies & Environment

- Use virtual environments for each Python version
- Create requirements.txt for each version
- Document Python version requirements clearly
- Use poetry or pip-tools for dependency management
- Include environment setup scripts

## Testing & Validation

- Write unit tests for all benchmark functions
- Validate results across different Python versions
- Include statistical analysis of performance differences
- Create regression tests to detect performance changes
- Use pytest for test framework

## Documentation

- Document benchmark methodology and assumptions
- Include performance analysis and conclusions
- Create visualizations (charts, graphs) for results
- Write clear setup and execution instructions
- Document any system requirements or limitations

## Performance Considerations

- Minimize overhead in benchmark code
- Use appropriate sample sizes for statistical significance
- Account for system variability in measurements
- Include warmup runs before actual measurements
- Consider garbage collection impact on results

## Output & Reporting

- Generate structured output (JSON, CSV) for results
- Create human-readable summary reports
- Include performance comparison tables
- Generate visual charts and graphs
- Export results for further analysis