===============
Benchmarking
===============

This document outlines the current approach to benchmarking within PMMoTo using the ``pytest_benchmark`` plugin. Please note that this is a work in progress, and improvements are ongoing.

Overview
--------

Benchmarking is essential for evaluating the performance of the PMMoTo framework. The ``pytest_benchmark`` plugin provides a simple way to measure the performance of your code.


Usage
-----

To run benchmarks, you can create benchmark tests in your test files. Here is a basic example:

.. code-block:: python

    import pytest

    @pytest.mark.benchmark
    def test_my_function(benchmark):
        # ...existing code...
        result = benchmark(my_function, *args, **kwargs)
        # ...existing code...

Running Benchmarks
-------------------

To execute your benchmarks, run the following command:

.. code-block:: shell

    pytest benchmark/

Interpreting Results
---------------------

The results will include various metrics such as:

- **Mean**: The average time taken for the benchmarked function.
- **Standard Deviation**: The variability of the benchmark results.
- **Minimum and Maximum**: The fastest and slowest recorded times.

Future Improvements
--------------------

As this benchmarking framework is still under development, future enhancements may include:

- More detailed reporting options.
- Integration with continuous integration tools for automated performance testing.
- Additional benchmarks for various components of PMMoTo.

Conclusion
----------

Benchmarking is a critical aspect of maintaining performance standards in PMMoTo. We encourage contributions and feedback as we continue to refine this process.