.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "gallery/benchmarks/metropolis_sweep_scaling.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_gallery_benchmarks_metropolis_sweep_scaling.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_gallery_benchmarks_metropolis_sweep_scaling.py:


Metropolis sampling cost per sweep
==================================

Measure the cost of advancing the same initial periodic configuration for
several sweep counts. Each measurement starts a new sampler with the same
seed; the reported median of three runs includes sampler construction.
These short timings measure work, not equilibration or statistical accuracy.
Report hardware, package version, and thread settings with results.

.. GENERATED FROM PYTHON SOURCE LINES 11-47



.. image-sg:: /gallery/benchmarks/images/sphx_glr_metropolis_sweep_scaling_001.png
   :alt: Metropolis sampling cost
   :srcset: /gallery/benchmarks/images/sphx_glr_metropolis_sweep_scaling_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    20 sweeps, 191 spheres: median 0.0169 s
    50 sweeps, 191 spheres: median 0.0419 s
    100 sweeps, 191 spheres: median 0.0835 s
    200 sweeps, 191 spheres: median 0.1690 s






|

.. code-block:: Python


    from time import perf_counter
    import matplotlib.pyplot as plt
    import numpy as np
    from PackLab import monte_carlo, samplers, ureg

    domain = monte_carlo.PackingDomain(
        3 * ureg.micrometer, 3 * ureg.micrometer, 3 * ureg.micrometer, use_periodic_boundaries=True
    )
    rsa = monte_carlo.RSAOptions()
    rsa.random_seed = 42
    rsa.target_packing_fraction = 0.1
    rsa.maximum_attempts = 100000
    initial = monte_carlo.RSASimulator(
        domain, samplers.ConstantRadiusSampler(0.15 * ureg.micrometer), rsa
    ).run()
    counts = [20, 50, 100, 200]
    timings = []
    for sweeps in counts:
        runs = []
        for _ in range(3):
            options = monte_carlo.MetropolisOptions()
            options.random_seed = 43
            options.number_of_sweeps = sweeps
            options.maximum_displacement = 0.05 * ureg.micrometer
            start = perf_counter()
            simulator = monte_carlo.MetropolisSimulator(domain, initial.sphere_configuration, options)
            simulator.run()
            runs.append(perf_counter() - start)
        timings.append(np.median(runs))
        print(f"{sweeps} sweeps, {initial.statistics.sphere_count} spheres: median {timings[-1]:.4f} s")
    figure, axis = plt.subplots()
    axis.plot(counts, timings, "o-")
    axis.set(xlabel="sweeps", ylabel="median wall time (s)", title="Metropolis sampling cost")
    axis.grid(alpha=0.2)
    figure.tight_layout()


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.999 seconds)


.. _sphx_glr_download_gallery_benchmarks_metropolis_sweep_scaling.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: metropolis_sweep_scaling.ipynb <metropolis_sweep_scaling.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: metropolis_sweep_scaling.py <metropolis_sweep_scaling.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: metropolis_sweep_scaling.zip <metropolis_sweep_scaling.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_