.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "gallery/benchmarks/analytical_species_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_analytical_species_scaling.py>`
        to download the full example code.

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

.. _sphx_glr_gallery_benchmarks_analytical_species_scaling.py:


Percus--Yevick mixture-size scaling
====================================

Measure the cost of evaluating mixtures with different numbers of radius
classes on fixed radial and wavenumber grids. The physical size distribution
changes with the number of classes, so this benchmark measures computational
scaling rather than a numerical convergence sequence. The manual grid uses
12 samples per longest-distance oscillation and a 0.01 µm radial resolution;
verify convergence separately before using it in a scientific calculation.

The reported median of seven runs includes result construction. Timings depend
on hardware, compiler, package versions and thread settings.

.. GENERATED FROM PYTHON SOURCE LINES 15-62



.. image-sg:: /gallery/benchmarks/images/sphx_glr_analytical_species_scaling_001.png
   :alt: Percus--Yevick mixture-size scaling
   :srcset: /gallery/benchmarks/images/sphx_glr_analytical_species_scaling_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    1 classes, 901 wavenumbers: 0.0013 s
    2 classes, 901 wavenumbers: 0.0016 s
    4 classes, 901 wavenumbers: 0.0025 s
    8 classes, 901 wavenumbers: 0.0064 s






|

.. code-block:: Python

    from time import perf_counter

    import matplotlib.pyplot as plt
    import numpy as np

    from PackLab import analytical, ureg


    distances = np.linspace(0.0, 1.5, 150) * ureg.micrometer
    wavenumber = analytical.make_wavenumber_grid(
        radial_resolution=0.01 * ureg.micrometer,
        maximum_distance=distances.max(),
        samples_per_oscillation=12,
    )
    class_counts = (1, 2, 4, 8)
    medians = []
    for count in class_counts:
        radii = np.linspace(0.1, 0.2, count) * ureg.micrometer
        domain = analytical.PercusYevickDomain(
            size=100 * ureg.micrometer,
            radii=radii,
            volume_fraction=0.2,
            number_fractions=np.full(count, 1.0 / count),
        )
        solver = analytical.PercusYevickSolver(
            densities=domain.particle_densities_per_radius,
            radii=domain.radii,
            wavenumber=wavenumber,
        )
        solver.compute(distances)  # warm-up
        timings = []
        for _ in range(7):
            start = perf_counter()
            solver.compute(distances)
            timings.append(perf_counter() - start)
        medians.append(np.median(timings))
        print(f"{count} classes, {len(wavenumber)} wavenumbers: {medians[-1]:.4f} s")

    figure, axis = plt.subplots()
    axis.plot(class_counts, medians, "o-")
    axis.set(
        xlabel="radius classes",
        ylabel="median wall time (s)",
        title="Percus--Yevick mixture-size scaling",
    )
    axis.grid(alpha=0.2)
    figure.tight_layout()


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

   **Total running time of the script:** (0 minutes 0.151 seconds)


.. _sphx_glr_download_gallery_benchmarks_analytical_species_scaling.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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