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

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

.. _sphx_glr_gallery_benchmarks_analytical_grid_scaling.py:


Analytical wavenumber-grid cost
===============================

Time a fixed binary mixture on increasingly fine manual grids. Grid spacing
resolves at least 12 samples per longest-distance oscillation; decreasing the
real-space resolution increases the high-wavenumber cutoff. The median of
three runs is a timing measure, not an accuracy assessment. Report machine,
software versions and thread settings when comparing measurements.

.. GENERATED FROM PYTHON SOURCE LINES 11-45



.. image-sg:: /gallery/benchmarks/images/sphx_glr_analytical_grid_scaling_001.png
   :alt: Grid resolution and evaluation cost
   :srcset: /gallery/benchmarks/images/sphx_glr_analytical_grid_scaling_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    451 wavenumbers: median 0.0008 s
    901 wavenumbers: median 0.0016 s
    1351 wavenumbers: median 0.0023 s
    1801 wavenumbers: median 0.0031 s






|

.. code-block:: Python


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

    radii = np.array([0.1, 0.2]) * ureg.micrometer
    domain = analytical.PercusYevickDomain(
        size=100 * ureg.micrometer, radii=radii, volume_fraction=0.2, number_fractions=[0.5, 0.5]
    )
    distances = np.linspace(0, 1.5, 150) * ureg.micrometer
    counts, timings = [], []
    for divisor in (5, 10, 15, 20):
        grid = analytical.make_wavenumber_grid(radii.min() / divisor, distances.max(), 12)
        solver = analytical.PercusYevickSolver(
            densities=domain.particle_densities_per_radius, radii=radii, wavenumber=grid
        )
        runs = []
        for _ in range(3):
            start = perf_counter()
            solver.compute(distances)
            runs.append(perf_counter() - start)
        counts.append(len(grid))
        timings.append(np.median(runs))
        print(f"{len(grid)} wavenumbers: median {timings[-1]:.4f} s")
    figure, axis = plt.subplots()
    axis.plot(counts, timings, "o-")
    axis.set(
        xlabel="wavenumber points",
        ylabel="median wall time (s)",
        title="Grid resolution and evaluation cost",
    )
    axis.grid(alpha=0.2)
    figure.tight_layout()


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

   **Total running time of the script:** (0 minutes 0.081 seconds)


.. _sphx_glr_download_gallery_benchmarks_analytical_grid_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_grid_scaling.ipynb <analytical_grid_scaling.ipynb>`

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

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

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

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


.. only:: html

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

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