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

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

.. _sphx_glr_gallery_benchmarks_parameter_grid.py:


Parameter-grid scaling
=======================

Measure the effect of expanding two sweep dimensions: particle diameter and
material refractive index.

.. GENERATED FROM PYTHON SOURCE LINES 8-57



.. image-sg:: /gallery/benchmarks/images/sphx_glr_parameter_grid_001.png
   :alt: PyMieSim parameter-grid scaling
   :srcset: /gallery/benchmarks/images/sphx_glr_parameter_grid_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

       4 combinations | 0.0005 s | 4 results
      16 combinations | 0.0006 s | 16 results
      64 combinations | 0.0012 s | 64 results
     144 combinations | 0.0020 s | 144 results






|

.. code-block:: Python


    import time

    import matplotlib.pyplot as plt
    import numpy as np

    from PyMieSim import Experiment, GaussianSet, PolarizationSet, SphereSet, ureg


    def run_grid(size: int) -> tuple[float, int]:
        source = GaussianSet(
            wavelength=[600] * ureg.nanometer,
            polarization=PolarizationSet(angles=[0] * ureg.degree),
            optical_power=[1e-3] * ureg.watt,
            numerical_aperture=[0.2],
        )
        scatterer = SphereSet(
            diameter=np.linspace(100, 1000, size) * ureg.nanometer,
            material=np.linspace(1.3, 1.8, size),
            medium=[1.0],
        )
        experiment = Experiment(scatterer_set=scatterer, source_set=source)

        start = time.perf_counter()
        result = experiment.get("Qsca").as_numpy()
        return time.perf_counter() - start, int(np.asarray(result).size)


    grid_sizes = [2, 4, 8, 12]
    measurements = [run_grid(size) for size in grid_sizes]
    runtime_seconds, result_sizes = np.asarray(measurements).T
    configuration_counts = np.square(grid_sizes)

    figure, axis = plt.subplots()
    axis.plot(configuration_counts, runtime_seconds, marker="o")
    axis.set(
        xlabel="Number of parameter combinations",
        ylabel="Runtime [s]",
        title="PyMieSim parameter-grid scaling",
    )
    axis.grid(True, alpha=0.3)
    figure.tight_layout()

    for combinations, runtime, results in zip(
        configuration_counts, runtime_seconds, result_sizes
    ):
        print(f"{combinations:4d} combinations | {runtime:.4f} s | {int(results)} results")

    plt.show()


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

   **Total running time of the script:** (0 minutes 0.225 seconds)


.. _sphx_glr_download_gallery_benchmarks_parameter_grid.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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