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

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

.. _sphx_glr_gallery_benchmarks_sweep_runtime.py:


Runtime scaling with sweep size
================================

Measure how long a ``Qsca`` calculation takes as the number of sphere
diameters increases.

.. GENERATED FROM PYTHON SOURCE LINES 8-54



.. image-sg:: /gallery/benchmarks/images/sphx_glr_sweep_runtime_001.png
   :alt: PyMieSim runtime scaling
   :srcset: /gallery/benchmarks/images/sphx_glr_sweep_runtime_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

      10 diameters | 0.0005 s | 10 results
      25 diameters | 0.0006 s | 25 results
      50 diameters | 0.0009 s | 50 results
     100 diameters | 0.0015 s | 100 results
     200 diameters | 0.0026 s | 200 results
     400 diameters | 0.0050 s | 400 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_sweep(number_of_diameters: 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, number_of_diameters) * ureg.nanometer,
            material=[1.5],
            medium=[1.0],
        )
        experiment = Experiment(scatterer_set=scatterer, source_set=source)

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


    sweep_sizes = [10, 25, 50, 100, 200, 400]
    measurements = [run_sweep(size) for size in sweep_sizes]
    runtime_seconds, result_sizes = np.asarray(measurements).T

    for size, runtime, result_size in zip(sweep_sizes, runtime_seconds, result_sizes):
        print(f"{size:4d} diameters | {runtime:.4f} s | {int(result_size)} results")

    figure, axis = plt.subplots()
    axis.plot(sweep_sizes, runtime_seconds, marker="o")
    axis.set(
        xlabel="Number of diameters",
        ylabel="Runtime [s]",
        title="PyMieSim runtime scaling",
    )
    axis.grid(True, alpha=0.3)
    figure.tight_layout()
    plt.show()


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

   **Total running time of the script:** (0 minutes 0.228 seconds)


.. _sphx_glr_download_gallery_benchmarks_sweep_runtime.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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