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

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

.. _sphx_glr_gallery_benchmarks_memory_scaling.py:


Memory scaling with sweep size
===============================

Estimate Python-managed peak memory for increasingly large parameter sweeps.
``tracemalloc`` does not include all native allocations made by the C++
extension; use an operating-system profiler for complete process memory.

.. GENERATED FROM PYTHON SOURCE LINES 9-57



.. image-sg:: /gallery/benchmarks/images/sphx_glr_memory_scaling_001.png
   :alt: PyMieSim memory scaling
   :srcset: /gallery/benchmarks/images/sphx_glr_memory_scaling_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

      10 diameters | 0.010 MiB peak Python memory
      25 diameters | 0.011 MiB peak Python memory
      50 diameters | 0.012 MiB peak Python memory
     100 diameters | 0.015 MiB peak Python memory
     200 diameters | 0.018 MiB peak Python memory
     400 diameters | 0.025 MiB peak Python memory






|

.. code-block:: Python


    import gc
    import tracemalloc

    import matplotlib.pyplot as plt
    import numpy as np

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


    def peak_memory(number_of_diameters: int) -> float:
        gc.collect()
        tracemalloc.start()

        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(scatterer_set=scatterer, source_set=source).get("Qsca").as_numpy()

        _, peak_bytes = tracemalloc.get_traced_memory()
        tracemalloc.stop()
        return peak_bytes / 1024**2


    sweep_sizes = [10, 25, 50, 100, 200, 400]
    peak_memory_mib = [peak_memory(size) for size in sweep_sizes]

    for size, memory in zip(sweep_sizes, peak_memory_mib):
        print(f"{size:4d} diameters | {memory:.3f} MiB peak Python memory")

    figure, axis = plt.subplots()
    axis.plot(sweep_sizes, peak_memory_mib, marker="o", color="tab:orange")
    axis.set(
        xlabel="Number of diameters",
        ylabel="Peak Python memory [MiB]",
        title="PyMieSim memory 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 0.560 seconds)


.. _sphx_glr_download_gallery_benchmarks_memory_scaling.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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