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PyMieSim

PyMieSim is an open-source Python package for fast and flexible Mie scattering simulations. It supports spherical, cylindrical and core–shell particles and provides helper classes for custom sources and detectors. The project targets both quick single-scatterer studies and large parametric experiments.

Try the live web GUI: PyMieSim Parameter Sweep Lab.

Features

  • Solvers for spheres, cylinders and core–shell geometries.

  • Built-in models for plane wave and Gaussian sources.

  • Multiple detector types including photodiodes and coherent modes.

  • Simple data analysis with pandas DataFrame outputs.

Installation

PyMieSim is available on PyPI and Anaconda. Install it with:

pip install PyMieSim
conda install PyMieSim  --channels MartinPdeS

See the online documentation for detailed usage and additional examples.

Quick example

Below is a short example computing the scattering efficiency of a sphere.

from PyMieSim.experiment.scatterer_set import SphereSet
from PyMieSim.experiment.source_set import GaussianSet
from PyMieSim.experiment.polarization_set import PolarizationSet
from PyMieSim.experiment import Setup
from PyMieSim.units import ureg
import numpy as np

polarization = PolarizationSet(
    angles=[0] * ureg.degree
)

source = GaussianSet(
    wavelength=np.linspace(400, 2000, 500) * ureg.nanometer,
    polarization=polarization,
    optical_power=1e-3 * ureg.watt,
    numerical_aperture=0.2 * ureg.AU,
)

scatterer = SphereSet(
    diameter=[200, 300] * ureg.nanometer,
    material=[4 + 1j] * ureg.RIU,
    medium=[1] * ureg.RIU,
)

experiment = Setup(
    scatterer_set=scatterer,
    source_set=source
)
df = experiment.get("Qsca", "Qext")
df.plot(x="source:wavelength")
Scattering efficiency of a 200 nm sphere with refractive index 4.0.

Code structure

Here is the architecture for a standard workflow using PyMieSim:

Code structure of a standard workflow using PyMieSim.

Building from source

For development or manual compilation, clone the repository and run:

git submodule update --init
mkdir build && cd build
cmake ../ -G"Unix Makefiles"
sudo make install
cd ..
python -m pip install .

Testing

Run the unit tests with:

pip install PyMieSim[testing]
pytest

Citing PyMieSim

If you use PyMieSim in academic work, please cite:

@article{PoinsinetdeSivry-Houle:23,
    author = {Martin Poinsinet de Sivry-Houle and Nicolas Godbout and Caroline Boudoux},
    journal = {Opt. Continuum},
    title = {PyMieSim: an open-source library for fast and flexible far-field Mie scattering simulations},
    volume = {2},
    number = {3},
    pages = {520--534},
    year = {2023},
    doi = {10.1364/OPTCON.473102},
}

Contact

For questions or contributions, contact martin.poinsinet.de.sivry@gmail.com.

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (5.2.0)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for MartinPdeS from gravatar.com MartinPdeS

Credits

PyPI data
Data sourced directly from PyPI's database.

Author: Martin Poinsinet de Sivry-Houle

License

MIT License (MIT License)

Requires

Python >=3.10

Provides Extra

testing documentation dev

Tags

mie scattering backscatter sphere cylinder nanoparticle phase function efficiency rayleigh backscattering

Classifiers

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