| Name | PyMieSim JSON |
| Version |
3.1.1.0
JSON |
| download |
| home_page | None |
| Summary | A package for light scattering computation. |
| upload_time | 2024-12-16 11:42:59 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | MIT License Copyright (c) 2020 Martin Poinsinet de Sivry-Houle Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
mie
scattering
backscatter
sphere
cylinder
nanoparticle
phase function
efficiency
rayleigh
backscattering
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
|logo|
.. list-table::
:widths: 10 25 25 25
:header-rows: 0
* - Meta
- |python|
- |docs|
- |zenodo|
* - Testing
- |ci/cd|
- |coverage|
- |colab|
* - PyPi
- |PyPi|
- |PyPi_download|
-
* - Anaconda
- |anaconda|
- |anaconda_download|
- |anaconda_date|
PyMieSim
========
**PyMieSim** is a Python library designed to provide a robust and flexible framework for performing Mie scattering simulations.
The software is easy to install and operate, making it accessible to both new users and experienced researchers.
PyMieSim enables users to explore the scattering properties of particles under various configurations, and is tailored for investigating single scattering events, as well as conducting large-scale parametric experiments.
At its core, PyMieSim includes three solvers optimized for different types of scatterers:
- **Spherical particles**
- **Infinite cylindrical particles**
- **Core-shell spherical particles**
The software also allows the user to customize the light source and detector attributes, depending on the specific simulation needs. The package is modular and provides an intuitive interface for users to model complex scattering scenarios with minimal effort.
|code_structure|
Main Submodules
---------------
PyMieSim is organized into two primary submodules:
1. **single**: Focused on analyzing individual scattering events, such as:
- Far-field distributions
- Scattering phase functions
- Stokes parameters
2. **experiment**: Designed for exploring how scattering parameters, such as `Qsca`, `Qext`, `g`, and `coupling (power)`, behave over large datasets, incorporating variations in sources, scatterers, and detectors.
Both submodules work seamlessly together, making PyMieSim adaptable for a wide range of scattering simulations.
----
Getting Started
---------------
To use PyMieSim in Python, simply install the package and begin incorporating it into your scripts.
Installation
************
PyMieSim supports Windows, Linux, macOS (including Apple M1/M2 chips), and ARM architectures. To install the package, use pip:
.. code-block:: bash
pip install PyMieSim (using pip package manager)
conda install PyMieSim (using conda environment manager)
For more details, visit the `documentation <https://pymiesim.readthedocs.io/en/latest/>`_ for a comprehensive guide on how to use the package.
----
Example Code
------------
Here is an example of how to use PyMieSim for a simple Mie scattering simulation. This example demonstrates how to configure a light source, scatterer, and detector, and retrieve the scattering data:
.. code-block:: python
import numpy as np
from PyMieSim.experiment.scatterer import Sphere
from PyMieSim.experiment.source import Gaussian
from PyMieSim.experiment import Setup
from PyMieSim.units import nanometer, degree, watt, AU, RIU
source = Gaussian(
wavelength=np.linspace(400, 1000, 500) * nanometer,
polarization=0 * degree,
optical_power=1e-3 * watt,
NA=0.2 * AU
)
scatterer = Sphere(
diameter=[200] * nanometer,
property=[4] * RIU,
medium_property=1 * RIU,
source=source
)
experiment = Setup(scatterer=scatterer, source=source)
dataframe = experiment.get('Qsca')
dataframe.plot_data(x="source:wavelength")
It produces the following figure which is equivalent to the one found on `wikipedia <https://en.wikipedia.org/wiki/Mie_scattering#/media/File:N4wiki.svg>`_.
|wikipedia_example|
This is just one example of PyMieSim in action. You can find more examples in the
`examples section <https://pymiesim.readthedocs.io/en/master/gallery/index.html>`_ of the documentation.
----
Examples
--------
Here are a few more examples showcasing the capabilities of PyMieSim:
Example 1: Plasmonic Resonances for CoreShell Particles
*******************************************************
|example_plasmon|
Example 2: Scattering Efficiency vs Diameter for Spherical Particles
********************************************************************
|example_qsca|
----
Manual Building
---------------
If you prefer or need to build the project manually (e.g., for Apple silicon devices), ensure you have a C++ compiler (such as gcc) and Fortran installed, as well as Python 3.7+.
Build Instructions
******************
Linux/MacOS
~~~~~~~~~~~
.. code-block:: bash
git clone https://github.com/MartinPdeS/PyMieSim.git
cd PyMieSim
git submodule init && git submodule update
mkdir build
cd build
cmake ../ -G"Unix Makefiles"
sudo make install
cd ..
python -m pip install .
For Windows, use `MinGW Makefiles` instead of `Unix Makefiles` when invoking CMake.
----
Testing
-------
You can test the local version of PyMieSim by running the following commands:
.. code-block:: bash
git clone https://github.com/MartinPdeS/PyMieSim.git
cd PyMieSim
pip install PyMieSim[testing]
pytest
This will run the suite of unit tests and provide coverage details.
----
Google Colab
------------
In 2024, running code on your local machine is optional! You can leverage the power of Google Colab to run PyMieSim remotely. Use the provided
`Colab notebook <https://colab.research.google.com/github/MartinPdeS/PyMieSim/blob/master/notebook.ipynb>`_ for an interactive experience.
|colab|
----
Citing PyMieSim
---------------
If PyMieSim contributes to your research, we kindly ask that you cite the following paper:
.. code-block:: none
@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},
}
You can access the full article `here <https://opg.optica.org/optcon/fulltext.cfm?uri=optcon-2-3-520&id=526697>`_
----
Experimental Graphical User Interface (GUI)
-------------------------------------------
Since version 1.7.0, PyMieSim offers an experimental GUI for users who prefer a graphical approach to simulations. While still under development, the GUI can be installed and accessed as follows:
.. code-block:: bash
pip install PyMieSim
python -m PyMieSim
The GUI is not yet as robust as the core Python API, but it provides a simplified interface for generating simulations.
|example_gui|
----
Contact Information
-------------------
PyMieSim is actively developed and maintained by Martin Poinsinet de Sivry-Houle. If you're interested in contributing or have questions, feel free to reach out.
Email: `martin.poinsinet.de.sivry@gmail.ca <mailto:martin.poinsinet.de.sivry@gmail.ca?subject=PyMieSim>`_
Flag_0
----
.. |logo| image:: https://github.com/MartinPdeS/PyMieSim/raw/master/docs/images/logo.png
:alt: PyOptik logo
.. |python| image:: https://img.shields.io/pypi/pyversions/pymiesim.svg
:alt: Python
:target: https://www.python.org/
.. |zenodo| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.5593704.svg
:alt: Scientific article
:target: https://doi.org/10.5281/zenodo.4556074
.. |colab| image:: https://colab.research.google.com/assets/colab-badge.svg
:alt: Google Colab
:target: https://colab.research.google.com/github/MartinPdeS/PyMieSim/blob/master/notebook.ipynb
.. |docs| image:: https://github.com/martinpdes/pymiesim/actions/workflows/deploy_documentation.yml/badge.svg
:target: https://martinpdes.github.io/PyMieSim/
:alt: Documentation Status
.. |PyPi| image:: https://badge.fury.io/py/PyMieSim.svg
:alt: PyPi version
:target: https://badge.fury.io/py/PyMieSim
.. |PyPi_download| image:: https://img.shields.io/pypi/dm/PyMieSim?style=plastic&label=PyPi%20downloads&labelColor=hex&color=hex
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.. |coverage| image:: https://raw.githubusercontent.com/MartinPdeS/PyMieSim/python-coverage-comment-action-data/badge.svg
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.. |code_structure| image:: https://github.com/MartinPdeS/PyMieSim/raw/master/docs/images/code_structure.png
:width: 800
:alt: Structure of the library
.. |example_gui| image:: https://github.com/MartinPdeS/PyMieSim/raw/master/docs/images/example_gui.png
:width: 800
:alt: Structure of the library
.. |wikipedia_example| image:: https://github.com/MartinPdeS/PyMieSim/raw/master/docs/images/wikipedia_example.png
:width: 800
:alt: Example wikipedia
.. |example_plasmon| image:: https://github.com/MartinPdeS/PyMieSim/raw/master/docs/images/plasmonic_resonances.png
:width: 800
:alt: Plasmonic resonances
.. |example_qsca| image:: https://github.com/MartinPdeS/PyMieSim/raw/master/docs/images/Qsca_diameter.png
:width: 800
:alt: Qsca vs diameter
.. |anaconda| image:: https://anaconda.org/martinpdes/pymiesim/badges/version.svg
:alt: Anaconda version
:target: https://anaconda.org/martinpdes/pymiesim
.. |anaconda_download| image:: https://anaconda.org/martinpdes/pymiesim/badges/downloads.svg
:alt: Anaconda downloads
:target: https://anaconda.org/martinpdes/pymiesim
.. |anaconda_date| image:: https://anaconda.org/martinpdes/pymiesim/badges/latest_release_relative_date.svg
:alt: Latest release date
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"description": "|logo|\n\n.. list-table::\n :widths: 10 25 25 25\n :header-rows: 0\n\n * - Meta\n - |python|\n - |docs|\n - |zenodo|\n * - Testing\n - |ci/cd|\n - |coverage|\n - |colab|\n * - PyPi\n - |PyPi|\n - |PyPi_download|\n -\n * - Anaconda\n - |anaconda|\n - |anaconda_download|\n - |anaconda_date|\n\n\n\n\nPyMieSim\n========\n\n**PyMieSim** is a Python library designed to provide a robust and flexible framework for performing Mie scattering simulations.\nThe software is easy to install and operate, making it accessible to both new users and experienced researchers.\nPyMieSim enables users to explore the scattering properties of particles under various configurations, and is tailored for investigating single scattering events, as well as conducting large-scale parametric experiments.\n\nAt its core, PyMieSim includes three solvers optimized for different types of scatterers:\n\n- **Spherical particles**\n- **Infinite cylindrical particles**\n- **Core-shell spherical particles**\n\nThe software also allows the user to customize the light source and detector attributes, depending on the specific simulation needs. The package is modular and provides an intuitive interface for users to model complex scattering scenarios with minimal effort.\n\n|code_structure|\n\nMain Submodules\n---------------\n\nPyMieSim is organized into two primary submodules:\n\n1. **single**: Focused on analyzing individual scattering events, such as:\n - Far-field distributions\n - Scattering phase functions\n - Stokes parameters\n\n2. **experiment**: Designed for exploring how scattering parameters, such as `Qsca`, `Qext`, `g`, and `coupling (power)`, behave over large datasets, incorporating variations in sources, scatterers, and detectors.\n\nBoth submodules work seamlessly together, making PyMieSim adaptable for a wide range of scattering simulations.\n\n\n----\n\nGetting Started\n---------------\n\nTo use PyMieSim in Python, simply install the package and begin incorporating it into your scripts.\n\nInstallation\n************\n\nPyMieSim supports Windows, Linux, macOS (including Apple M1/M2 chips), and ARM architectures. To install the package, use pip:\n\n.. code-block:: bash\n\n pip install PyMieSim (using pip package manager)\n\n conda install PyMieSim (using conda environment manager)\n\nFor more details, visit the `documentation <https://pymiesim.readthedocs.io/en/latest/>`_ for a comprehensive guide on how to use the package.\n\n----\n\nExample Code\n------------\n\nHere is an example of how to use PyMieSim for a simple Mie scattering simulation. This example demonstrates how to configure a light source, scatterer, and detector, and retrieve the scattering data:\n\n.. code-block:: python\n\n import numpy as np\n\n from PyMieSim.experiment.scatterer import Sphere\n from PyMieSim.experiment.source import Gaussian\n from PyMieSim.experiment import Setup\n from PyMieSim.units import nanometer, degree, watt, AU, RIU\n\n source = Gaussian(\n wavelength=np.linspace(400, 1000, 500) * nanometer,\n polarization=0 * degree,\n optical_power=1e-3 * watt,\n NA=0.2 * AU\n )\n\n scatterer = Sphere(\n diameter=[200] * nanometer,\n property=[4] * RIU,\n medium_property=1 * RIU,\n source=source\n )\n\n experiment = Setup(scatterer=scatterer, source=source)\n\n dataframe = experiment.get('Qsca')\n\n dataframe.plot_data(x=\"source:wavelength\")\n\n\nIt produces the following figure which is equivalent to the one found on `wikipedia <https://en.wikipedia.org/wiki/Mie_scattering#/media/File:N4wiki.svg>`_.\n\n|wikipedia_example|\n\n\nThis is just one example of PyMieSim in action. You can find more examples in the\n`examples section <https://pymiesim.readthedocs.io/en/master/gallery/index.html>`_ of the documentation.\n\n----\n\nExamples\n--------\n\nHere are a few more examples showcasing the capabilities of PyMieSim:\n\nExample 1: Plasmonic Resonances for CoreShell Particles\n*******************************************************\n\n|example_plasmon|\n\nExample 2: Scattering Efficiency vs Diameter for Spherical Particles\n********************************************************************\n\n|example_qsca|\n\n----\n\nManual Building\n---------------\n\nIf you prefer or need to build the project manually (e.g., for Apple silicon devices), ensure you have a C++ compiler (such as gcc) and Fortran installed, as well as Python 3.7+.\n\nBuild Instructions\n******************\n\nLinux/MacOS\n~~~~~~~~~~~\n\n.. code-block:: bash\n\n git clone https://github.com/MartinPdeS/PyMieSim.git\n cd PyMieSim\n git submodule init && git submodule update\n mkdir build\n cd build\n cmake ../ -G\"Unix Makefiles\"\n sudo make install\n cd ..\n python -m pip install .\n\nFor Windows, use `MinGW Makefiles` instead of `Unix Makefiles` when invoking CMake.\n\n----\n\nTesting\n-------\n\nYou can test the local version of PyMieSim by running the following commands:\n\n.. code-block:: bash\n\n git clone https://github.com/MartinPdeS/PyMieSim.git\n cd PyMieSim\n pip install PyMieSim[testing]\n pytest\n\nThis will run the suite of unit tests and provide coverage details.\n\n----\n\nGoogle Colab\n------------\n\nIn 2024, running code on your local machine is optional! You can leverage the power of Google Colab to run PyMieSim remotely. Use the provided\n`Colab notebook <https://colab.research.google.com/github/MartinPdeS/PyMieSim/blob/master/notebook.ipynb>`_ for an interactive experience.\n\n|colab|\n\n----\n\nCiting PyMieSim\n---------------\n\nIf PyMieSim contributes to your research, we kindly ask that you cite the following paper:\n\n.. code-block:: none\n\n @article{PoinsinetdeSivry-Houle:23,\n author = {Martin Poinsinet de Sivry-Houle and Nicolas Godbout and Caroline Boudoux},\n journal = {Opt. Continuum},\n title = {PyMieSim: an open-source library for fast and flexible far-field Mie scattering simulations},\n volume = {2},\n number = {3},\n pages = {520--534},\n year = {2023},\n doi = {10.1364/OPTCON.473102},\n }\n\nYou can access the full article `here <https://opg.optica.org/optcon/fulltext.cfm?uri=optcon-2-3-520&id=526697>`_\n\n----\n\nExperimental Graphical User Interface (GUI)\n-------------------------------------------\n\nSince version 1.7.0, PyMieSim offers an experimental GUI for users who prefer a graphical approach to simulations. While still under development, the GUI can be installed and accessed as follows:\n\n.. code-block:: bash\n\n pip install PyMieSim\n python -m PyMieSim\n\nThe GUI is not yet as robust as the core Python API, but it provides a simplified interface for generating simulations.\n\n|example_gui|\n\n----\n\nContact Information\n-------------------\n\nPyMieSim is actively developed and maintained by Martin Poinsinet de Sivry-Houle. If you're interested in contributing or have questions, feel free to reach out.\n\nEmail: `martin.poinsinet.de.sivry@gmail.ca <mailto:martin.poinsinet.de.sivry@gmail.ca?subject=PyMieSim>`_\n\nFlag_0\n\n----\n\n.. |logo| image:: https://github.com/MartinPdeS/PyMieSim/raw/master/docs/images/logo.png\n :alt: PyOptik logo\n\n.. |python| image:: https://img.shields.io/pypi/pyversions/pymiesim.svg\n :alt: Python\n :target: https://www.python.org/\n\n.. |zenodo| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.5593704.svg\n :alt: Scientific article\n :target: https://doi.org/10.5281/zenodo.4556074\n\n.. |colab| image:: https://colab.research.google.com/assets/colab-badge.svg\n :alt: Google Colab\n :target: https://colab.research.google.com/github/MartinPdeS/PyMieSim/blob/master/notebook.ipynb\n\n.. |docs| image:: https://github.com/martinpdes/pymiesim/actions/workflows/deploy_documentation.yml/badge.svg\n :target: https://martinpdes.github.io/PyMieSim/\n :alt: Documentation Status\n\n.. |PyPi| image:: https://badge.fury.io/py/PyMieSim.svg\n :alt: PyPi version\n :target: https://badge.fury.io/py/PyMieSim\n\n.. |PyPi_download| image:: https://img.shields.io/pypi/dm/PyMieSim?style=plastic&label=PyPi%20downloads&labelColor=hex&color=hex\n :alt: PyPI - Downloads\n :target: https://pypistats.org/packages/pymiesim\n\n.. |coverage| image:: https://raw.githubusercontent.com/MartinPdeS/PyMieSim/python-coverage-comment-action-data/badge.svg\n :alt: Unittest coverage\n :target: https://htmlpreview.github.io/?https://github.com/MartinPdeS/PyMieSim/blob/python-coverage-comment-action-data/htmlcov/index.html\n\n.. |ci/cd| image:: https://github.com/martinpdes/pymiesim/actions/workflows/deploy_coverage.yml/badge.svg\n :alt: Unittest Status\n\n.. |code_structure| image:: https://github.com/MartinPdeS/PyMieSim/raw/master/docs/images/code_structure.png\n :width: 800\n :alt: Structure of the library\n\n.. |example_gui| image:: https://github.com/MartinPdeS/PyMieSim/raw/master/docs/images/example_gui.png\n :width: 800\n :alt: Structure of the library\n\n.. |wikipedia_example| image:: https://github.com/MartinPdeS/PyMieSim/raw/master/docs/images/wikipedia_example.png\n :width: 800\n :alt: Example wikipedia\n\n.. |example_plasmon| image:: https://github.com/MartinPdeS/PyMieSim/raw/master/docs/images/plasmonic_resonances.png\n :width: 800\n :alt: Plasmonic resonances\n\n.. |example_qsca| image:: https://github.com/MartinPdeS/PyMieSim/raw/master/docs/images/Qsca_diameter.png\n :width: 800\n :alt: Qsca vs diameter\n\n.. |anaconda| image:: https://anaconda.org/martinpdes/pymiesim/badges/version.svg\n :alt: Anaconda version\n :target: https://anaconda.org/martinpdes/pymiesim\n\n.. |anaconda_download| image:: https://anaconda.org/martinpdes/pymiesim/badges/downloads.svg\n :alt: Anaconda downloads\n :target: https://anaconda.org/martinpdes/pymiesim\n\n.. |anaconda_date| image:: https://anaconda.org/martinpdes/pymiesim/badges/latest_release_relative_date.svg\n :alt: Latest release date\n :target: https://anaconda.org/martinpdes/pymiesim",
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