Skip to main content

"Periodic T-matrix scattering algorithms"

Project description

treams

The package treams provides a framework to simplify computations of the electromagnetic scattering of waves at finite and at periodic arrangements of particles based on the T-matrix method.

Installation

Installation using pip

To install the package with pip, use

pip install git+https://github.com/tfp-photonics/treams.git

If you're using the system wide installed version of python, you might consider the --user option.

Running on Windows

For Windows, there are currently two tested ways how to install treams. The first option is using the Windows Subsystem for Linux (WSL). Within WSL treams can be installed just as described above. The second option, that was tested is using MSYS2 with the mingw64 environment. Likely, other python versions based on mingw-w64 might also work.

Documentation

The documentation can be found at https://tfp-photonics.github.io/treams.

Publications

When using this code please cite:

D. Beutel, A. Groner, C. Rockstuhl, C. Rockstuhl, and I. Fernandez-Corbaton, Efficient Simulation of Biperiodic, Layered Structures Based on the T-Matrix Method, J. Opt. Soc. Am. B, JOSAB 38, 1782 (2021).

Other relevant publications are

Features

  • T-matrix calculations using a spherical or cylindrical wave basis set
  • Calculations in helicity and parity (TE/TM) basis
  • Scattering from clusters of particles
  • Scattering from particles and clusters arranged in 3d-, 2d-, and 1d-lattices
  • Calculation of light propagation in stratified media
  • Band calculation in crystal structures

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

treams-0.3.0.tar.gz (1.5 MB view hashes)

Uploaded Source

Built Distributions

treams-0.3.0-cp311-cp311-win_amd64.whl (1.4 MB view hashes)

Uploaded CPython 3.11 Windows x86-64

treams-0.3.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_28_x86_64.whl (5.0 MB view hashes)

Uploaded CPython 3.11 manylinux: glibc 2.17+ x86-64 manylinux: glibc 2.28+ x86-64

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page