Skip to main content

Nonlinear transfer matrix method

Project description

PyPI version Build status Build Status

NonlinearTMM : Nonlinear transfer-matrix method

Overview

Transfer-matrix method (TMM) is powerful analytical method to solve Maxwell equations in layered structures. However, standard TMM is limited by infinite plane waves (e.g no Gaussian beam excitation) and it is only limited to linear processes (i.e calculation of second-harmonic, sum-frequency, difference-frequency generation is not possible). The aim of this package is extand standard TMM to include those features. The physics of those extensions are described in the follwoing publications, first extends the standard TMM to nonlinear processes and the second extends to the beams with arbritary profiles.

  1. A. Loot and V. Hizhnyakov, “Extension of standard transfer-matrix method for three-wave mixing for plasmonic structures,” Appl. Phys. A, vol. 123, no. 3, p. 152, 2017.
  2. A. Loot and V. Hizhnyakov, “Modeling of enhanced spontaneous parametric down-conversion in plasmonic and dielectric structures with realistic waves,” Journal of Optics, vol. 20, no. 055502, 2018.

For additional details see our documentation https://ardiloot.github.io/NonlinearTMM/. For getting started guide see Getting started.

Main features

In addition to the standard TMM features this package also supports:

  • Calculation of Gaussian beam (or any other beam) propagartion inside layered structures
  • Calculation of nonlinear processes SHG/SFG/DFG

Technical features

  • Written in C++
  • Python wrapper written in Cython
  • Parallerization through OpenMP
  • Use of SSE instructions for speedup

Documentation

https://ardiloot.github.io/NonlinearTMM/

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

NonlinearTMM-1.3.11.tar.gz (3.1 MB view hashes)

Uploaded Source

Built Distributions

NonlinearTMM-1.3.11-cp311-cp311-win_amd64.whl (154.3 kB view hashes)

Uploaded CPython 3.11 Windows x86-64

NonlinearTMM-1.3.11-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.2 MB view hashes)

Uploaded CPython 3.11 manylinux: glibc 2.17+ x86-64

NonlinearTMM-1.3.11-cp310-cp310-win_amd64.whl (155.7 kB view hashes)

Uploaded CPython 3.10 Windows x86-64

NonlinearTMM-1.3.11-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.2 MB view hashes)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64

NonlinearTMM-1.3.11-cp39-cp39-win_amd64.whl (156.5 kB view hashes)

Uploaded CPython 3.9 Windows x86-64

NonlinearTMM-1.3.11-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.2 MB view hashes)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64

NonlinearTMM-1.3.11-cp38-cp38-win_amd64.whl (156.9 kB view hashes)

Uploaded CPython 3.8 Windows x86-64

NonlinearTMM-1.3.11-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.2 MB view hashes)

Uploaded CPython 3.8 manylinux: glibc 2.17+ x86-64

NonlinearTMM-1.3.11-cp37-cp37m-win_amd64.whl (154.9 kB view hashes)

Uploaded CPython 3.7m Windows x86-64

NonlinearTMM-1.3.11-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.2 MB view hashes)

Uploaded CPython 3.7m manylinux: glibc 2.17+ x86-64

NonlinearTMM-1.3.11-cp36-cp36m-win_amd64.whl (174.9 kB view hashes)

Uploaded CPython 3.6m Windows x86-64

NonlinearTMM-1.3.11-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.1 MB view hashes)

Uploaded CPython 3.6m manylinux: glibc 2.17+ 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