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LAser pulse manipulation made eaSY

Project description



lasy is a Python library that facilitates the initialization of complex laser pulses, in simulations of laser-plasma interactions.

More specifically, lasy offers many ways to define complex laser pulses (e.g. from commonly-known analytical formulas, from experimental measurements, etc.) and offers pre-processing functionalities (e.g. propagation, re-normalization, geometry conversion). The laser field is then exported in a standardized file, that can be read by external simulation codes.


LASY manipulates laser pulses, and operates on the laser envelope. In 3D (x,y,t) Cartesian coordinates, the definition used is:

   E_x(x,y,t) = \mathrm{Re}\left( \mathcal{E}(x,y,t) e^{-i\omega_0t}p_x\right)\\
   E_y(x,y,t) = \mathrm{Re}\left( \mathcal{E}(x,y,t) e^{-i\omega_0t}p_y\right)\end{aligned}

where $\mathrm{Re}$ stands for real part, $E_x$ (resp. $E_y$) is the laser electric field in the x (resp. y) direction, $\mathcal{E}$ is the complex laser envelope stored and used in lasy, $\omega_0 = 2\pi c/\lambda_0$ is the angular frequency defined from the laser wavelength $\lambda_0$ and $(p_x,p_y)$ is the (complex and normalized) polarization vector.

In cylindrical coordinates, the envelope is decomposed in $N_m$ azimuthal modes ( see Ref. [A. Lifschitz et al., J. Comp. Phys. 228.5: 1803-1814 (2009)]). Each mode is stored on a 2D grid (r,t), using the following definition:

   E_x (r,\theta,t) = \mathrm{Re}\left( \sum_{-N_m+1}^{N_m-1}\mathcal{E}_m(r,t) e^{-im\theta}e^{-i\omega_0t}p_x\right)\\
   E_y (r,\theta,t) = \mathrm{Re}\left( \sum_{-N_m+1}^{N_m-1}\mathcal{E}_m(r,t) e^{-im\theta}e^{-i\omega_0t}p_y\right).\end{aligned}


How to contribute

All contributions are welcome! For a new contribution, we use pull requests from forks. Below is a very rough summary, please have a look at the appropriate documentation at and around.

First, setup your fork workflow (only once):

  • Fork the repo by clicking the Fork button on the top right, and follow the prompts. This will create your own (remote) copy of the main repo, located at[yourusername]/LASY.
  • Make your local copy aware of your fork: from your local repository, do git remote add [some-name][your username]/LASY. For [some-name] it can be convenient to use e.g. your username.

Then, for each contribution:

  • Get the last version of branch development from the main repo (e.g. git checkout development && git pull).
  • Create a new branch (e.g. git checkout -b my_contribution).
  • Do usual git add and git commit operations.
  • Push your branch to your own fork: git push -u [some-name] my_contribution
  • Whenever you're ready, open a PR from branch my_contribution on your fork to branch development on the main repo. Github typically suggests this very well.

Style conventions

  • Docstrings are written using the Numpy style.
  • Functions in utils/ only depend on standard types (Python & Numpy) and on the Grid class. That way, they are relatively stand-alone and can be used on different data structures. A simple Grid factory is provided for that purpose.
  • A PR should be open for any contribution: the description helps to explain the code and open dicussion.


python3 -m pip install lasy

For tests, you need to have a few extra packages, such as pytest and openpmd-viewer installed:

python3 -m pip install -r tests/requirements.txt


After successful installation, you can run the unit tests:

# Run all tests
python3 -m pytest tests/

# Run tests from a single file
python3 -m pytest tests/

# Run a single test (useful during debugging)
python3 -m pytest tests/

# Run all tests, do not capture "print" output and be verbose
python3 -m pytest -s -vvvv tests/

Creating Documentation

Install sphinx (

cd docs
python -m pip install -r requirements.txt
sphinx-build -b html source _build

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