Skip to content
No description, website, or topics provided.
Jupyter Notebook Other
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Type Name Latest commit message Commit time
Failed to load latest commit information.

Jupyter/IPython Beam Simulation Container (radiasoft/beamsim-jupyter)

For a description of the majority of particle accelerator and FEL beam simulation codes, please see radiasoft/container-beamsim.

Additional codes and major libraries:

  • FBPIC (Fourier-Bessel Particle-In-Cell)is a Particle-In-Cell (PIC) code for relativistic plasma physics. It is especially well-suited for physical simulations of laser-wakefield acceleration and plasma-wakefield acceleration.
  • GPy is a Gaussian Process (GP) framework written in python, from the Sheffield machine learning group.
  • Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation.
  • scikit-learn provides a range of supervised and unsupervised learning algorithms via a consistent interface in Python.
  • SciPy stack is a Python-based ecosystem of open-source software for mathematics, science, and engineering. In particular, these are some of the core packages: NumPy, SciPy library, Matplotlib, IPython, Sympy, Pandas.
  • Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.
  • Tensorflow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.
  • TeX Live includes all the major TeX-related programs, macro packages, and fonts that are free software, including support for many languages around the world.
  • YT supports structured, variable-resolution meshes, unstructured meshes, and discrete or sampled data such as particles. Focused on driving physically-meaningful inquiry, yt has been applied in domains such as astrophysics, seismology, nuclear engineering, molecular dynamics, and oceanography.
You can’t perform that action at this time.