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        "description": "# Fourier-Bessel Particle-In-Cell code (FBPIC)\n\n[![Build Status main](https://img.shields.io/travis/fbpic/fbpic/main.svg?label=main)](https://travis-ci.com/fbpic/fbpic/branches)\n[![Build Status dev](https://img.shields.io/travis/fbpic/fbpic/dev.svg?label=dev)](https://travis-ci.com/fbpic/fbpic/branches)\n[![pypi version](https://img.shields.io/pypi/v/fbpic.svg)](https://pypi.python.org/pypi/fbpic)\n[![License](https://img.shields.io/pypi/l/fbpic.svg)](LICENSE.txt)\n[![DOI](https://zenodo.org/badge/69215997.svg)](https://zenodo.org/badge/latestdoi/69215997)\n\nOnline documentation: [http://fbpic.github.io](http://fbpic.github.io)<br/>\nSupport: [Join slack](https://slack-fbpic.herokuapp.com)\n\n## Overview\n\nFBPIC is a\n[Particle-In-Cell (PIC) code](https://en.wikipedia.org/wiki/Particle-in-cell)\nfor relativistic plasma physics.  \n\nIt is especially well-suited for physical simulations of\n**laser-wakefield acceleration** and **plasma-wakefield acceleration**, with close-to-cylindrical symmetry.\n\n### Algorithm\n\nThe distinctive feature of FBPIC is to use\na **spectral decomposition in\ncylindrical geometry** (Fourier-Bessel\ndecomposition) for the fields. This combines the advantages of **spectral 3D** PIC codes (high accuracy and stability) and\nthose of **finite-difference cylindrical** PIC codes\n(orders-of-magnitude speedup when compared to 3D simulations).  \nFor more details on the algorithm, its advantages and limitations, see\nthe [documentation](http://fbpic.github.io).\n\n### Language and hardware\n\nFBPIC is written entirely in Python, but uses\n[Numba](http://numba.pydata.org/) Just-In-Time compiler for high\nperformance. In addition, the code can run on **CPU** (with multi-threading)\nand on **GPU**. For large simulations, running the\ncode on GPU can be much faster than on CPU.\n\n### Advanced features of laser-plasma acceleration\n\nFBPIC implements several useful features for laser-plasma acceleration, including:\n- Moving window\n- Cylindrical geometry (with azimuthal mode decomposition)\n- Calculation of space-charge fields at the beginning of the simulation\n- Intrinsic mitigation of Numerical Cherenkov Radiation (NCR) from relativistic bunches\n- Field ionization module (ADK model)\n\nIn addition, FBPIC supports the **boosted-frame** technique (which can\ndramatically speed up simulations), and includes:\n- Utilities to convert input parameters from the lab frame to the boosted frame\n- On-the-fly conversion of simulation results from the boosted frame back to the lab frame\n- Suppression of the Numerical Cherenkov Instability (NCI) using the Galilean technique\n\n## Installation\n\nThe installation instructions below are for a local computer. For more\ndetails, or for instructions specific to a particular HPC cluster, see\nthe [documentation](http://fbpic.github.io).\n\nThe recommended installation is through the\n[Anaconda](https://www.continuum.io/why-anaconda) distribution.\nIf Anaconda is not your default Python installation, download and install\nit from [here](https://www.continuum.io/downloads).\n\n**Installation steps**:\n\n- Install the dependencies of FBPIC. This can be done in two lines:\n```\nconda install numba scipy h5py mkl\nconda install -c conda-forge mpi4py\n```\n- Download and install FBPIC:\n```\npip install fbpic\n```\n(If you want to run FBPIC through the [PICMI](https://picmi-standard.github.io/)\ninterface, you can instead use `pip install fbpic[picmi]`.)\n\n- **Optional:** in order to run on GPU, install the additional package\n`cudatoolkit` and `cupy` -- e.g. using CUDA version 10.0.\n```\nconda install cudatoolkit=10.0\npip install cupy-cuda100\n```\n(In the above command, you should choose a CUDA version that is compatible with your GPU driver ; see [this table](https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html#major-components__table-cuda-toolkit-driver-versions) for more info.)\n\n- **Optional:** in order to run on a CPU which is **not** an Intel model, you\nneed to install `pyfftw`, in order to replace the MKL FFT:\n```\nconda install -c conda-forge pyfftw\n```\n\n## Running simulations\n\nOnce installed, FBPIC is available as a **Python module** on your\nsystem.\n\nTherefore, in order to run a physical simulation, you will need a **Python\nscript** that imports FBPIC's functionalities and use them to setup the\nsimulation. You can find examples of such scripts in the\n[documentation](http://fbpic.github.io) or in this repository, in `docs/source/example_input/`.\n\nOnce your script is ready, the simulation is run simply by typing:\n```\npython fbpic_script.py\n```\nThe code outputs HDF5 files, that comply with the\n[OpenPMD standard](http://www.openpmd.org/#/start),\n and which can thus be read as such (e.g. by using the\n [openPMD-viewer](https://github.com/openPMD/openPMD-viewer)).\n\n## Contributing\n\nWe welcome contributions to the code! Please read [this page](https://github.com/fbpic/fbpic/blob/main/CONTRIBUTING.md) for guidelines on how to contribute.\n\n## Research & Attribution\n\nFBPIC was originally developed by Remi Lehe at [Berkeley Lab](http://www.lbl.gov/),\nand Manuel Kirchen at\n[CFEL, Hamburg University](http://lux.cfel.de/). The code also\nbenefitted from the contributions of Soeren Jalas (CFEL), Kevin Peters (CFEL),\nIrene Dornmair (CFEL), Laurids Jeppe (CFEL), Igor Andriyash (Laboratoire d'Optique Appliquee),\nOmri Seemann (Weizmann Institute), Daniel Seipt (University of Michigan),\nSamuel Yoffe (University of Strathclyde) and David Grote (LLNL and LBNL).\n\nFBPIC's algorithms are documented in following scientific publications:\n\n* General description of FBPIC and it's algorithm (original paper): [R. Lehe et al., CPC, 2016](http://www.sciencedirect.com/science/article/pii/S0010465516300224) ([arXiv](https://arxiv.org/abs/1507.04790))\n* Boosted-frame technique with Galilean algorithm: [M. Kirchen et al., PoP, 2016](https://aip.scitation.org/doi/10.1063/1.4964770) ([arXiv](https://arxiv.org/abs/1608.00215)) and [Lehe et al., PRE, 2016](https://journals.aps.org/pre/abstract/10.1103/PhysRevE.94.053305) ([arXiv](https://arxiv.org/abs/1608.00227))\n* Parallel finite-order solver for multi-CPU/GPU simulations: [S. Jalas et al., PoP, 2017](https://aip.scitation.org/doi/abs/10.1063/1.4978569) ([arXiv](https://arxiv.org/abs/1611.05712))\n* Parallel finite-order boosted-frame simulations for multi-CPU/GPU simulations: [M. Kirchen et al., PRE, 2020](https://journals.aps.org/pre/abstract/10.1103/PhysRevE.102.013202)\n\nIf you use FBPIC for your research project: that's great! We are\nvery pleased that the code is useful to you!\n\nIf your project even leads to a scientific publication, please consider citing at least FBPIC's original paper. If your project uses the more advanced algorithms, please consider citing the respective publications in addition.\n\n\n",
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