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tutorial.ipynb

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"Once `miniconda` is installed run this command in your terminal:\n",
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"\n",
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"```bash\n",
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"conda env create -f environment.yml\n",
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"conda env create -f environment.yaml\n",
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"```\n",
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"This should create a virtual environment named `malapa-cheetah-tutorial-2025` and install the necessary packages inside.\n",
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"<h2 style=\"color: #b51f2a\">Some achievements</h2>\n",
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"\n",
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"To be filled @Jan\n",
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"### Timeline\n",
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"\n",
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"- When was it first launched\n",
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"- How many downloads up to date\n",
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"- 2024 PRAB highlight paper\n"
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"- In development since March 2021 (initially as _\"JOSS 🤯\"_, renamed to _Cheetah_ in July 2021)\n",
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"- First publicly released version 0.5.12 in June 2022\n",
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"- First fully differentiable release 0.6 in Septemeber 2023\n",
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"- Merged with Bmad-X beginning March 2024\n",
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"- Fully vectorised release 0.7 Decmeber 2024\n",
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"\n",
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"### Cheetah in numbers\n",
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"\n",
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"- Over 22,300 downloads as off April 2025 (just from PyPI)\n",
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"- Currently about 1,500 downloads per month (just from PyPI)\n",
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"\n",
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"### Papers\n",
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"\n",
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"- 2022 first paper on Cheetah developments at IPAC'22: [Accelerating Linear Beam Dynamics Simulations for Machine Learning Applications](https://accelconf.web.cern.ch/ipac2022/doi/JACoW-IPAC2022-WEPOMS036.html)\n",
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"- 2024 main Cheetah paper in PRAB: [Bridging the Gap Between Machine Learning and Particle Accelerator Physics with High-Speed, Differentiable Simulations](https://doi.org/10.1103/PhysRevAccelBeams.27.054601)\n",
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"- 2024 paper on advancements in differentiable simulations at LINAC2024: [Advancements in Backwards Differentiable Beam Dynamics Simulations for Accelerator Design, Model Calibration, and Machine Learning](https://meow.elettra.eu/71/doi/jacow-linac2024-thpb068/index.html)\n"
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{

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