cheetah-accelerator 0.6.0

Fast particle accelerator optics simulation for reinforcement learning and optimisation applications.

Cheetah

Cheetah is a particle tracking accelerator we built specifically to speed up the training of reinforcement learning models.

Installation

Simply install Cheetah from PyPI by running the following command.

pip install cheetah-accelerator

How To Use

A sequence of accelerator elements (or a lattice) is called a Segment in Cheetah. You can create a Segment as follows

segment = Segment(
    elements=[
        BPM(name="BPM1SMATCH"),
        Drift(length=torch.tensor(1.0)),
        BPM(name="BPM6SMATCH"),
        Drift(length=torch.tensor(1.0)),
        VerticalCorrector(length=torch.tensor(0.3), name="V7SMATCH"),
        Drift(length=torch.tensor(0.2)),
        HorizontalCorrector(length=torch.tensor(0.3), name="H10SMATCH"),
        Drift(length=torch.tensor(7.0)),
        HorizontalCorrector(length=torch.tensor(0.3), name="H12SMATCH"),
        Drift(length=torch.tensor(0.05)),
        BPM(name="BPM13SMATCH"),
    ]
)

Alternatively you can create a segment from an Ocelot cell by running

segment = Segment.from_ocelot(cell)

All elements can be accesses as a property of the segment via their name. The strength of a quadrupole named AREAMQZM2 for example, may be set by running

segment.AREAMQZM2.k1 = torch.tensor(4.2)

In order to track a beam through the segment, simply call the segment like so

outgoing_beam = segment.track(incoming_beam)

You can choose to track either a beam defined by its parameters (fast) or by its particles (precise). Cheetah defines two different beam classes for this purpose and beams may be created by

beam1 = ParameterBeam.from_parameters()
beam2 = ParticleBeam.from_parameters()

It is also possible to load beams from Ocelot ParticleArray or Astra particle distribution files for both types of beam

ocelot_beam = ParticleBeam.from_ocelot(parray)
astra_beam = ParticleBeam.from_astra(filepath)

You may plot a segment with reference particle traces bay calling

segment.plot_overview(beam=beam)

where the optional keyword argument beam is the incoming beam represented by the reference particles. Cheetah will use a default incoming beam, if no beam is passed.

Cite Cheetah

To cite Cheetah in publications:

@inproceedings{stein2022accelerating,
    title        = {Accelerating Linear Beam Dynamics Simulations for Machine Learning Applications},
    author       = {Stein, Oliver and Kaiser, Jan and Eichler, Annika},
    year         = 2022,
    booktitle    = {Proceedings of the 13th International Particle Accelerator Conference},
    url          = {https://github.com/desy-ml/cheetah}
}

For Developers

Activate your virtual environment. (Optional)

Install the cheetah package as editable

pip install -e .

We suggest installing pre-commit hooks to automatically conform with the code formatting in commits:

pip install pre-commit
pre-commit install