Installation#

pyAT is a Python interface to Accelerator Toolbox. It uses the ‘pass methods’ defined in Accelerator Toolbox, implemented by compiling the C code used in the AT ‘integrators’ into a Python extension. These pass methods are used by higher-level functions to provide physics results.

Requirements#

pyAT supports Python 3.7 to 3.12.

Installation#

From PyPI#

PyPI maintains PyAT versions for Linux, MacOS and Windows, and python versions:

CPython 3.7, 3.8, 3.9, 3.10, 3.11, 3.12
PyPI does not support PyPy, because of the missing numpy and scipy libraries

Installation with minimal dependencies:#

$ pip install accelerator-toolbox

This minimal installation disables all plotting functions. Note that the plotting functions can be enabled later by simply installing matplotlib.

Standard installation:#

$ pip install "accelerator-toolbox[plot]"

This installs in addition the matplotlib package and its dependencies, which enables all plotting functions.

Optional dependencies:#

In addition to [plot], other modifiers are available for specific purposes:

"accelerator-toolbox[dev]": Installs the test framework: pytest, pytest-cov and flake8. This allows to run locally the test sequence executed on GitHub.
"accelerator-toolbox[doc]": Installs Sphinx and its utilities, allowing the generate locally the HTML documentation.

These modifiers may be combined. Example: pip install "accelerator-toolbox[plot,dev]"

From conda-forge#

conda-forge maintains PyAT versions for Linux, MacOS and Windows, and python versions:

CPython 3.8, 3.9, 3.10, 3.11, 3.12
PyPy 3.9

PyAT can be installed in a conda environment with:

$ conda install -c conda-forge accelerator-toolbox

From source#

Install git on your computer.

Download the latest version of AT:

$ git clone https://github.com/atcollab/at.git

Go to the pyAT installation directory:
```
$ cd at
```
Build and install:
```
$ pip install .
```

Parallel computation#

PyAT can be compiled for parallel processing. See Parallel processing

Usage#

Example:

>>> import at
>>> ring = at.Lattice.load('machine_data/hmba.mat')
>>> print(at.radiation_parameters(ring))
          Frac. tunes: [0.2099983  0.34001317 0.00349013]
                Tunes: [76.2099983  27.34001317]
       Chromaticities: [5.73409894 3.91761206]
Momentum compact. factor: 8.506669e-05
          Slip factor: -8.505944e-05
               Energy: 6.000000e+09 eV
   Energy loss / turn: 2.526189e+06 eV
Radiation integrals - I1: 0.07179435013387388 m
                   I2: 0.13844595446798158 m^-1
                   I3: 0.003357584058614851 m^-2
                   I4: -0.07375725030666251 m^-1
                   I5: 5.281495714523264e-07 m^-1
      Mode emittances: [1.3148797e-10           nan           nan]
Damping partition numbers: [1.53275121 1.         1.46724879]
        Damping times: [0.00872477 0.0133729  0.00911427] s
        Energy spread: 0.000934463
         Bunch length: 0.0030591 m
     Cavities voltage: 6000000.0 V
    Synchrotron phase: 2.70701 rd
Synchrotron frequency: 1239.74 Hz

For more examples of how to use pyAT, see pyat_examples.rst.