ACTS Common Tracking Software

or A Common Tracking Software if you do not like recursive acronyms

ACTS is an experiment-independent toolkit for (charged) particle track reconstruction in (high energy) physics experiments implemented in modern C++.

More information can be found in the ACTS documentation.

Installation

The easiest way to get the ACTS Python bindings is via PyPI:

pip install pyacts

The pyacts distribution includes bindings for the core library, Fatras, and the Examples framework.

Warning The PyPI package does not include all optional plugins (e.g. DD4hep/ODD, Geant4, ROOT). To use those, build from source with the relevant CMake options enabled.

Note Even though the name of the PyPI package is pyacts, the python module must be imported as import acts.

The wheel includes .pyi type stubs and supports Linux and macOS with Python 3.11 or newer.

For a source build, enable the Python bindings and Examples framework, then load the generated <build>/this_acts_withdeps.sh script before importing acts:

cmake -B <build> -S <source> -DACTS_BUILD_PYTHON_BINDINGS=ON -DACTS_BUILD_EXAMPLES=ON
cmake --build <build> --target ActsPythonBindings
source <build>/this_acts_withdeps.sh

Enable optional components with their CMake options. For ROOT readers, writers, and evaluation, use -DACTS_BUILD_EXAMPLES_ROOT=ON; this also enables the ROOT plugin. A source build needs Python development headers. The ActsPythonBindings target builds the enabled bindings, and the setup script adds the built modules and dependencies to your environment.

Getting started

An acts.examples.Sequencer runs readers, algorithms, and writers over events. This first script generates muons and prints them; it works with the PyPI wheel:

import acts
from acts import UnitConstants as u
import acts.examples
from acts.examples.simulation import (
    EtaConfig,
    MomentumConfig,
    ParticleConfig,
    addParticleGun,
)

s = acts.examples.Sequencer(events=5, numThreads=1)
addParticleGun(
    s,
    momentumConfig=MomentumConfig(1 * u.GeV, 10 * u.GeV, transverse=True),
    etaConfig=EtaConfig(-2.0, 2.0),
    particleConfig=ParticleConfig(1, acts.PdgParticle.eMuon),
    rnd=acts.examples.RandomNumbers(seed=42),
    printParticles=True,
)
s.run()

For convenience, the package contains helpers in acts.examples.simulation and acts.examples.reconstruction that add entire workflow steps with addXYZ-functions (such as simulation, seeding, ...) and connect their data keys. For full flexibility, the underlying sequencer algorithms can be directly configured as well. For simple but complete chains, see the PyPI finding and fitting demo or the truth-tracking Kalman example (source build). Their helper signatures and default keys are in simulation.py and reconstruction.py.