Low-level data processing pipeline software for CTA (the Cherenkov Telescope Array)

This is code is a prototype data processing framework and is under rapid development. It is not recommended for production use unless you are an expert or developer!

• Code: https://github.com/cta-observatory/ctapipe
• Docs: https://ctapipe.readthedocs.io/
• Slack: Contact Karl Kosack for invite

If you use this software for a publication, please cite the Zenodo Record for the specific version you are using and our latest publication.

You can find all ctapipe Zenodo records here: List of ctapipe Records on Zenodo.

There is also a Zenodo DOI always pointing to the latest version:

At this point, our latest publication is the 2021 ICRS proceeding, which you can cite using this bibtex entry:

@inproceedings{ctapipe-icrc-2021,
    author = {Nöthe, Maximilian  and  Kosack, Karl  and  Nickel, Lukas  and  Peresano, Michele},
    title = {Prototype Open Event Reconstruction Pipeline for the Cherenkov Telescope Array},
    doi = {10.22323/1.395.0744},
    booktitle = {Proceedings, 37th International Cosmic Ray Conference},
    year=2021,
    volume={395},
    number={744},
    location={Berlin, Germany},
  }

ctapipe and its dependencies may be installed using the Anaconda or Miniconda package system. We recommend creating a conda virtual environment first, to isolate the installed version and dependencies from your master environment (this is optional).

The following command will set up a conda virtual environment, add the necessary package channels, and install ctapipe specified version and its dependencies:

CTAPIPE_VER=0.17.0
wget https://raw.githubusercontent.com/cta-observatory/ctapipe/v$CTAPIPE_VER/environment.yml
conda env create -n cta -f environment.yml
conda activate cta
conda install -c conda-forge ctapipe=$CTAPIPE_VER

Note: this environment contains many useful packages that are not strictly requirements of ctapipe. To get only ctapipe and its direct dependencies, just do conda install -c conda-forge ctapipe[=<version>] in an environment of your choice.

Note: If you encounter long Solving environment times with conda, try using mamba (https://github.com/mamba-org/mamba) instead.

The file environment.yml can be found in this repo. Note this is pre-alpha software and is not yet stable enough for end-users (expect large API changes until the first stable 1.0 release).

Developers should follow the development install instructions found in the documentation.