CTLearn is a package under active development to run deep learning models to analyze data from all major current and future arrays of imaging atmospheric Cherenkov telescopes (IACTs). CTLearn can load DL1 data from CTA (Cherenkov Telescope Array), FACT, H.E.S.S., MAGIC, and VERITAS telescopes processed by ctapipe or DL1DataHandler.
- Code, feature requests, bug reports, pull requests: https://github.com/ctlearn-project/ctlearn
- Documentation: https://ctlearn.readthedocs.io
- License: BSD-3
Download and install Anaconda, or, for a minimal installation, Miniconda.
The following command will set up a conda virtual environment, add the necessary package channels, and install CTLearn specified version and its dependencies:
CTLEARN_VER=0.9.0
wget https://raw.githubusercontent.com/ctlearn-project/ctlearn/v$CTLEARN_VER/environment.yml
conda env create -n [ENVIRONMENT_NAME] -f environment.yml
conda activate [ENVIRONMENT_NAME]
pip install ctlearn==$CTLEARN_VER
ctlearn -hThis should automatically install all dependencies (NOTE: this may take some time, as by default MKL is included as a dependency of NumPy and it is very large). If you are working on the IT-cluster, please use 'environment-cluster.yml' instead of 'environment.yml' in the instructions above.
See the documentation for further information like installation instructions for developers, package usage, and dependencies among other topics.
Please cite the corresponding version using the DOIs below if this software package is used to produce results for any publication:
- 0.8.0 :
|
|
|
|
|---|---|---|---|
| Ari Brill | Bryan Kim | Tjark Miener | Daniel Nieto |
|
|
|---|---|
| Qi Feng | Ruben Lopez-Coto |
|
|
|
|
|
|
|---|---|---|---|---|---|
| Jaime Sevilla | Héctor Rueda | Juan Redondo Pizarro | Luca Romanato | Sahil Yadav | Sergio García Heredia |