The TACO modules will be restructured to be fully pythonic. Please find the heritage bash modules here.
It is recommended to use git for downloading the TACO source code
git clone --recurse-submodules https://github.com/HITS-TOS/TACO.git
The dependency sloscillations is integrated as a git submodule and will be available using --recurse-submodules during git clone. If the flag was not used, it can be done afterwards with
git submodule update --init --recursive
Note: As long as the repository is private a personal access token is needed for the authentication.
Tests are implemented using pytest and can be executed with
python3 -m pytest
The Jupyterlab docker container provides a comfortable way to perform TACO modules and can by started with
docker build -t taco-jupyterlab -f .devcontainer/Dockerfile-jupyterlab .
docker run -it --rm -p 8888:8888 taco-jupyterlab
Open the printed URL in your browser to access Jupyterlab. The jupyter notebook work/pipeline.ipynb is a good starting point.
Basing on Miniconda, TACO can be installed with
conda env create
conda activate taco
Download and install the packages as per the requirements.txt file.
For processing a long list of stars the high-throughput pipeline is available. Before running the pipline, please execute
export PATH=$PWD/src:$PATH
export PYTHONPATH=$PWD/src:$PWD/libs/sloscillations:$PYTHONPATH
once from the TACO root directory. Then the high-troughput pipline can be started with
pipeline.py -i <input directory> -s <settings file>
taking every <name>.dat file in the input directory and write the results in a directory <name>.
A settings file with all entries is available at pipeline/pipeline_settings_full.yaml.
Tip
Copy the settings-file into a result directory and executing the pipline from there, leaves the run parameters documented.
TACO docker-jupyterlab was tested on:
- Linux (Ubuntu and CentOS)
- MacOS (M1-Chip) (please consider #25)
- Windows 11, Docker engine 4.15.0 using WSL 2
TACO conda high-throughput pipeline was tested on:
- Linux (CentOS)