The bfast package provides a highly-efficient parallel implementation for the Breaks For Additive Season and Trend (BFASTmonitor) proposed by Verbesselt et al. The implementation is based on OpenCL.
See the documentation for details and examples.
The bfast package has been tested under Python 3.*. The required Python dependencies are:
- numpy==1.16.3
- pandas==0.24.2
- pyopencl==2018.2.5
- scikit-learn==0.20.3
- scipy==1.2.1
- matplotlib==2.2.2
- wget==3.2
- Sphinx==2.2.0
- sphinx-bootstrap-theme==0.7.1
Further, OpenCL needs to be available.
The package can easily be installed via pip via:
pip install bfastTo install the package from the sources, first get the current stable release via:
git clone https://github.com/gieseke/bfast.gitAfterwards, on Linux systems, you can install the package locally for the current user via:
python setup.py install --userThe source code is published under the GNU General Public License (GPLv3). The authors are not responsible for any implications that stem from the use of this software.