This is a python tool for decomposing and model reduction for multiple transport phenomena. It is written in python3.
#. For a easy installation of the sPOD library use conda with the following command:
conda env create -f sPOD-env.yml
This will create an environment called sPOD in which you can run all the examples. For activating the environment use:
conda activate sPOD
before executing the examples in the example folder.
#. In order to run sPOD package, the following libraries are required:
- Numpy
- Matplotlib
- Scikit-learn
- SciPy
#. The documentation generator relies on Sphinx. The latter generator can be installed, for instance, using pip with the following command
python3 -m pip install sphinx pydata-sphinx-theme
Clone the repository and use it in your Python code
import sPOD_tools
or use instead
from sPOD_tools import sPOD
The documentation can be generated by running the Makefile in the folder
doc/.
#. For example, the following command generates the documentation in HTML format
make html
#. To read the documentation, open the file build/html.index.html in your
favorite browser.
For simple examples, you can check out the Python scripts in the example/
folder. To download the wildland fire and two cylinders test case from 
make download
After you can run the individual examples by executing them using Python or using the command:
make
to run all the examples in the folder. The synthetic_examples_1D.py implements the basic functionality and is a good introduction to understanding the implementation.
BE AWARE THAT THIS CODE IS STILL UNDER DEVELOPMENT. FUTURE DEVS WILL INCREASE PERFORMANCE AND USABILITY