Technical guide¶
Installation¶
The pyLabFEA package requires an Anaconda or Miniconda environment with a recent Python version. It can be installed directly from its GitHub repository with the following command
$ python -m pip install git+https://github.com/AHartmaier/pyLabFEA.git
Alternatively, the repository can be cloned locally and installed via
$ git clone https://github.com/AHartmaier/pyLabFEA.git
$ cd pyLabFEA.git/trunk/
$ conda env create -f environment.yml
$ conda activate pylabfea$ python -m pip install . --user
The correct implementation can be tested with
$ pytest tests -v
After this, the package can by imported into python scripts with
import pylabfea as FE
Documentation¶
Online documentations for pyLabFEA is found under https://ahartmaier.github.io/pyLabFEA, for offline use open pyLabFEA/doc/index.html to browse through the contents. The documentation is generated using Sphinx.
Contributions¶
Contributions to the pyLabFEA package are highly welcome, either in form of new notebooks with application examples or tutorials, or in form of new functionalities to the Python code. Furthermore, bug reports or any comments on possible improvements of the code or its documentation are greatly appreciated.
The latest version of the pyLabFEA package can be found on GitHub: https://github.com/AHartmaier/pyLabFEA.git
Dependencies¶
pyLabFEA requires the following packages as imports:
NumPy for array handling
Scipy for numerical solutions
scikit-learn for machine learning algorithms
MatPlotLib for graphical output
pandas for data import
fireworks direct import of data resulting from fireworks workflows
License¶
The pyLabFEA package comes with ABSOLUTELY NO WARRANTY. This is free software, and you are welcome to redistribute it under the conditions of the GNU General Public License (GPLv3)
The contents of the examples and notebooks are published under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)