pyLabDD

Python Laboratory for Dislocation Dynamics

• Author: Alexander Hartmaier
• Organization: ICAMS, Ruhr University Bochum, Germany
• Contact: alexander.hartmaier@rub.de

Dislocation Dynamics (DD) is a numerical method for studying the evolution of a population of discrete dislocations in an elastic medium under mechanical loads. The pyLabDD package introduces a simple version of Dislocation Dynamics in 2-dimensional space to study fundamental aspects of plastic deformation associated with the motion and mutual interaction of dislocations. Dislocations are considered as pure edge dislocations where the line direction is normal to the considered plane.

Installation

The preferred method to use pyLabDD is within Anaconda or Miniconda, into which it can be easily installed from conda-forge by

$ conda install conda-forge::pylabdd

or

$ conda install pylabdd -c conda-forge

Generally, it can be installed within any Python environment supporting the package installer for python pip from its latest PyPi image via pip

$ pip install pylabdd

Alternatively, the complete repository can be cloned and installed locally. It is recommended to create a conda environment before installation. This can be done by the following the command line instructions

$ git clone https://github.com/AHartmaier/pyLabDD.git ./pyLabDD
$ cd pyLabDD
$ conda env create -f environment.yml  
$ conda activate pylabdd
$ python -m pip install .

For this installation method, the correct implementation of the package can be tested with

$ pytest tests

After this, the package can be used within Python, e.g. be importing the entire package with

import pylabdd as dd

Speedup with Fortran subroutines

The subroutines to calculate the Peach-Koehler (PK) force on dislocations are rather time consuming. The Fortran implementation of these subroutines brings a considerable seepdup of the simulations compared with the pure Python version. After installation from conda-forge, these faster Fortran subroutines are automatically availble. Installations with pip or from source require a Fortran compiler, typically gfortran, within your active environment, i.e. you might need to install gfortran from conda or pip first.
The embedding of Fortran subroutines into Python is accomplished with the leightweight Fortran wrapper fmodpy. If the compliation of the Fortran subroutines should fail, you will receive a warning and the slower Python subroutines will be used as fallback option. In that case, please check if the gfortran compiler is available in your active environment. If problems still persist, please report them directly to the author of this packges or create an issue in the GitHub repo.

Jupyter notebooks

pyLabDD is conveniently used with Jupyter notebooks. Available notebooks with tutorials on the dislocation dynamics method and the Taylor hardening model are contained in the subfolder notebooks.

The Jupyter notebooks of the pyLabDD tutorials are also available on Binder

Contributions

Contributions to the pyLabDD 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.

Dependencies

pyLabDD requires the following packages as imports:

• NumPy for array handling
• MatPlotLib for graphical output
• fmodpy for embedding of faster Fortran subroutines for PK force calculation (optional)

Version history

• v 1.0: Initial version (with F90 subroutine)
• v 1.1: Pure Python version (with optional F90 subroutines)
• v 1.2: Automatic compilation of F90 subroutines with fallback to Python version

License

The pyLabDD 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 examples, notebooks and documentation are published under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)

© 2025 Alexander Hartmaier, ICAMS/Ruhr University Bochum, Germany