BoltzTraP2 is a modern implementation of the smoothed Fourier interpolation algorithm for electronic bands that formed the base of the original and widely used BoltzTraP code. One of the most typical uses of BoltzTraP is the calculation of thermoelectric transport coefficients as functions of temperature and chemical potential in the rigid-band picture. However, many other features are available, including 3D plots of Fermi surfaces based on the reconstructed bands. For more information, check out the BoltzTraP2 tutorial.
BoltzTraP2 is a Python module, with a small performance-critical portion written in C++ and Cython. BoltzTraP2’s runtime requirements are Python version 3.5 or higher, and the Python libraries NumPy, SciPy, matplotlib, spglib, NetCDF4 and ASE. All of them can be easily obtained from the Python Package Index (PyPI), using tools such as pip. They may also be bundled with Python distributions aimed at scientists, like Anaconda, and with a number of Linux distributions. If pip is used to install BoltzTraP2, dependencies should be resolved automatically.
If available, BoltzTraP2 will also make use of pyFFTW (for faster Fourier transforms), colorama (to colorize some console output) and VTK (to generate 3D representations). Those packages are not required, but they are recommended to be able to access the full functionality of BoltzTraP2.
Furthermore, compiling BoltzTraP2 from its sources requires a C++ compiler, and the development headers and libraries for Python. Cython is not required for a regular compilation.
Compiling and install BoltzTraP2¶
The easiest way to get BoltzTraP2 is to run:
$ pip install BoltzTraP2
This should take care of downloading and installing the dependencies as well.
Users installing from source must install the dependencies first and then run:
$ python setup.py install
from the source directory. For finer-grained control, please see the output of these commands:
$ python setup.py --help $ python setup.py --help-commands $ python setup.py install --help
The BoltzTraP2 installer supports
$ python setup.py develop
which install the module through a set of symbolic links to the source directory, allowing users to immediately tests the effects of their changes to the code.