The Python library sisl was born out of a need to handle(create and read), manipulate and analyse output from DFT programs.
It was initially developed by Nick Papior (co-developer of Siesta) as a side-project to TranSiesta
and TBtrans to efficiently analyse TBtrans output for N-electrode calculations.
Since then it has expanded to accommodate a rich set of DFT code input/outputs such as (but not limited to)
VASP, OpenMX, BigDFT, Wannier90.
A great deal of codes are implementing, roughly, the same thing. However, every code implements their own analysis and post-processing utilities which typically turns out to be equivalent utilities only having the interface differently.
sisl tries to solve some of the analysis issues by creating a unified scripting approach in Python which does analysis using the same interface, regardless of code being used. For instance one may read the Kohn-Sham eigenvalue spectrum from various codes and return them in a consistent manner so the post-processing is the same, regardless of code being used.
sisl is also part of the training material for a series of workshops hosted here.
In some regards it has overlap with ASE and sisl also interfaces with ASE.
Here we show 2 examples of using sisl together with Siesta.
To read in a Hamiltonian from a Siesta calculation and calculate the DOS for a given Monkhorst-Pack grid one would do:
import numpy as np
import sisl
H = sisl.get_sile('RUN.fdf').read_hamiltonian()
mp = sisl.MonkhorstPack(H, [13, 13, 13])
E = np.linspace(-4, 4, 500)
DOS = mp.apply.average.DOS(E)
from matplotlib import pyplot as plt
plt.plot(E, DOS)
Which calculates the DOS for a 13x13x13 Monkhorst-Pack grid.
Another common analysis is real-space charge analysis, the following command line subtracts two real-space charge grids and writes them to a CUBE file:
sgrid reference/Rho.grid.nc --diff Rho.grid.nc --geometry RUN.fdf --out diff.cube
which may be analysed using VMD, XCrySDen or other tools.
Installing sisl using PyPi or Conda is the easiest:
pip3 install sisl
pip3 install sisl[analysis] # also installs tqdm and xarray
# or
conda install -c conda-forge sisl
If performing a manual installation, these packages are required:
- A C- and fortran-compiler
- numpy (1.13 or later)
- scipy (0.18 or later)
- netCDF4
- setuptools
- pyparsing (1.5.7 or later)
- pytest, optional dependency for running the tests
- matplotlib, encouraged optional dependency
- tqdm, encouraged optional dependency
- xarray, optional dependency
Subsequently manual installation may be done using this command:
python3 setup.py install --prefix=<prefix>
If trying to install without root access, you may be required to use this command:
python3 setup.py install --user --prefix=<prefix>
Once installed, the installation can be tested by executing the following:
pytest --pyargs sisl
There are different places for getting information on using sisl, here is a short list of places to search/ask for answers:
- Documentation, recommended reference page
- Workshop examples showing different uses
- Ask questions on the Github issue page
- Ask questions on the Gitter page
If sisl was used to produce scientific contributions, please use this DOI for citation. We recommend to specify the version of sisl in combination of this citation:
@misc{zerothi_sisl,
author = {Papior, Nick},
title = {sisl: v<fill-version>},
year = {2020},
doi = {10.5281/zenodo.597181},
url = {https://doi.org/10.5281/zenodo.597181}
}
To get the BibTeX entry easily you may issue the following command:
sdata --cite
which fills in the version number.
- If you've ideas of missing features
- If you've ideas for improving documentation
- If you've found a bug
- If you've found a documentation error
- If you've created a tutorial
Then please share them here!
All of the above may be done via a pull-request or by opening an issue.
Remember:
No contribution is too small!