Galore is a package which applies Gaussian and Lorentzian broadening to data from ab initio calculations. The two main intended applications are
- Gaussian and Lorentzian broadening of electronic density-of-states, with orbital weighting to simulate UPS/XPS/HAXPES measurements.
- Application of Lorentzian instrumental broadening to simulated Raman spectra from DFPT calculations.
A brief overview is given in this README file.
A full manual, including tutorials and API documentation,
is available online at readthedocs.io.
You can build a local version using Sphinx with
make html from
the docs directory of this project.
An brief formal overview of the background and purpose of this code has been published in The Journal of Open Source Software.
Broadening, weighting and plotting are accessed with the
For full documentation of the command-line flags, please use the
Data may be provided as a set of X,Y coordinates in a text file of comma-separated values (CSV). Whitespace-separated data is also readable, in which case a .txt file extension should be used.
To plot a CSV file to the screen with default Lorentzian broadening (2 cm-1), use the command:
galore MY_DATA.csv -l -p
and to plot to a file with more generous 10 cm-1 broadening:
galore MY_DATA.csv -l 10 -p MY_PLOT.png
will provide the additional data needed.
Other file formats are supported, including IR and Raman intensity simulation output. See the Tutorials for usage examples.
UPS, XPS or HAXPES spectra can be simulated using Galore. This requires several inputs:
- Orbital-projected density of states data. - This may be provided as an output file from the VASP or GPAW codes. - Formatted text files may also be used.
- Instrumental broadening parameters. The Lorentzian and Gaussian broadening widths are input by the user as before.
- Photoionization cross section data, which is used to weight the
contributions of different orbitals.
- Galore includes data for valance band orbitals at Al k-α (XPS) and He II (UPS) energies, drawn from a more extensive table computed by Yeh and Lindau (1985). An alternative dataset may be provided as a JSON file; it is only necessary to include the elements and orbitals used in the DOS input files.
- Cross-sections for high-energy (1-1500 keV) photons have been fitted from tabulated data computed by Scofield (1973).
See the Tutorials for a walkthrough using sample data.
The orbital data can also be accessed without working on a particular
spectrum with the
galore-get-cs program. For example:
galore-get-cs 4 Sn O
will print a set of valence orbital weightings for Sn and O corresponding to a 4 keV hard x-ray source. These values have been converted from atomic orbital data to per electron cross-sections.
galore-plot-cs program is provided for plotting over a range
of energies using the high-energy fitted data:
galore-plot-cs Pb S --emin 2 --emax 10 -o PbS.pdf
generates a publication-quality plot of cross-sections which may help in the selection of appropriate HAXPES energies for experiments with a given material.
Galore is currently compatible with Python versions 3.7 and newer. Galore uses Numpy to apply convolution operations. Matplotlib is required for plotting.
Galore uses Pip and setuptools for installation. You probably already
have this; if not, your GNU/Linux package manager will be able to oblige
with a package named something like
python-setuptools. On Max OSX,
the Python distributed with Homebrew includes
setuptools and Pip.
Windows user installation
Anaconda is recommended for managing the Python environment and dependencies on Windows. From the Anaconda shell:
pip3 install .
Linux/Mac developer installation
From the directory containing this README:
pip3 install --user -e .
which installs an editable (
-e) version of galore in your
userspace. The executable program
galore goes to a user directory
~/.local/bin (which may need to be added to your PATH) and
the galore library should be available on your PYTHONPATH. These are
links to the project source folder, which you can continue to edit and
update using Git.
To import data from VASP calculations you will need the Pymatgen
library. If you don't have Pymatgen yet, the requirements can be added
to the Galore installation with by adding
[vasp] to the pip
pip3 install --user -e .[vasp]
Installation for documentation
If you need to build the documentation you can add
[docs] to the
pip command to ensure you have all the Sphinx requirements and
pip3 install --upgrade .[docs]
If you're having trouble with Galore or think you've found a bug, please report it using the Github issue tracker. Issues can also be used for questions and discussion about the Galore methodology/implementation.
This code is developed by the Scanlon Materials Theory Group based at University College London. Suggestions and contributions are welcome; please read the CONTRIBUTING guidelines and use the Github issue tracker.
How to cite Galore
If you use Galore in your research, please consider citing the following work:
Adam J. Jackson, Alex M. Ganose, Anna Regoutz, Russell G. Egdell, David O. Scanlon (2018). Galore: Broadening and weighting for simulation of photoelectron spectroscopy. Journal of Open Source Software, 3(26), 773, doi: 10.21105/joss.007733
Galore includes a machine-readable citation file in an emerging standard format with citation details for the actual code, but as conventions for software citation are still developing the JOSS paper is a more reliable method of giving credit.
Galore is made available under the GNU Public License, version 3.
Development work by Adam J. Jackson took place in the course of research into new transparent conducting materials, led by David O. Scanlon and funded by EPSRC (project code EP/N01572X/1). Work by Alex M. Ganose was supported by a studentship co-sponsored by the Diamond Light Source at the EPSRC Centre for Doctoral Training in Molecular Modelling and Materials Science (EP/L01582/1). Anna Ragoutz was our expert advisor on all things PES, guiding the feature-set and correcting the implementation of weighting, and was supported by an Imperial College Research Fellowship.
We acknowledge useful discussions with Alexey Sokol (who proposed that a code such as this would be useful), Katie Inzani, and Tim Veal. Feature requests and user testing came from Benjamin Williamsion, Christopher Savory and Winnie L. Leung.
This would have been much more painful if not for the excellent scientific Python ecosystem, and the Python Materials Genome project spared us the pain of writing Yet Another Vasp Parser.