Python tools and scripts for ARPES data analysis
ARPYS: python module for ARPES (Angle Resolved PhotoEmission Spectroscopy) data analysis
This repository consists of libraries, programs and scripts related to ARPES data analysis. The software contained in this repository is distributed under the GNU General Public License v3+. See file 'COPYING' for more information. The file 'LICENSE-3RD-PARTY.txt' covers the different licenses of libraries and other programs used by ARPYS.
The requirements are listed in
requirements.txt. Most notable are
pyqtgraph, a nice library that's built on PyQt and allows fast real-time
data visualization. Consequently,
PyQt5 and its dependency
INSTALLING.md for more info.
Please refer to the file
Rough description of contents
The recommended way of using
arpys currently is to make use of the classes
dataloaders.py (if the beamline in question has already been implemented)
to get the relevant data into a usable format in python. Then, one can use the
functions provided in
postprocessing.py (normalizations, background
subtractions, etc.) on the so loaded data. Here's a simple example:
# Import the dataloaders and postprocessings from arpys import dl, pp # Load the data (this requires an appropriate dataloader to be defined in # dataloaders.py. If it isn't, check the file to see how you should define it # in your case. D = dl.load_data('your_arpes_data_file.suffix') # D is a Namespace object which stores the data array and some meta-data. # In this example we're assuming the data to contain a single energy-k cut. # arpys always loads data as 3d-arrays, however, so we need to take D.data # here. data = D.data energies = D.xscale angles = D.yscale # Apply some background subtraction (use at your own discretion): bg_subtracted = pp.subtract_bg_matt(data) # Try taking the second derivative to make the bands more visible. This often # requires smoothing first and is very susceptible to the various parameters. from scipy.ndimage import filters smoothened = filters.gaussian_filter(bg_subtracted, sigma=10) dx = energies - energies dy = angles - angles second_derivative = pp.laplacian(smoothened, dx, dy)
The tools that ship with
arpys should be considered to be in an untested
stage and used at your own discretion.
Library-like module that contains functions to process ARPES data, like normalizations, bg subtractions, derivative methods, etc.
Contains classes which handle reading of ARPES data from different beamlines (i.e. different data format and conventions) and passing it in a fixed, python-friendly format for use by other tools and scripts in this module.
Implements a commad-line interpreter that allows quick visualization of data
and provides some basic postprocessing options (like cropping, normalization,
Can be used as a program from the command line (possibly after a
chmod 755 arpes_plot.py) by
$ arpes_plot.py <path-to-data>
The Python Image Tool: A graphical data analysis tool (in the
making) based on the
A set of little scripts and command-line tools for specific jobs. Confer each tools respective documentation for more info.
[Deprecated] A GUI which allows to take quick looks at cuts and maps. This is
built with tkinter and matplotlib and, consequently, is rather slow.
In most cases
arpes_plot.py should be used instead.
A link to
A tool to plot the band characters from a wien2k DFT calculation.
A submodule that contains some custom python code that the original author used on his system and got incorporated into arpys. arpys mostly needs the axes subclasses and some small helper functions from there. This is actually just a copy of another module that is hosted at <firstname.lastname@example.org:kuadrat/kustom.git>.
================================================================================ Copyright (c) 2018 Kevin Kramer, Universität Zürich (email@example.com)
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