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Python tools and scripts for ARPES data analysis

Project description

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.

Requirements

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, arpys requires PyQt5 and its dependency SIP. Confer INSTALLING.md for more info.

Installation

Please refer to the file INSTALLING.md

Rough description of contents

The recommended way of using arpys currently is to make use of the classes in 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[0]
# here.
data = D.data[0]
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[1] - energies[0]
dy = angles[1] - angles[0]
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.

postprocessing.py

Library-like module that contains functions to process ARPES data, like normalizations, bg subtractions, derivative methods, etc.

dataloaders.py

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.

arpes_plot.py

Implements a commad-line interpreter that allows quick visualization of data and provides some basic postprocessing options (like cropping, normalization, angle-to-k conversion,...) 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>

pit

The Python Image Tool: A graphical data analysis tool (in the making) based on the pyqtgraph module.

tools/

A set of little scripts and command-line tools for specific jobs. Confer each tools respective documentation for more info.

gui.py

[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.

apc

A link to arpes_plot.py

bandcharacters.py

A tool to plot the band characters from a wien2k DFT calculation.

utilities/

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 <git@github.com:kuadrat/kustom.git>.

================================================================================ Copyright (c) 2018 Kevin Kramer, Universität Zürich (kevin.kramer@uzh.ch)

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