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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, functions and tools related to ARPES data loading and 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.

Installation

It is recommended to install with pip:

$ pip install arpys

Documentation

Please find the documentation [here]https://arpys.readthedocs.io/en/latest/.

Rough description of contents

The recommended way of using arpys currently is to make use of the classes in dataloaders.py (if a class for 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)

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.

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>.

See also

This module had at some point exploded in size, offering many different tools, GUIs and command line interpreters to accomplish all kinds of things. The result was a long dependency list and complicated installations. In an attempt to get things more streamlined and structured, the module has been stripped down to its bare essentials, outsourcing the graphical capabilities.

You can find PIT, a GUI for quick visualization of ARPES (and other) data here. Use the corresponding plugin to use arpys data loading and postprocessing tools in conjunction with PIT.

================================================================================ Copyright (c) 2020 Kevin Kramer, Universität Zürich (kevin.kramer@uzh.ch)

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