piva 3.0.0

PIVA - Photoemission Interface for Visualization and Analysis

Photoemission Interface for Visualization and Analysis

PIVA is a graphical user interface (GUI) application built with PyQt5 and pyqtgraph toolkits, designed for the interactive and intuitive examination of large image-like datasets. While it can display any multidimensional data, most of its functionalities are specifically tailored for users conducting Angle-Resolved Photoemission Spectroscopy (ARPES) experiments.

Its primary objective is to improve the efficiency of ARPES data inspection and analysis by providing interactive, intuitive tools designed to handle large, multidimensional datasets. For more information and a comparison with other packages commonly used in the ARPES community, please refer to the Statement of need section of the accompanying manuscript.

A variety of standard methods and image processing algorithms are available directly from the GUI. Additionally, several utilities are particularly useful during the experimental phase when decisions about subsequent steps need to be made quickly. These utilities include automated methods for locating the highest symmetry points, azimuthal rotation, and autogenerated experimental notebooks. These features are implemented for various beamlines at different synchrotron sources around the world.

Installation

The installation of PIVA has been tested on macOS, Windows and Linux.

The easiest way to install the package is to use pip. Just type the following on a command line:

 pip install piva

Alternatively, you can install the package directly from the source:

git clone https://github.com/pudeIko/piva.git
cd piva
conda env create -f environment.yml

This will automatically set up the virtual environment and install the package in editable mode.

Documentation

The showcase above highlights the general usage and capabilities of the package. For more detailed information and examples, visit the project's documentation website, including:

• Getting Started, to learn more about installation, opening example datasets, and running automated tests,
• GUI Applications, to explore the layout and functionalities of the interactive viewers, and
• Data Handling, to discover the supported file types and how PIVA manages data harmonization.

Citing

TBD