Introduction
In this documentation, you will find step-by-step instructions on setting up the package, detailed descriptions of its components, and all necessary information to fully leverage its functionalities.
Given the abundance of image processing software available, it is essential to
clearly define the motivations behind piva, as well as its capabilities
and limitations. This will provide users with a comprehensive overview of the
package, highlighting the aspects that may be beneficial for their research
or work.
What is piva
piva is a GUI application based on the PyQt5 and
pyqtgraph toolkits, designed for the interactive and intuitive
exploration of large image-like datasets. While it can generally display any
multidimensional data, most of its functionalities are tailored for users
performing Angle-Resolved Photoemission Spectroscopy (ARPES) experiments.
Graphical Interface and Interactive Viewers
The showcase video presented below offers a demonstration of the GUI’s layout and overall user experience, while also highlighting its core functionalities and key features in action.
The package allows for live investigation of various datasets individually, as well as linking separate datasets for simultaneous browsing. Interactive sliders enable the display of multidimensional data across all available directions (see DataViewers). In addition to numerous image processing methods, PIVA can represent ARPES images in momentum space and apply corrections specific to the experimental conditions under which the data were acquired.
Several utilities are particularly useful during the experimental phase, such as automated methods for finding the highest symmetry points, azimuthal rotation, and autogenerated experimental notebooks implemented for various beamlines at synchrotron sources worldwide.
The Fitters and PlotTool applications offer additional functionalities for detailed analysis and personalized representation of acquired results.
Data Format Standardization
Moreover, piva translates raw data into a standardized
Dataset object, addressing the issue of diverse data
formats within the ARPES community. The Dataset can be
easily used outside of piva by simply importing the
data_loaders.py module.
Analysis Tools
Unlike other experimental techniques, discrepancies in ARPES results between
different physical systems necessitate the implementation of unique analysis
methods for nearly every investigated system. To address this, piva
includes a generic toolkit for handling photoemission results that can be
further tailored to meet specific user needs. Additionally, it offers
straightforward tools for exporting loaded datasets and opening them with a
jupyter-lab notebook for more sophisticated analysis requiring hands-on
scripting.
Custom Add-ons
The architecture of the piva package is designed with modularity in
mind, providing users with a convenient platform for implementing custom data
loaders and other plugins. Detailed descriptions and examples of
configuring new modules can be found in this documentation.
In summary, piva provides an efficient, intuitive GUI application for
examining multiple datasets and includes a platform for importing data into a
convenient format. It is based on python and jupyter-lab environments,
allowing users to easily conduct detailed analyses of their acquired data.