PIVA Submission

[pudeIko]

Submitting Author: Wojciech Radoslaw Pudelko (@pudeIko)
All current maintainers: Wojciech Radoslaw Pudelko (@pudeIko)
Package Name: PIVA
One-Line Description of Package: Visualization and analysis toolkit for experimental data from Angle-Resolved Photoemission Spectroscopy (ARPES)
Repository Link: https://github.com/pudeIko/piva
Version submitted: v2.3.1
EiC: TBD
Editor: TBD
Reviewer 1: TBD
Reviewer 2: TBD
Archive: TBD
JOSS DOI: TBD
Version accepted: TBD
Date accepted (month/day/year): TBD

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Description

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PIVA (Photoemission Interface for Visualization and Analysis) is a GUI application designed for the interactive and intuitive exploration of large, image-like datasets. While it accommodates the visualization of any multidimensional data, its features are specifically optimized for researchers conducting Angle-Resolved Photoemission Spectroscopy (ARPES) experiments. In addition to numerous image processing tools and the ability to apply technique-specific corrections, PIVA includes an expanding library of functions and methods for detailed fitting and advanced spectral analysis.

Scope

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  • Data retrieval
  • Data extraction
  • Data processing/munging
  • Data deposition
  • Data validation and testing
  • Data visualization¹
  • Workflow automation
  • Citation management and bibliometrics
  • Scientific software wrappers
  • Database interoperability

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• Geospatial
• Education

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• For all submissions, explain how and why the package falls under the categories you indicated above. In your explanation, please address the following points (briefly, 1-2 sentences for each):

Data extraction: Within the ARPES community, it is common for each beamline and lab to use their own file formats and conventions, which means one often need a custom script to get everything into a common format. To handle these discrepancies, PIVA comes with a data_loaders module that converts them into a standardized Dataset object. The current version includes specific Dataloader classes implemented for numerous sources and beamlines around the world.

Data visualization: The package enables efficient and intuitive exploration of large, image-like datasets. It includes specialized interactive viewers designed to handle 2D, 3D, and 4D datasets, depending on the experimental mode or conditions under which they were collected.

• Who is the target audience and what are scientific applications of this package?

Experimental physicists conducting ARPES measurements. The package provides a comprehensive framework addressing most of the experimenter's needs, including data extraction, inspection, validation, and detailed analysis.

• Are there other Python packages that accomplish the same thing? If so, how does yours differ?

Regarding software tailored for ARPES, two notable packages are ARPES Python Tools and PyARPES. However, they differ significantly from PIVA.

The visualization module in the former is limited to generating static plots and lacks any interactive features.

The latter is focused on post-processing and detailed analysis of the spectra, and is different in the following respects:

• interactive exploration and browsing through data is either restricted to 2D data, or conducted inside the Jupyter environment, which highly affects efficiency and makes working with multiple datasets simultaneously difficult.
• Viewers designed for 4D datasets are not implemented.
• PIVA's data_loader module contains richer library of data loading scripts for different light sources around the world.

Furthermore, PyARPES has not been maintained for several years.

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#223 (@SimonMolinsky)

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Editor comments

This is a very well done package, the documentation is nearly perfect with the detailed description of each GUI element and the ability to specify a custom data set. The only issue is the lack of short walk through from data ingestion to modeling using a sample data set.

In addition, it would be highly beneficial to show a fully working custom data loader for a given ARPES file that has a non-standard format. The present example is too sparse.

https://piva.readthedocs.io/en/latest/dataloaders.html#writing-a-custom-dataloader

Please also fix the parameter labels under the API documentation for DataBrowser.add_viewer_to_linked_list()

https://piva.readthedocs.io/en/latest/modules/data_browser.html#data_browser.DataBrowser.add_viewer_to_linked_list