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: v2.3.2
EiC: @coatless
Editor: @crhea93
Reviewer 1: @jsdodge
Reviewer 2: @eigenbrot
Archive: TBD
JOSS DOI: TBD
Version accepted: TBD
Date accepted (month/day/year): TBD

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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.

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  • Data retrieval
  • Data extraction
  • Data processing/munging
  • Data deposition
  • Data validation and testing
  • Data visualization¹
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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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[coatless]

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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

[pudeIko]

Hi @coatless,

Thanks for the great feedback and initial remarks! I've implemented the requested changes:

The only issue is the lack of short walk through from data ingestion to modeling using a sample data set.

I've added a new section to the documentation with an example covering data extraction and basic analysis. I hope this aligns with what you had in mind.

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

I've prepared a simulated data file and updated the CustomDataloader class so users can test proper configuration and have a more robust template for their code.

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

Done!

I have a technical question. I've committed all changes to the main branch and bumped the version number. Is this the correct approach, or would it be better to keep the changes in a separate branch during the review?

[coatless]

@pudeIko Thank you for the fast turn around!

That's okay with putting everything into main and tagging a release as this is part of intake. I'll update the starting version number in the ticket to match with the initial EiC intake.

[pudeIko]

Hi @coatless,

Hope you're well! I’m just touching base on the review of the software package. I totally understand how things can get busy—just keen to know how it’s going or if there’s anything you need from my side to keep things rolling.

[coatless]

@pudeIko it's on my end at the moment. We're working on finding a new editor for the submission.