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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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.

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  • Data retrieval
  • Data extraction
  • Data processing/munging
  • Data deposition
  • Data validation and testing
  • Data visualization¹
  • Workflow automation
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• Geospatial
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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.

[coatless]

@pudeIko Thank you for your patience! We've secured an editor to further move the review along.

We're happy to announce that @crhea93 will be the editor for your submission.

For next step, we'll be working toward getting reviewers assigned. This step is detailed here:

https://www.pyopensci.org/software-peer-review/how-to/author-guide.html#the-review-begins

I'll let @crhea93 introduce himself 🙂

[crhea93]

@pudeIko Good morning!
Just to introduce my self briefly -- I am a Ph.D. scientist at the Dragonfly Telescope working on photometric and spectroscopic analysis. I'm very excited to help you through the process of a successful submission!

As @coatless mentioned, the first step is to procure two reviewers -- I will be working on this diligently this week.

Please let me know if you have any questions or need any assistance throughout this process.

[crhea93]

Editor response to review:

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Editor comments

👋 Hi @jsdodge and @eigenbrot! Thank you for volunteering to review
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Reviewers: jsdodge, eigenbrot
Due date: 04/22/2025

[pudeIko]

Hi all! 👋

That's great news, and thank you @crhea93, @jsdodge, and @eigenbrot for joining!

I'm excited to see the ball rolling and look forward to working with you on the review! 🚀

[jsdodge]

Hi @crhea93 , thanks, I've completed the review.

Nice to meet you, @pudeIko, and @eigenbrot! I look forward to working with you on this review.

[crhea93]

@jsdodge @eigenbrot

Fantastic! Thank you two for being so prompt.

The next step is to work your way through the package following the review template. When it is complete, please post your filled in template here as a comment.

[jsdodge]

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Estimated hours spent reviewing:

---

Review Comments

[eigenbrot]

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The package includes all the following forms of documentation:

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• The package name
• Badges for:
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  • A repostatus.org badge,
  • Python versions supported,
  • Current package version (on PyPI / Conda).

NOTE: If the README has many more badges, you might want to consider using a table for badges: see this example. Such a table should be more wide than high. (Note that the a badge for pyOpenSci peer-review will be provided upon acceptance.)

• Short description of package goals.
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  • Currently says "TBD"

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Reviewers are encouraged to submit suggestions (or pull requests) that will improve the usability of the package as a whole.
Package structure should follow general community best-practices. In general please consider whether:

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• The need for the package is clear
• All functions have documentation and associated examples for use
• The package is easy to install

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• Functionality: Any functional claims of the software been confirmed.
• Performance: Any performance claims of the software been confirmed.
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  • All tests pass on the reviewer's local machine for the package version submitted by the author. Ideally this should be a tagged version making it easy for reviewers to install.
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    • See below for a more detailed description
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  • Code format is standard throughout package and follows PEP 8 guidelines (CI tests for linting pass)
    • It appears that the CI linting step is failing, but being ignored

For packages also submitting to JOSS

• The package has an obvious research application according to JOSS's definition in their submission requirements.

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Final approval (post-review)

• The author has responded to my review and made changes to my satisfaction. I recommend approving this package.

Estimated hours spent reviewing: 7

---

Review Comments

Summary

The PIVA package is an incredibly well realized toolkit for analyzing multidimensional data, specifically data from ARPES experiments. I don't personally work with ARPES data, but after reading the thorough documentation and playing with PIVA for a few hours I felt I had gained a good understanding of the common data viewing and analysis techniques of that field.

PIVA is undoubtedly a very useful tool for analyzing complex data, and a great addition to the pyOpenSci community. PIVA's representation of, and interaction with, 3D data is so natural and intuitive that I wrote my own toy DataLoadersubclass to view 3D FITS data (a common format in astronomy) and it works great! I love the way binning is represented in the Data Viewer.

The biggest weakness from a user-facing experience is a gap in the documentation regarding the final steps of implementing custom data loaders and GUI plugins. This could be easily remedied with more detailed documentation and examples.

The biggest weakness from a packaging perspective is how the tests are implemented and the test coverage in general.

Specific Comments

Custom Loaders/Plugins

1. The construction of either a Dataloader subclass or a custom QT widget is generally well described, but how they are imported into PIVA through either DataloaderImporter or PluginImporter is less clear. Examples of these *Importer classes are provided in external files, but I would really like to see a detailed description of the parameters and methods of these classes. They are not mentioned in the otherwise-complete API documentation. A suggestion would be to write abstract bases of these *Importer classes that clearly define the interface.

2. The fact that a Dataloader subclass must contain a load_data() method is a good indication that Dataloader should probably be an abstract base class.

Test System

1. Per the documentation, PIVA tests are run by calling the test modules directly, which starts a pytest session. This felt weird to me; I think most people are used to simply running pytest in the project root, which does work for PIVA. Running pytest is the method for testing described in CONTRIBUTING.md.

2. pytest is not listed in the package requirements table. There is also no "test" pip extra.

3. Test coverage for the GUI functionality seems high, but the rest of the package could use more tests. In particular, there don't seem to be any tests of the working_procedures module.

4. Despite the presence of a "tox.ini" file, simply running tox locally fails. (I suspect tox is only used for CI builds, so maybe this doesn't matter)

Use of Older Frameworks

There are few places in the code where older methods and frameworks could be replaced with more modern alternatives that would improve the maintainability and robustness of the package. In particular:

1. The Dataset class inherits from python's Namespace class, which allows for properties to be accessing with a nice ds.property syntax. I would highly recommend moving to using either python dataclasses or pydantic BaseModels, both of which are specifically designed for this type of interaction. Pydantic's BaseModel also offers a lot of useful type-validation functionality that may be particularly useful because the internals of the Dataset are tightly constrained to how that class is used by other code.

2. In a few places (e.g., Notebooks created by the "touch" button in the Data Viewer) file paths are concatenated with a + operator. At the very least I would recommend using os.path.join, or (even better) pathlib.Path objects.

Documentation

Overall the documentation is extremely good. My comments are minor:

1. PIVA's pypi page does not contain a "Documentation" link to the readthedocs site. This is a shame because the documentation is so good!

2. I think the "What is piva" section would be more impactful if the multiple strengths/features of piva were given their own subheadings.

3. The "Implemented Dataloaders" table is great, and would be even more great if the individual data loaders were links to the respective class in the API documentation.

4. On the "Data Ingestion and Analysis" page it would be very useful to have an example dataset (the same one used in the example code) so a user can follow along directly. I was eventually able to run the notebook with one of the "test/data" datasets; perhaps elevating one or more of these datasets to an "examples" folder would make their utility more obvious.

Misc

• Data Viewer line plots show nothing if NaN values are present. The images still render correctly.
• astropy is listed as a dependency, but I can't find its usage anywhere
• Related, the use of astropy.units might be helpful. It's also possible the entire constants module could be replaced with astropy.constants.
• When Saving data to a pickle (via the "save" button in the Data Viewer) it would be nice to be able to specify the save directory.
• I found db as the script name to be a little confusing. I kept expecting it to be piva.
• I was expecting the "touch" and "start JL session" buttons to populate a notebook with correct paths for the dataset loaded into the Data Viewer. Instead it looks like they just copied a generic template to the given directory.
• It looks like ruff errors are being ignored in CI
• In tiled window manager, content in windows gets truncated if the window is small enough
• Any opened Jupyter sessions (created with the "start JL session" button) persist after closing all Piva windows.
• Installation was painless with multiple methods, even those not described in the documentation. FWIW, PIVA appears to work correctly (and the tests pass) for all python versions between 3.10 and 3.13.
• When clicking the "open in PIT" button the resulting PIT window displays a terminal with the following error in python 3.11 (but not 3.10):

Cell In[1], line 1
----> 1 get_ipython().run_line_magic('pylab', 'qt')

File ~/micromamba/envs/piva11/lib/python3.11/site-packages/IPython/core/interactiveshell.py:2481, in InteractiveShell.run_line_magic(self, magic_name, line, _stack_depth)
   2479     kwargs['local_ns'] = self.get_local_scope(stack_depth)
   2480 with self.builtin_trap:
-> 2481     result = fn(*args, **kwargs)
   2483 # The code below prevents the output from being displayed
   2484 # when using magics with decorator @output_can_be_silenced
   2485 # when the last Python token in the expression is a ';'.
   2486 if getattr(fn, magic.MAGIC_OUTPUT_CAN_BE_SILENCED, False):

File ~/micromamba/envs/piva11/lib/python3.11/site-packages/IPython/core/magics/pylab.py:159, in PylabMagics.pylab(self, line)
    155 else:
    156     # invert no-import flag
    157     import_all = not args.no_import_all
--> 159 gui, backend, clobbered = self.shell.enable_pylab(args.gui, import_all=import_all)
    160 self._show_matplotlib_backend(args.gui, backend)
    161 print(
    162     "%pylab is deprecated, use %matplotlib inline and import the required libraries."
    163 )

File ~/micromamba/envs/piva11/lib/python3.11/site-packages/ipykernel/inprocess/ipkernel.py:200, in InProcessInteractiveShell.enable_pylab(self, gui, import_all, welcome_message)
    198 if not gui:
    199     gui = self.kernel.gui
--> 200 return super().enable_pylab(gui, import_all, welcome_message)

TypeError: InteractiveShell.enable_pylab() takes from 1 to 3 positional arguments but 4 were given