`phasecurvefit`

[nstarman]

Submitting Author: (@nstarman)
All current maintainers: (@nstarman, @jnibauer)
Package Name: phasecurvefit
Construct paths through phase-space points, with JAX.
Repository Link: https://github.com/GalacticDynamics/phasecurvefit
Version submitted: v0.1.0
EiC: @crhea93
Editor: TBD
Reviewer 1: TBD
Reviewer 2: TBD
Archive: TBD
JOSS DOI: TBD
Version accepted: TBD
Date accepted (month/day/year): TBD

---

Code of Conduct & Commitment to Maintain Package

• I agree to abide by pyOpenSci's Code of Conduct during the review process and in future interactions in spaces supported by pyOpenSci should it be accepted.
• I have read and will commit to package maintenance after the review as per the pyOpenSci Policies Guidelines.

Description

Filamentary structures are ubiquitous in the physical sciences, ranging from coherent streams of stars called stellar streams to elongated structures in turbulent fluids, plasmas, and the interstellar medium. In the context of stellar streams, a common preprocessing step is ordering observational or simulation data to derive the mean path of the stream's trajectory through phase-space.

phasecurvefit is an open-source Python package for constructing such orderings and paths by walking along the local phase-space flow using. There are two core components: the first is walk_local_flow which builds an approximate ordering and trajectory but which might miss some of the data; and the second is a fast-to-train autoencoder that imputes the full ordering and trajectory.

Scope

• Please indicate which category or categories.
  Check out our package scope page to learn more about our
  scope. (If you are unsure of which category you fit, we suggest you make a pre-submission inquiry):

  • 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

Domain Specific

• Geospatial
• Education

Community Partnerships

If your package is associated with an existing community please check below:

While this package does not directly interface with Astropy, it easily interoperates with it, through unxt, which brings Astropy to JAX.

• Astropy:My package adheres to Astropy community standards
• Pangeo: My package adheres to the Pangeo standards listed in the pyOpenSci peer review guidebook

• 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):

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

The target audience is scientists with data or simulations of stellar streams, or similar filamentary structures like elongated structures in turbulent fluids, plasmas, and the interstellar medium. Current scientific applications of this package, not by the submitters, are analyses of Milky Way stellar streams data, fitting the path of the stream for orbit and membership likelihood analyses.

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

Not to our knowledge. Our code, written in JAX, not only fits stream-like structures in phase space, it does so differentiably e.g. for use in gradient-utilizing inference pipelines.

• If you made a pre-submission enquiry, please paste the link to the corresponding issue, forum post, or other discussion, or @tag the editor you contacted:

Technical checks

For details about the pyOpenSci packaging requirements, see our packaging guide. Confirm each of the following by checking the box. This package:

• does not violate the Terms of Service of any service it interacts with.
• uses an OSI approved license.
• contains a README with instructions for installing the development version.
• includes documentation with examples for all functions.
• contains a tutorial with examples of its essential functions and uses.
• has a test suite.
• has continuous integration setup, such as GitHub Actions CircleCI, and/or others.

Publication Options

• Do you wish to automatically submit to the Journal of Open Source Software? If so:

JOSS Checks

• The package has an obvious research application according to JOSS's definition in their submission requirements. Be aware that completing the pyOpenSci review process does not guarantee acceptance to JOSS. Be sure to read their submission requirements (linked above) if you are interested in submitting to JOSS.
• The package is not a "minor utility" as defined by JOSS's submission requirements: "Minor ‘utility’ packages, including ‘thin’ API clients, are not acceptable." pyOpenSci welcomes these packages under "Data Retrieval", but JOSS has slightly different criteria.
• The package contains a paper.md matching JOSS's requirements with a high-level description in the package root or in inst/. Yes, in the PR 📝 docs: JOSS paper GalacticDynamics/phasecurvefit#3, which will be merged on acceptance
• The package is deposited in a long-term repository with the DOI: 10.5281/zenodo.18714341

Note: JOSS accepts our review as theirs. You will NOT need to go through another full review. JOSS will only review your paper.md file. Be sure to link to this pyOpenSci issue when a JOSS issue is opened for your package. Also be sure to tell the JOSS editor that this is a pyOpenSci reviewed package once you reach this step.

Are you OK with Reviewers Submitting Issues and/or pull requests to your Repo Directly?

This option will allow reviewers to open smaller issues that can then be linked to PR's rather than submitting a more dense text based review. It will also allow you to demonstrate addressing the issue via PR links.

• Yes I am OK with reviewers submitting requested changes as issues to my repo. Reviewers will then link to the issues in their submitted review.

Confirm each of the following by checking the box.

• I have read the author guide.
• I expect to maintain this package for at least 2 years and can help find a replacement for the maintainer (team) if needed.

Please fill out our survey

• Last but not least please fill out our pre-review survey. This helps us track
  submission and improve our peer review process. We will also ask our reviewers
  and editors to fill this out.

P.S. Have feedback/comments about our review process? Leave a comment here

Editor and Review Templates

The editor template can be found here.

The review template can be found here.

Footnotes

1. Please fill out a pre-submission inquiry before submitting a data visualization package. ↩

[crhea93]

Editor in Chief checks

Hi there! Thank you for submitting your package for pyOpenSci
review. Below are the basic checks that your package needs to pass
to begin our review. If some of these are missing, we will ask you
to work on them before the review process begins.

Please check our Python packaging guide for more information on the elements
below.

• Installation The package can be installed from a community repository such as PyPI (preferred), and/or a community channel on conda (for example, conda-forge, bioconda).
  • The package imports properly into a standard Python environment import package.
• Fit The package meets criteria for fit and overlap.
• Documentation The package has sufficient online documentation to allow us to evaluate the package's function and scope without installing the package. This includes:
  • User-facing documentation that overviews how to install and start using the package.
  • Short, quickstart tutorials that help a user understand how to use the package and what it can do for them.
  • API documentation (documentation for your code's functions, classes, methods, and attributes): this includes clearly written docstrings with variables defined using a standard docstring format.
• Core GitHub repository Files
  • README The package has a README.md file with a clear explanation of what the package does, instructions on how to install it, and a link to development instructions.
  • Contributing File The package has a CONTRIBUTING.md file that details how to install and contribute to the package.
  • Code of Conduct The package has a CODE_OF_CONDUCT.md file.
  • License The package has an OSI approved license.
    NOTE: We prefer that you have development instructions in your documentation too.
• Issue Submission Documentation All of the information is filled out in the YAML header of the issue (located at the top of the issue template).
• Automated tests Package has a testing suite and is tested via a Continuous Integration service.
• Repository The repository link resolves correctly.
• Package overlap The package doesn't entirely overlap with the functionality of other packages that have already been submitted to pyOpenSci.
• Archive (JOSS only, may be post-review): The repository DOI resolves correctly.
• Version (JOSS only, may be post-review): Does the release version given match the GitHub release (v1.0.0)?
• The package authors have disclosed the use of Generative AI tools in the development and/or maintenance of their package.

Optional but recommended:

• Linting The package uses automated linting (e.g., ruff) to enforce code style and quality standards, ideally integrated with CI.
• Type checking The package includes type hints and uses a type checker (e.g., mypy) to catch type-related issues, ideally integrated with CI.
• Modern tooling The package uses modern Python development tools (e.g., uv for dependency management) and Hatch for building publishing and running development tasks to streamline development workflows. See the pyOpenSci packaging guide for more on modern packaging tools.

Optional: Let projects know that it's a great idea for projects to have a .github repository for the project organization where they can host a commonly used LICENSE, Code of Conduct, and even a YAML file with label definitions. These items will then be automatically applied to every repository in the organization to ensure consistency (but can be customized within repos too). The SunPy project has a great example of this.

---

• Initial onboarding survey was filled out
  We appreciate each maintainer of the package filling out this survey individually. 🙌
  A big thank you in advance to authors for setting aside five to ten minutes to do this. It truly helps our organization! 🙌

---

---

Editor comments

Thank you for this submission. It is well conceived demonstrating a substantial effort on your part. There are however a few things that we would like to see addressed before assigning an editor.

• PyOpenSci is committed to ensuring that all code developed is readily maintainable in light of recent agentic coding. I notice that a high number of your commits seem to be generated by generative AI. While this is certainly not a problem in-and-of itself, it is important that you can vouch for every line of code created by AI on your behalf. Therefore, we require a detailed report in the README disclosing the use of generative AI. Additionally, although not yet solidified in our policy, we ask that you do not use generative AI in your communications in this thread except for translation help :) Thank you for your patience as we develop more stringent requirements/policies in this ever-evolving landscape.

[nstarman]

Thank you for this submission. It is well conceived demonstrating a substantial effort on your part. There are however a few things that we would like to see addressed before assigning an editor.

Thanks! Happy to address

PyOpenSci is committed to ensuring that all code developed is readily maintainable in light of recent agentic coding. I notice that a high number of your commits seem to be generated by generative AI. While this is certainly not a problem in-and-of itself, it is important that you can vouch for every line of code created by AI on your behalf. Therefore, we require a detailed report in the README disclosing the use of generative AI.

I'd like to push back on this point in two ways. The first is that the commits are "generated" by AI. I reviewed each commit (13) and can confidently state that the vast majority of the operative code was written by me. Adapting, improving, and fine-tuning the mathematical techniques. Crafting the API. That was not AI. AI was used as a code assistant for writing the docs(strings), type annotations, and unit tests. As is the case with generated content, significant editing was required. Especially since the docs are tested as part of CI. So I don't believe either a high number of commits nor most of the core code was AI generated, just the surrounding supports. The infrastructure of this package is closely copied from the infrastructure I have been developing across my many other packages: for example the 22 repositories I maintain at https://github.com/orgs/GalacticDynamics/repositories. This speaks to the challenging issue of determining authorship, when for example I ask an AI agent to "copy the infrastructure from https://github.com/orgs/GalacticDynamics/unxt and adapt it to this code repository". Is this generated by AI or is this mostly written by me (in unxt) and an AI changed the word unxt to phasecurvefit? I would like to think that having spent significant time developing the shared architecture between the GalacticDynamics org's repos, that copy-paste-with-small changes wouldn't erase my authorship.
Either way, I agree that taking responsibility for one's code is important. To this end I have a PR adding to the Contributing.md, adapting the AI policy we're still developing for Astropy — GalacticDynamics/phasecurvefit#18.
The second point — which might not be relevant given the first point — is that the README is the appropriate place for a disclosure report on who/what authored which lines. Generative AI runs the gamut from line-completions to file-completions. And the README is a living document that is also published to PyPI. As an active code that will continue to see development, such a disclosure is guaranteed to be quickly outdated and should be removed essentially immediately as it's relevant only to a specific moment.
If required I would produce a separate document relevant to this review process, but as the policy in GalacticDynamics/phasecurvefit#18 indicates, I would more happily "explain all [code] during review" as I wrote most of it and reviewed, tested, and understand all of it.

Thank you!

[crhea93]

Hi Nathan,
My apologies if my original comments were not clear enough! I didn't mean to insinuate (and I sincerely apologize if I did) that the code itself was written by AI and not you. I meant that the commit messages are often signed by Co-pilot and the text of the commit is very AI-coded (emojis & language). There is no worries at all if gen AI was used to help build the support, documentation, or tests.

We believe that the README is the perfect place for the disclosure of use of generative AI since it is the first place people look when they check out a repo. We don't expect the authors to have a detailed description of each location in which they used generative AI in the README since, as you mention, that could change drastically over the life of a project. However, we do believe that a README can be reasonably written and updated to include the major AI usage.
This repo has an example of a minimal acceptable gen AI disclosure : https://github.com/DynamicsAndNeuralSystems/pyhctsa

[nstarman]

The commit messages are often signed by Co-pilot

Oh. That's copilot review, for reviewing the PRs to find bugs / edge cases.

commit is very AI-coded (emojis & language)

That's https://pypi.org/project/commitizen-gitmoji/ to enable me to standardize my construction of commit messages via the command line. Standard construction means labeler can auto-label PRs and release can auto-sort and categorize PRs into a release changelog. I recommend some form of https://www.conventionalcommits.org/en/v1.0.0/ to any project, I just prefer the gitmoji variant for visual aesthetics.

README can be reasonably written and updated to include the major AI usage.

SGTM. GalacticDynamics/phasecurvefit#23 adapts https://github.com/DynamicsAndNeuralSystems/pyhctsa .

[crhea93]

@nstarman Thank you very much for addressing those concerns! I really appreciate the clear responses :)

We will be proceeding with this submission!

[nstarman]

Thank you, @crhea93! I'm looking forward to the review process and happy to address any feedback that comes up. 😊