Martini

[kyleaoman]

Submitting Author: Kyle Oman (@kyleaoman)
All current maintainers: (@kyleaoman)
Package Name: martini
One-Line Description of Package: MARTINI is a modular package for the creation of synthetic resolved HI line observations (data cubes) of smoothed-particle hydrodynamics simulations of galaxies.
Repository Link: https://github.com/kyleaoman/martini
Version submitted: 2.0.11 (note JOSS paper is in branch joss-paper, and that branch is somewhat behind main & 2.0.11)
Editor: @hamogu
Reviewer 1: @taldcroft
Reviewer 2: @MicheleDelliVeneri
Archive: https://zenodo.org/doi/10.5281/zenodo.11193206
JOSS DOI: https://doi.org/10.21105/joss.06860
Version accepted: 2.0.15
Date accepted (month/day/year): 06/03/2024

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Description

MARTINI is a modular package for the creation of synthetic resolved HI line observations (data cubes) of smoothed-particle hydrodynamics simulations of galaxies. The various aspects of the mock-observing process are divided logically into sub-modules handling the data cube, source, beam, noise, spectral model and SPH kernel. MARTINI is object-oriented: each sub-module provides a class (or classes) which can be configured as desired. For most sub-modules, base classes are provided to allow for straightforward customization. Instances of each sub-module class are given as parameters to the Martini class; a mock observation is then constructed by calling a handful of functions to execute the desired steps in the mock-observing process.

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The target audience is research astronomers interested in galaxies (broadly, "extragalactic astronomers") from both the theoretical and observational communities. The package provides a way to transform data products from the theory community (smoothed-particle hydrodynamics based simulations of galaxy formation and evolution) into data products closely resembling the atomic hydrogen signal observed with a radio telescope at 21-cm wavelengths. This enables much more faithful comparisons between theoretical predictions and measurements.

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I am not aware of any other actively maintained packages with similar purpose.

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Since I'm coming in through the new astropy route, I had not prepared for JOSS requirements, but was thinking of submitting to JOSS soon anyway. I've now pushed a paper.md in the joss-paper branch. My package is indexed in the ASCL which in turn is indexed in ADS, but neither of these seems to provide an actual DOI so I will need to look into other repositories (probably Zenodo). As far as I understand submission to JOSS happens after the pyOpenSci review so there is a little bit of time to get this done - I expect to have these two items ticked off by the time the pyOpenSci review process is completed.

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[isabelizimm]

Hello there! Just letting you know we have seen this issue and will be following up with pre-review checks shortly!

[isabelizimm]

Thank you so much for this submission! It's so exciting to see these astropy submissions roll in ☄️

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Editor comments

In your README can you add a quick installation piece? It doesn't have to be too fancy, something like the # Installation section on the rdata package would be perfect. In the meantime, I'll sort out finding an editor for this review 🙌

Also, are you able to to add a "version submitted"? I'm assuming it is the latest, but we'll want to know what version the review started at. It's okay if there are releases during the review/the accepted version is different, that is expected as you make edits!

[isabelizimm]

Also, we have an editor lined up; @hamogu will be the editor lined up for this review 🙌

[kyleaoman]

@isabelizimm I added the version submitted. Not sure what you're requesting for the installation information in README - there's a paragraph there already so if something else/different is needed I'm not sure what it is. Or do you not mean the package README?

[isabelizimm]

Ah, I was specifically looking for the way to pip install martini, a section similar to "Installation" on this package. I do see this command in your installation paragraph, but it's quite easy to miss, so it would be ideal to pull it out into its own line, rather than be part of a larger paragraph.

This change is non-blocking to your review, just a note to make it easier to skim through details 😄

[hamogu]

Editor Note

April 7th: I've found one reviewer, but I'm still looking for a second reviewer. Since that's more difficult than expected, I figured the first reviewer can get started, so that we can make good use of the extra time and any suggestions/PRs that the first reviewer might have can already be discussed and implemented.

Editor response to review:

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Editor comments

👋 Hi @taldcroft and @MicheleDelliVeneri! Thank you for volunteering to review
for pyOpenSci!

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[hamogu]

Just a short note that I'm still looking for a second reviewer for martini. There seems to be a bunching of proposals and proposal review deadlines around this time of year that made several people decline whom I asked. I'm doing my best to find someone and sorry for the delay.

[taldcroft]

First look review

@kyleaoman - I started on the review of martini. Some initial comments below.

Installation

In all the places where you specify optional pip dependencies with [optional], the whole thing should be enclosed in quotes for some shells like zsh. Without that you get problems like this:

(martini) ➜  martini git:(main) python3 -m pip install astromartini[hdf5_output]
zsh: no matches found: astromartini[hdf5_output]

!{sys.executable} -m pip install astromartini[eaglesource]==2.0.10
zsh:1: no matches found: astromartini[eaglesource]==2.0.10

This link doesn't exist: https://github.com/kyleaoman/martini/tree/2.0.X.
I think you should be pointing to releases page and pointing users to download a zip file from there.

Tests

Tests are currently failing for me in main and 2.0.10 using Python 3.7:

>           from multiprocess.pool import Pool
E           ModuleNotFoundError: No module named 'multiprocess'

../../miniconda3/envs/martini/lib/python3.7/site-packages/martini/spectral_models.py:90: ModuleNotFoundError
============================================================ short test summary info =============================================================
FAILED tests/test_martini.py::TestParallel::test_parallel_consistent_with_serial[True] - ModuleNotFoundError: No module named 'multiprocess'
FAILED tests/test_martini.py::TestParallel::test_parallel_consistent_with_serial[False] - ModuleNotFoundError: No module named 'multiprocess'
FAILED tests/test_spectral_models.py::TestParallelSpectra::test_parallel_spectra[GaussianSpectrum] - ModuleNotFoundError: No module named 'multiprocess'
FAILED tests/test_spectral_models.py::TestParallelSpectra::test_parallel_spectra[DiracDeltaSpectrum] - ModuleNotFoundError: No module named 'multiprocess'
======================================================= 4 failed, 900 deselected in 1.03s ========================================================

This might be something as simple as a typo where you want multiprocessing not multiprocess.

CI

The above test failure highlights a lack of CI since the README badge is showing green for tests.

The review criteria for badges in the README is shown here, so these will need to be included.

• Badges for:
  • Continuous integration and test coverage,
  • Docs building (if you have a documentation website),
  • A repostatus.org badge,
  • Python versions supported,
  • Current package version (on PyPI / Conda).

The current GitHub workflow action which is providing the current Run Tests pass badge is apparently only checking code linting. You should be running unit tests on at least the minimum and most recent supported versions of Python.

[kyleaoman]

Hi @taldcroft! Thanks for the initial thoughts. A couple of quick things:

• I do mean multiprocess and not multiprocessing, it's a similar package but uses dill for serialisation. It's in the optional_requirements.txt, but this probably means I should have a look at what's required, optional-required and how this interacts with tests.
• There is CI including a full test suite and it does run on github, both on a schedule and on pushes, PRs, etc. The test suite does install the optional requirements. Possible something is glitchy with the badge, it's been ages since I looked at that. I will look at the other badges that aren't present yet.

[taldcroft]

@kyleaoman - about the CI, got it. I was a bit hasty but the workflow file name code_quality.yml and the lint job at the top got me thinking it was only about linting. Since that is not the case then ignore that comment.

The installation docs say that versions of Python 3.7 and later are supported, so the CI testing should probably include all available versions up through 3.12. It is common policy now for scientific Python packages to follow SPEC 0 — Minimum Supported Dependencies and only support Python versions for 3 years from their release date. You can reduce the version proliferation and CI testing by following that, but that is your choice.

[hamogu]

@kyleaoman just to keep you in the loop. I've just contacted the fifth person as a potential second reviewer. I promise we'll find someone, but I'm sorry it's taking way longer than I expected.

[kyleaoman]

@hamogu Thanks for the update, and no worries. I originally submitted this as an astropy affiliated package in March 2023 as that process was being re-thought, so I've got the hang of being patient with the process.

[hamogu]

@MicheleDelliVeneri : Thank you for agreeing to be the second reviewer for this package. I've updated all the posts above which have instructions on how the review is supposed to work, but I don't know if github send out notifications when I add a handle by editing, so I'm just writing separately here. Please reach out to me if you have any questions about the review or if I can help in any way.

[hamogu]

@kyleaoman About getting a DOI for the code: While there are several ways of getting snapshots of your code into a permanent archive that issues a DOI, the easiest (and what I use for my own projects) is probably the Github-Zenodo integration. That way, every time you make a "release" on github, it triggers a webhook that makes zenodo pull that specific release into zenodo and issue a corresponding DOI. It also pulls most of the metadata (authors, licence, etc.) automatically. (It sometimes takes up to an hour, so don't worry if you don't see it immediately, and sometimes the metadata is not right, e.g. if you have bot commits to your code, you might only want to have the humans listed a "author" on zenodo, but you can edit the metadata on zenodo later if needed.)
The nice thing about it is that's is a fire-and-forget setup: Once it's set up it'll work for all future releases automatically.

Of course, you should do whatever you think works best for your package, I'm just trying to make suggestions to help you make that process easy.

[taldcroft]

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Please check off boxes as applicable, and elaborate in comments below. Your review is not limited to these topics, as described in the reviewer guide

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Estimated hours spent reviewing:

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Review Comments

Footnotes

1. Example notebooks provide vignettes, but these require authenticated access to
   specialized databases. I did not verify that these run, so I can only guess that they
   run for a researcher in the field with access. ↩

2. Strictly speaking the API docs do not include an example for every user-facing function.
   This seems like an unreasonable requirement in this package due to the setup required in
   most cases to make use of a function or method. Overall the API docs seem thorough and
   sufficient to me. ↩

3. I don't have the domain expertise to confirm the functional claims of the package. ↩

4. There is an open issue related to missing dependencies for tests. ↩

5. Test coverage is not included in CI but I ran it locally and found better
   than 90% coverage in core modules. Backend-specific modules had low coverage
   perhaps because of missing dependencies. ↩

[kyleaoman]

Got Zenodo automated and put in earlier releases too. There's a known issue where the latest release is flagged incorrectly in Zenodo but that's supposed to resolve when the next release is created, which I'll do at the end of review at the latest.

[kyleaoman]

Thanks @taldcroft for the review. I think that I've now addressed everything raised:

• All issues that you opened are closed with a PR, commit or explanation.
• I added a codecov badge to the readme, it shows 86%. Most files are ~100% covered, with a few (deliberately) close to 0%. These uncovered ones are specific to particular simulations that won't get tested on github. I'm looking at setting up some local tests that I can run from time to time on a cluster where the large test data for these can be easily hosted.
• I noticed you didn't tick the item for "references with DOIs" in the JOSS paper. The reference list does include DOIs for all items in the generated PDF. This can be obtained as an artifact from workflow runs, the most recent is currently https://github.com/kyleaoman/martini/actions/runs/8402372561/artifacts/1352483918

[MicheleDelliVeneri]

HI @kyleaoman here it is my review, sorry for taking some time.
In short, my compliments for the great work.

Package Review

Please check off boxes as applicable, and elaborate in comments below. Your review is not limited to these topics, as described in the reviewer guide

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Documentation

The package includes all the following forms of documentation:

• A statement of need clearly stating problems the software is designed to solve and its target audience in README.
• Installation instructions: for the development version of the package and any non-standard dependencies in README.
• Vignette(s) demonstrating major functionality that runs successfully locally.
• Function Documentation: for all user-facing functions.
• Examples for all user-facing functions.
• Community guidelines including contribution guidelines in the README or CONTRIBUTING.
• Metadata including author(s), author e-mail(s), a url, and any other relevant metadata e.g., in a pyproject.toml file or elsewhere.

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The package meets the readme requirements below:

• Package has a README.md file in the root directory.

The README should include, from top to bottom:

• The package name
• Badges for:
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  • Docs building (if you have a documentation website),
  • 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.
• Package installation instructions
• Any additional setup required to use the package (authentication tokens, etc.)
• Descriptive links to all vignettes. If the package is small, there may only be a need for one vignette which could be placed in the README.md file.
  • Brief demonstration of package usage (as it makes sense - links to vignettes could also suffice here if package description is clear)
• Link to your documentation website.
• If applicable, how the package compares to other similar packages and/or how it relates to other packages in the scientific ecosystem.
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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:

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

Functionality

• Installation: Installation succeeds as documented.
• Functionality: Any functional claims of the software been confirmed.
• Performance: Any performance claims of the software been confirmed.
• Automated tests:
  • 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.
  • Tests cover essential functions of the package and a reasonable range of inputs and conditions.
• Continuous Integration: Has continuous integration setup (We suggest using Github actions but any CI platform is acceptable for review)
• Packaging guidelines: The package conforms to the pyOpenSci packaging guidelines.
  A few notable highlights to look at:
  • Package supports modern versions of Python and not End of life versions.
  • Code format is standard throughout package and follows PEP 8 guidelines (CI tests for linting pass)

For packages also submitting to JOSS

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

Note: Be sure to check this carefully, as JOSS's submission requirements and scope differ from pyOpenSci's in terms of what types of packages are accepted.

The package contains a paper.md matching JOSS's requirements with:

• A short summary describing the high-level functionality of the software
• Authors: A list of authors with their affiliations
• A statement of need clearly stating problems the software is designed to solve and its target audience.
• References: With DOIs for all those that have one (e.g. papers, datasets, software).

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:

---

Review Comments

All green from me, workflows have passed the tests on my local server, and I have run all tests locally.
All functions works as expected.

[hamogu]

@taldcroft Could you have a look and see if all your concerns are addressed and, if so, update your comment above by ticking the "final-approval" box?

[taldcroft]

@hamogu - Done!

[hamogu]

@MicheleDelliVeneri I noticed that you did not tick the box for "Package supports modern versions of Python", but I just checked, the tests run with Python version up to 3.12, so I think that's safe! I take the editors privilege to edit your review and tick that box before proceeding.

[hamogu]

---

🎉 has been approved by pyOpenSci! Thank you for submitting and many thanks to for reviewing this package! 😸

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• Change the status tag of the issue to 6/pyOS-approved6 🚀🚀🚀.
• Invite the package maintainer(s) and both reviewers to slack if they wish to join.
• If the author submits to JOSS, please continue to update the labels for JOSS on this issue until the author is accepted (do not remove the 6/pyOS-approved label). Once accepted add the label 9/joss-approved to the issue. Skip this check if the package is not submitted to JOSS.
• If the package is JOSS-accepted please add the JOSS doi to the YAML at the top of the issue.

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If you have any feedback for us about the review process please feel free to share it here. We are always looking to improve our process and documentation in the peer-review-guide.

[hamogu]

@kyleaoman Just to confirm: I see you did a very recent release on May15th. Is that the version that includes the responses to @taldcroft comments? Or do you plan to make a new release now that it's accepted? I just want to make sure I'm linking to the correct release number.

[kyleaoman]

@hamogu release 2.0.12 contains the latest commit so yes, that's up to date. There's also a Zenodo DOI for it. I'll do the JOSS submission and other little to-do items asap, probably tomorrow. Thanks!

[hamogu]

Congratulations! I switched to accepted - just remember to fill out the post-review survey and add a badge pointing to this issue, if you want to.

Also, we want to invite you to write a blog post (totally optional) on your package for us to promote your work! if you are interested - here are a few examples of other blog posts:

pandera
movingpandas

and here is a markdown example that you could use as a guide when creating your post.

If you are too busy for this no worries. But if you have time - we'd love to spread the word about your package!

Last, a huge thanks to @taldcroft and @MicheleDelliVeneri for their review - without reviewers like you PyOpenSci would not be possible.

Also, we want to invite all of you (authors and reviewers) you to join the PyOpenSci slack - it's a very active community (more than astropy!) with lots if technical discussion and people happy to help with packaging and related Python questions. Let me know if you are interested, and we'll send you an invite yo any email address you choose!

[kyleaoman]

I've completed the post-review survey, added the badge, and just now submitted to JOSS - will come back once that process has run its course :)