[REVIEW]: archetypal: A Python package for collecting, simulating, converting and analyzing building archetypes

[whedon]

Submitting author: @samuelduchesne (Samuel Letellier-Duchesne)
Repository: https://github.com/samuelduchesne/archetypal
Version: v1.3
Editor: @kyleniemeyer
Reviewer: @brynpickering, @ThibaultMarzullo
Archive: 10.5281/zenodo.3885587

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Reviewers and authors:

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Review checklist for @brynpickering

Conflict of interest

• I confirm that I have read the JOSS conflict of interest (COI) policy and that: I have no COIs with reviewing this work or that any perceived COIs have been waived by JOSS for the purpose of this review.

Code of Conduct

• I confirm that I read and will adhere to the JOSS code of conduct.

General checks

• Repository: Is the source code for this software available at the repository url?
• License: Does the repository contain a plain-text LICENSE file with the contents of an OSI approved software license?
• Contribution and authorship: Has the submitting author (@samuelduchesne) made major contributions to the software? Does the full list of paper authors seem appropriate and complete?

Functionality

• Installation: Does installation proceed as outlined in the documentation?
• Functionality: Have the functional claims of the software been confirmed?
• Performance: If there are any performance claims of the software, have they been confirmed? (If there are no claims, please check off this item.)

Documentation

• A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
• Installation instructions: Is there a clearly-stated list of dependencies? Ideally these should be handled with an automated package management solution.
• Example usage: Do the authors include examples of how to use the software (ideally to solve real-world analysis problems).
• Functionality documentation: Is the core functionality of the software documented to a satisfactory level (e.g., API method documentation)?
• Automated tests: Are there automated tests or manual steps described so that the functionality of the software can be verified?
• Community guidelines: Are there clear guidelines for third parties wishing to 1) Contribute to the software 2) Report issues or problems with the software 3) Seek support

Software paper

• Summary: Has a clear description of the high-level functionality and purpose of the software for a diverse, non-specialist audience been provided?
• A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
• State of the field: Do the authors describe how this software compares to other commonly-used packages?
• Quality of writing: Is the paper well written (i.e., it does not require editing for structure, language, or writing quality)?
• References: Is the list of references complete, and is everything cited appropriately that should be cited (e.g., papers, datasets, software)? Do references in the text use the proper citation syntax?

Review checklist for @ThibaultMarzullo

Conflict of interest

• I confirm that I have read the JOSS conflict of interest (COI) policy and that: I have no COIs with reviewing this work or that any perceived COIs have been waived by JOSS for the purpose of this review.

Code of Conduct

• I confirm that I read and will adhere to the JOSS code of conduct.

General checks

• Repository: Is the source code for this software available at the repository url?
• License: Does the repository contain a plain-text LICENSE file with the contents of an OSI approved software license?
• Contribution and authorship: Has the submitting author (@samuelduchesne) made major contributions to the software? Does the full list of paper authors seem appropriate and complete?

Functionality

• Installation: Does installation proceed as outlined in the documentation?
• Functionality: Have the functional claims of the software been confirmed?
• Performance: If there are any performance claims of the software, have they been confirmed? (If there are no claims, please check off this item.)

Documentation

• A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
• Installation instructions: Is there a clearly-stated list of dependencies? Ideally these should be handled with an automated package management solution.
• Example usage: Do the authors include examples of how to use the software (ideally to solve real-world analysis problems).
• Functionality documentation: Is the core functionality of the software documented to a satisfactory level (e.g., API method documentation)?
• Automated tests: Are there automated tests or manual steps described so that the functionality of the software can be verified?
• Community guidelines: Are there clear guidelines for third parties wishing to 1) Contribute to the software 2) Report issues or problems with the software 3) Seek support

Software paper

• Summary: Has a clear description of the high-level functionality and purpose of the software for a diverse, non-specialist audience been provided?
• A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
• State of the field: Do the authors describe how this software compares to other commonly-used packages?
• Quality of writing: Is the paper well written (i.e., it does not require editing for structure, language, or writing quality)?
• References: Is the list of references complete, and is everything cited appropriately that should be cited (e.g., papers, datasets, software)? Do references in the text use the proper citation syntax?

[whedon]

Hello human, I'm @whedon, a robot that can help you with some common editorial tasks. @brynpickering, @ThibaultMarzullo it looks like you're currently assigned to review this paper 🎉.

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[whedon]

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[kyleniemeyer]

👋 @samuelduchesne @brynpickering @ThibaultMarzullo the actual review takes place in here. Please see the instructions above. Thanks!

[ThibaultMarzullo]

Hi @samuelduchesne, I'm going through the paper and the software now. It's an interesting idea and work.

Paper:
It would benefit from the addition of some context as well as a couple of clarifications, see this issue for more details.

Documentation:
Generally speaking it seems incomplete, the user is left to finding out on their own how to use archetypal for any task other than converting models. See this issue for more details.

I'll get back to you as soon as I run the tests, the only issue is that I won't be able to test the TRNSYS model since I don't own a license.

[brynpickering]

Hey @samuelduchesne, just had a chance to go through this. A tool to help collate building archetypes and work on collections of models seems incredibly useful, but I've come across a few issues that would need addressing to make sure it is a sufficiently robust piece of software for general use. It looks like neither myself or @ThibaultMarzullo will be able to test the TRNSYS functionality, so some demonstration from your end might be necessary to convince us on that...?

MacOS compatibility

see samuelduchesne/archetypal#42 and samuelduchesne/archetypal#45

I'm not sure this has actually been tested on MacOS. The installation instructions do cover it, but I have hit a few roadblocks along the way, and some of the inbuilt tests fail. I would consider a conda forge version of archetype, where you can do OS-specific testing, for a more robust package.

EnergyPlus compatibility

see samuelduchesne/archetypal#43

Given that E+ is such an integral part of your software, it would be good to have a better handle on allowed E+ versions and dealing with a. IDF files without explicit versions and b. use of v.9.2.0 (current latest E+ version, currently fails).

Documentation

see samuelduchesne/archetypal#44

This is currently seriously lacking, with errors in the little example code that there is. There seems to be a lot of functionality in the software, but it is all lost due to lack of tutorial documentation, and limited API method documentation. Using e.g. in-built E+ example models / DOE benchmark models to show off loading in multiple files, comparing DataFrames, comparing solve times, etc. is really necessary to help users understand the software and its benefits. As is, I don't feel able to really play around with the software as I don't know what most of the possibilities are without delving into each function in the core code.

Tests and graceful failure

see samuelduchesne/archetypal#46, samuelduchesne/archetypal#43 and samuelduchesne/archetypal#45

I think tests are currently lacking, since I've seemingly broken the software already on multiple occasions (some of which is MacOS-specific). In some instances, I would expect to see a much clearer error message (e.g. E+ version issues), which requires catching and processing some of the more likely strange ways a user could abuse the software. But in other cases, the package fails when following the documentation...

Paper

see samuelduchesne/archetypal#47
I'm happy with the paper, I get a good grasp of what I could do with archetypal, and you cite sources for the methods which underpin what you do. It might be nice to see a table of performance metrics to compare running a new E+ model, cached E+ model, and 'shoeboxed' E+ model, to show the relevant benefits of key archetypal functionality.

[samuelduchesne]

Hi @brynpickering and @ThibaultMarzullo! Sorry for the late reply on my end. I have been quite busy with the submission of my thesis this week. Now it is done! so I can commit to this review process! 💪

You have provided valuable comments and uncovered issues we weren't able to pick even with our CI integration. If you do not mind letting us some time to get over all the issues, we will try to address them as soon as possible.

For the issue of TRNSYS, how about we show a proof of use as a screencast video maybe? @louisleroy5, do you have some thoughts? Could be included in the documentation 🤔

[samuelduchesne]

@whedon generate pdf

[whedon]

Attempting PDF compilation. Reticulating splines etc...

[whedon]

👉 Check article proof 📄 👈

[kyleniemeyer]

Hi @samuelduchesne, just wanted to check in and see how your work was going.

[samuelduchesne]

Hi @kyleniemeyer, we are making good progress! We received valuable comments and are addressing them. Thanks for checking with us!

[samuelduchesne]

@whedon generate pdf from branch develop

[whedon]

Attempting PDF compilation from custom branch develop. Reticulating splines etc...

[whedon]

👉 Check article proof 📄 👈

[samuelduchesne]

@whedon generate pdf from branch develop

[whedon]

Attempting PDF compilation from custom branch develop. Reticulating splines etc...

[whedon]

👉 Check article proof 📄 👈

[samuelduchesne]

Yes I’ll try to wrap things up by the end of the month. Happy to see JOSS is fully back online!

[samuelduchesne]

@brynpickering, I have addressed the caching in samuelduchesne/archetypal#87

[kyleniemeyer]

@whedon generate pdf

[whedon]

👉 Check article proof 📄 👈

[kyleniemeyer]

Hi @samuelduchesne, I think we're ready to wrap things up here.

Can you make these final small edits to the paper?

• In the summary paragraph, the sentence

archetypal is a Python package that helps handling collections of such archetypes and to enabling the interoperability between these energy simulation platforms to accelerate the creation of reliable urban building energy models.
seems incorrect, because of the "and to enabling" (it's also a bit long and complicated). I think changing that to "to enable" helps fix it, but you might consider revising the sentence overall for clarify.

• in the paragraph about caching, I think API should be capitalized.

Once you've made those changes, please archive your repository on Zenodo and provide the DOI here.

[samuelduchesne]

Hi @kyleniemeyer, thanks for the feedback. Paper updated with samuelduchesne/archetypal@e9232f2 and following DOI for zenodo files: 10.5281/zenodo.3885587

[kyleniemeyer]

@whedon generate pdf

[whedon]

👉 Check article proof 📄 👈

[kyleniemeyer]

@whedon set 10.5281/zenodo.3885587 as archive

[whedon]

OK. 10.5281/zenodo.3885587 is the archive.

[kyleniemeyer]

@whedon accept

[whedon]

Attempting dry run of processing paper acceptance...

[whedon]

Reference check summary:

OK DOIs

- 10.1016/j.enbuild.2017.01.030 is OK
- 10.1016/j.buildenv.2015.12.001 is OK
- 10.2172/90674 is OK

MISSING DOIs

- None

INVALID DOIs

- None

[whedon]

👋 @openjournals/joss-eics, this paper is ready to be accepted and published.

Check final proof 👉 openjournals/joss-papers#1471

If the paper PDF and Crossref deposit XML look good in openjournals/joss-papers#1471, then you can now move forward with accepting the submission by compiling again with the flag deposit=true e.g.

@whedon accept deposit=true

[kyleniemeyer]

@whedon accept deposit=true

[whedon]

Doing it live! Attempting automated processing of paper acceptance...

[whedon]

🐦🐦🐦 👉 Tweet for this paper 👈 🐦🐦🐦

[whedon]

🚨🚨🚨 THIS IS NOT A DRILL, YOU HAVE JUST ACCEPTED A PAPER INTO JOSS! 🚨🚨🚨

Here's what you must now do:

0. Check final PDF and Crossref metadata that was deposited 👉 Creating pull request for 10.21105.joss.01833 joss-papers#1472
1. Wait a couple of minutes to verify that the paper DOI resolves https://doi.org/10.21105/joss.01833
2. If everything looks good, then close this review issue.
3. Party like you just published a paper! 🎉🌈🦄💃👻🤘

Any issues? notify your editorial technical team...

[samuelduchesne]

🎉🎉🎉!

[kyleniemeyer]

Congrats @samuelduchesne on your article's publication in JOSS!

Many thanks to @brynpickering and @ThibaultMarzullo for reviewing this submission.

[whedon]

🎉🎉🎉 Congratulations on your paper acceptance! 🎉🎉🎉

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[kyleniemeyer]

@samuelduchesne FYI, it looks like the PDF isn't yet showing up on the journal—that should just be a temporary issue

[danielskatz]

It's there for me

[samuelduchesne]

Same here. Thank you everyone!

[samuelduchesne]

I signed up to become a reviewer 😃