[REVIEW]: fABBA: A Python library for the fast symbolic approximation of time series #6294

editorialbot · 2024-01-29T09:25:12Z

Submitting author: @chenxinye (Xinye Chen)
Repository: https://github.com/nla-group/fABBA
Branch with paper.md (empty if default branch): master
Version: 1.2.1
Editor: @lrnv
Reviewers: @Karangupta1994, @allie-tatarian
Archive: 10.5281/zenodo.10885652

Status

Status badge code:

HTML: <a href="https://joss.theoj.org/papers/bd028bbee6f04f9ec2ad0b1034997c61"><img src="https://joss.theoj.org/papers/bd028bbee6f04f9ec2ad0b1034997c61/status.svg"></a>
Markdown: [![status](https://joss.theoj.org/papers/bd028bbee6f04f9ec2ad0b1034997c61/status.svg)](https://joss.theoj.org/papers/bd028bbee6f04f9ec2ad0b1034997c61)

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Reviewer instructions & questions

@Karangupta1994 & @allie-tatarian, your review will be checklist based. Each of you will have a separate checklist that you should update when carrying out your review.
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Checklists

📝 Checklist for @allie-tatarian

📝 Checklist for @Karangupta1994

The text was updated successfully, but these errors were encountered:

editorialbot · 2024-01-29T09:25:15Z

Hello humans, I'm @editorialbot, a robot that can help you with some common editorial tasks.

For a list of things I can do to help you, just type:

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For example, to regenerate the paper pdf after making changes in the paper's md or bib files, type:

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editorialbot · 2024-01-29T09:25:34Z

Software report:

github.com/AlDanial/cloc v 1.88  T=1.29 s (75.8 files/s, 270557.5 lines/s)
-------------------------------------------------------------------------------
Language                     files          blank        comment           code
-------------------------------------------------------------------------------
C                               12          16359          74932         195168
Python                          29           1657           2655           3294
Jupyter Notebook                18              0          50227           2514
Cython                          12            337            494            561
Markdown                         7            138              0            349
reStructuredText                 9            244            236            152
TeX                              2             14              0            128
YAML                             4             40             24             83
CSS                              1             18             15             57
DOS Batch                        1              8              1             26
Bourne Shell                     1              2              0             18
make                             1              4              7              9
TOML                             1              0              0              3
-------------------------------------------------------------------------------
SUM:                            98          18821         128591         202362
-------------------------------------------------------------------------------


gitinspector failed to run statistical information for the repository

editorialbot · 2024-01-29T09:25:37Z

Wordcount for paper.md is 875

editorialbot · 2024-01-29T09:26:23Z

Reference check summary (note 'MISSING' DOIs are suggestions that need verification):

OK DOIs

- None

MISSING DOIs

- 10.1007/s10618-020-00689-6 may be a valid DOI for title: ABBA: adaptive Brownian bridge-based symbolic aggregation of time series
- 10.1109/tit.1982.1056489 may be a valid DOI for title: Least squares quantization in PCM
- 10.1145/882082.882086 may be a valid DOI for title: A symbolic representation of time series, with implications for streaming algorithms
- 10.1145/3532622 may be a valid DOI for title: An efficient aggregation method for the symbolic representation of temporal data
- 10.1016/j.is.2023.102294 may be a valid DOI for title: ECG classification with learning ensemble based on symbolic discretization
- 10.1007/978-3-031-24378-3_4 may be a valid DOI for title: Fast Time Series Classification with Random Symbolic Subsequences
- 10.1007/s10618-007-0064-z may be a valid DOI for title: Experiencing SAX: a novel symbolic representation of time series
- 10.1109/seed55351.2022.00016 may be a valid DOI for title: Foreseer: Efficiently Forecasting Malware Event Series with Long Short-Term Memory
- 10.1016/j.ress.2023.109123 may be a valid DOI for title: Data-driven prognostics based on time-frequency analysis and symbolic recurrent neural network for fuel cells under dynamic load
- 10.1145/3448672 may be a valid DOI for title: A Framework for Generating Summaries from Temporal Personal Health Data

INVALID DOIs

- None

editorialbot · 2024-01-29T09:26:52Z

👉📄 Download article proof 📄 View article proof on GitHub 📄 👈

lrnv · 2024-01-29T09:27:17Z

👋🏼 @chenxinye @Karangupta1994 @allie-tatarian this is the review thread for the paper. All of our communications will happen here from now on.

As a reviewer, the first step is to create a checklist for your review by entering

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These checklists contain the JOSS requirements. As you go over the submission, please check any items that you feel have been satisfied. The first comment in this thread also contains links to the JOSS reviewer guidelines.

The JOSS review is different from most other journals. Our goal is to work with the authors to help them meet our criteria instead of merely passing judgment on the submission. As such, the reviewers are encouraged to submit issues and pull requests on the software repository. When doing so, please mention openjournals/joss-reviews#REVIEW_NUMBER so that a link is created to this thread (and I can keep an eye on what is happening). Please also feel free to comment and ask questions on this thread. In my experience, it is better to post comments/questions/suggestions as you come across them instead of waiting until you've reviewed the entire package.

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chenxinye · 2024-01-31T11:36:39Z

@lrnv Thanks for the arrangement!

allie-tatarian · 2024-02-01T17:28:03Z

Hi all, I'm not sure if this is the best place to this but I don't think it makes sense to post this as an issue because I think it's a problem on my end. When I try to use fABBA, I am consistently getting a "This installation is not using Cython" warning even though I do have Cython installed. @chenxinye is this something you have seen before? I cannot for the life of me figure out how to troubleshoot this. The fABBA library is still mostly working for me, but sometimes I get different outputs than the examples.

lrnv · 2024-02-01T17:32:51Z

@allie-tatarian Different output than the examples surely looks like an issue (unless the examples are explicitely random ?). Problem in the installation can also be reported as issues.

@Karangupta1994 & @allie-tatarian, to guide you in the review, there is an automated checklist that you have to produce by commenting in this thread :

@editorialbot generate my checklist

I think the standard checkpionts includes stuff such as the need for descriptive installation guidelines if some requirements are "non-standard".

More generally, this is a very good place to discuss this thread is here for that purpose

allie-tatarian · 2024-02-01T17:49:38Z

Thanks @lrnv for the input, I opened an issue on the project page, but I think I am going to try using fABBA again on a different computer just in case it is an issue with the security settings on this one.

chenxinye · 2024-02-01T19:34:41Z

Hi @lrnv, I just sorted the doi in bib, many thanks!

chenxinye · 2024-02-01T19:41:14Z

@allie-tatarian

I just checked, found some output in README.md is consistent with the real output of software. I now corrected it! Please check.

chenxinye · 2024-02-01T19:43:54Z

Thanks for pointing the issues.

For the Cython issue, @lrnv @allie-tatarian, some users reported before, most of them have this issue since their machine have firewall or without VC++ installed. Could you please provide your machine specifications for me?

I checked my three machines, all worked properly. I guess you use Windows without VC++ installed?

allie-tatarian · 2024-02-01T20:34:46Z

Thanks @chenxinye, I do have VC++ installed, so I think the issue is with the firewall on my work computer. I will try everything again on my personal computer tomorrow and report back!

chenxinye · 2024-02-01T20:59:21Z

You would need to install fABBA using:

pip install fABBA

allie-tatarian · 2024-02-02T15:27:16Z

Thanks @chenxinye, like I mentioned in the issue I did install using pip. It is working fine on my personal computer, so I think the issue is that the security on my work computer is not meshing well with cython. I wonder if it's worth putting a note about that in the docs for fABBA - I do think the issue is with cython, not fABBA, but looking around online it looks like I'm not the only one who has run into this issue and I haven't seen anyone spell out the solution as concisely as you did here!

chenxinye · 2024-02-02T18:16:40Z

Hi @allie-tatarian Thanks for your suggestions! That's really a good idea, I will put a note there!

chenxinye · 2024-02-02T21:46:58Z

@allie-tatarian Just added in https://fabba.readthedocs.io/en/latest/quickstart.html ! Many thanks

allie-tatarian · 2024-02-06T15:36:54Z

Review checklist for @allie-tatarian

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 https://github.com/nla-group/fABBA?
License: Does the repository contain a plain-text LICENSE or COPYING file with the contents of an OSI approved software license?
Contribution and authorship: Has the submitting author (@chenxinye) made major contributions to the software? Does the full list of paper authors seem appropriate and complete?
Substantial scholarly effort: Does this submission meet the scope eligibility described in the JOSS guidelines
Data sharing: If the paper contains original data, data are accessible to the reviewers. If the paper contains no original data, please check this item.
Reproducibility: If the paper contains original results, results are entirely reproducible by reviewers. If the paper contains no original results, please check this item.
Human and animal research: If the paper contains original data research on humans subjects or animals, does it comply with JOSS's human participants research policy and/or animal research policy? If the paper contains no such data, please check this item.

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: Does the paper have a section titled 'Statement of need' that clearly states what problems the software is designed to solve, who the target audience is, and its relation to other work?
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?

allie-tatarian · 2024-02-06T15:43:56Z

I did run into some issues when I was trying out the JABBA package, trying to follow the examples both in the README and the docs, but then I realized that JABBA isn't in the paper so I did the peer review following the functionality that is listed in the paper. @lrnv can you let me know if you think that is off-base?

lrnv · 2024-03-27T12:24:57Z

@editorialbot check references

editorialbot · 2024-03-27T12:26:01Z

👉📄 Download article proof 📄 View article proof on GitHub 📄 👈

editorialbot · 2024-03-27T12:26:10Z

Reference check summary (note 'MISSING' DOIs are suggestions that need verification):

OK DOIs

- 10.1007/s10618-020-00689-6 is OK
- 10.1109/tit.1982.1056489 is OK
- 10.1145/882082.882086 is OK
- 10.1145/3532622 is OK
- 10.1016/j.is.2023.102294 is OK
- 10.1007/978-3-031-24378-3_4 is OK
- 10.1007/s10618-007-0064-z is OK
- 10.1109/seed55351.2022.00016 is OK
- 10.48550/arXiv.2003.05672 is OK
- 10.1016/j.ress.2023.109123 is OK
- 10.1145/3448672 is OK

MISSING DOIs

- No DOI given, and none found for title: Scikit-learn: Machine Learning in Python

INVALID DOIs

- None

lrnv · 2024-03-27T12:26:35Z

@editorialbot recommend-accept

editorialbot · 2024-03-27T12:26:37Z

Attempting dry run of processing paper acceptance...

editorialbot · 2024-03-27T12:27:52Z

Reference check summary (note 'MISSING' DOIs are suggestions that need verification):

OK DOIs

- 10.1007/s10618-020-00689-6 is OK
- 10.1109/tit.1982.1056489 is OK
- 10.1145/882082.882086 is OK
- 10.1145/3532622 is OK
- 10.1016/j.is.2023.102294 is OK
- 10.1007/978-3-031-24378-3_4 is OK
- 10.1007/s10618-007-0064-z is OK
- 10.1109/seed55351.2022.00016 is OK
- 10.48550/arXiv.2003.05672 is OK
- 10.1016/j.ress.2023.109123 is OK
- 10.1145/3448672 is OK

MISSING DOIs

- No DOI given, and none found for title: Scikit-learn: Machine Learning in Python

INVALID DOIs

- None

editorialbot · 2024-03-27T12:28:37Z

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

Check final proof 👉📄 Download article

If the paper PDF and the deposit XML files look good in openjournals/joss-papers#5181, then you can now move forward with accepting the submission by compiling again with the command @editorialbot accept

chenxinye · 2024-03-27T13:00:36Z

Hi @lrnv I would need to add some comments in Acknowledgement, can I?

``Stefan Güttel acknowledges a Royal Society Industry Fellowship IF/R1/231032. Xinye Chen is supported by the European Union (ERC, inEXASCALE, 101075632). Views and opinions expressed are those of the authors only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them.''

Can I?

lrnv · 2024-03-27T13:23:56Z

Yes you can of course.

lrnv · 2024-03-27T13:24:10Z

Just regenerate the paper after that, and I'll re-recomand-accept

chenxinye · 2024-03-27T17:13:35Z

Hi @lrnv We just proofreaded, now it is okay for us.

Many thanks

chenxinye · 2024-03-27T17:14:06Z

@editorialbot generate pdf

editorialbot · 2024-03-27T17:15:31Z

👉📄 Download article proof 📄 View article proof on GitHub 📄 👈

lrnv · 2024-03-27T17:49:19Z

@editorialbot recomand-accept

editorialbot · 2024-03-27T17:49:21Z

I'm sorry human, I don't understand that. You can see what commands I support by typing:

@editorialbot commands

lrnv · 2024-03-27T17:49:33Z

@editorialbot recommend-accept

editorialbot · 2024-03-27T17:49:35Z

Attempting dry run of processing paper acceptance...

editorialbot · 2024-03-27T17:50:16Z

Reference check summary (note 'MISSING' DOIs are suggestions that need verification):

OK DOIs

- 10.1007/s10618-020-00689-6 is OK
- 10.1109/tit.1982.1056489 is OK
- 10.1145/882082.882086 is OK
- 10.1145/3532622 is OK
- 10.1016/j.is.2023.102294 is OK
- 10.1007/978-3-031-24378-3_4 is OK
- 10.1007/s10618-007-0064-z is OK
- 10.1109/seed55351.2022.00016 is OK
- 10.48550/arXiv.2003.05672 is OK
- 10.1016/j.ress.2023.109123 is OK
- 10.1145/3448672 is OK

MISSING DOIs

- No DOI given, and none found for title: Scikit-learn: Machine Learning in Python

INVALID DOIs

- None

editorialbot · 2024-03-27T17:51:52Z

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

Check final proof 👉📄 Download article

If the paper PDF and the deposit XML files look good in openjournals/joss-papers#5187, then you can now move forward with accepting the submission by compiling again with the command @editorialbot accept

arfon · 2024-03-30T14:15:49Z

@editorialbot accept

editorialbot · 2024-03-30T14:15:51Z

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

editorialbot · 2024-03-30T14:23:12Z

Ensure proper citation by uploading a plain text CITATION.cff file to the default branch of your repository.

If using GitHub, a Cite this repository menu will appear in the About section, containing both APA and BibTeX formats. When exported to Zotero using a browser plugin, Zotero will automatically create an entry using the information contained in the .cff file.

You can copy the contents for your CITATION.cff file here:

CITATION.cff

cff-version: "1.2.0"
authors:
- family-names: Chen
  given-names: Xinye
  orcid: "https://orcid.org/0000-0003-1778-393X"
- family-names: Güttel
  given-names: Stefan
  orcid: "https://orcid.org/0000-0003-1494-4478"
doi: 10.5281/zenodo.10885652
message: If you use this software, please cite our article in the
  Journal of Open Source Software.
preferred-citation:
  authors:
  - family-names: Chen
    given-names: Xinye
    orcid: "https://orcid.org/0000-0003-1778-393X"
  - family-names: Güttel
    given-names: Stefan
    orcid: "https://orcid.org/0000-0003-1494-4478"
  date-published: 2024-03-30
  doi: 10.21105/joss.06294
  issn: 2475-9066
  issue: 95
  journal: Journal of Open Source Software
  publisher:
    name: Open Journals
  start: 6294
  title: "fABBA: A Python library for the fast symbolic approximation of
    time series"
  type: article
  url: "https://joss.theoj.org/papers/10.21105/joss.06294"
  volume: 9
title: "*fABBA*: A Python library for the fast symbolic approximation of
  time series"

If the repository is not hosted on GitHub, a .cff file can still be uploaded to set your preferred citation. Users will be able to manually copy and paste the citation.

Find more information on .cff files here and here.

editorialbot · 2024-03-30T14:23:17Z

🐘🐘🐘 👉 Toot for this paper 👈 🐘🐘🐘

editorialbot · 2024-03-30T14:23:17Z

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

Here's what you must now do:

Check final PDF and Crossref metadata that was deposited 👉 Creating pull request for 10.21105.joss.06294 joss-papers#5195
Wait five minutes, then verify that the paper DOI resolves https://doi.org/10.21105/joss.06294
If everything looks good, then close this review issue.
Party like you just published a paper! 🎉🌈🦄💃👻🤘

Any issues? Notify your editorial technical team...

arfon · 2024-03-30T14:54:29Z

@Karangupta1994, @allie-tatarian – many thanks for your reviews here and to @lrnv for editing this submission! JOSS relies upon the volunteer effort of people like you and we simply wouldn't be able to do this without you ✨

@chenxinye – your paper is now accepted and published in JOSS ⚡🚀💥

editorialbot · 2024-03-30T14:54:30Z

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

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HTML:
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  <img src="https://joss.theoj.org/papers/10.21105/joss.06294/status.svg" alt="DOI badge" >
</a>

reStructuredText:
.. image:: https://joss.theoj.org/papers/10.21105/joss.06294/status.svg
   :target: https://doi.org/10.21105/joss.06294

This is how it will look in your documentation:

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editorialbot added C++ Cython Python review TeX Track: 5 (DSAIS) Data Science, Artificial Intelligence, and Machine Learning labels Jan 29, 2024

editorialbot assigned lrnv Jan 29, 2024

editorialbot mentioned this issue Jan 29, 2024

[PRE REVIEW]: fABBA: A Python library for the fast symbolic approximation of time series #6132

Closed

lrnv mentioned this issue Feb 1, 2024

Missing DOIs in bib nla-group/fABBA#5

Closed

allie-tatarian mentioned this issue Feb 2, 2024

Erroneous "This installation is not using Cython" message nla-group/fABBA#4

Closed

editorialbot added the recommend-accept Papers recommended for acceptance in JOSS. label Mar 27, 2024

editorialbot added accepted published Papers published in JOSS labels Mar 30, 2024

arfon closed this as completed Mar 30, 2024

[REVIEW]: fABBA: A Python library for the fast symbolic approximation of time series #6294

[REVIEW]: fABBA: A Python library for the fast symbolic approximation of time series #6294

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Review checklist for @allie-tatarian

Conflict of interest

Code of Conduct

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Documentation

Software paper

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