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[PRE REVIEW]: fABBA: A Python library for the fast symbolic approximation of time series #6132
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Five most similar historical JOSS papers: dfba: Software for efficient simulation of dynamic flux-balance analysis models in Python pyFBS: A Python package for Frequency Based Substructuring FreqAI: generalizing adaptive modeling for chaotic time-series market forecasts BATMAN: Statistical analysis for expensive computer codes made easy ECabc: A feature tuning program focused on Artificial Neural Network hyperparameters |
@chenxinye – thanks for your submission to JOSS. When including the C code, this looks to be a very large submission. Could you clarify which aspects of this submission you are asking the JOSS reviewers to review? Also, we're currently managing a large backlog of submissions and the editor most appropriate for your area is already rather busy. For now, we will need to waitlist this paper and process it as the queue reduces. Thanks for your patience! |
@arfon Thanks! fABBA is a native Python library and is completely driven by Python code, which has no reliance on C++ at all, so we just hope JOSS reviewers can help to review the Python code, on which our fABBA library is based. We provide native C++ implementation just for C++ users for reference. |
@arfon, I just removed the C++ code to for clarifying. Please let us know if you have any questions! Many thanks |
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Thanks for clarifying. Just to confirm, the C code doesn't need reviewing either? |
Yes @arfon, many thanks! |
@editorialbot invite @lrnv as editor 👋 @lrnv – would you be willing to edit this submission for JOSS? / cc @vissarion for visibility. |
Invitation to edit this submission sent! |
@editorialbot assign me as editor |
Assigned! @lrnv is now the editor |
@chenxinye Hey ! Would you please take a look at the list of reviewers to potentially identify a few that would be suitable to review this submission ? |
Hi @lrnv , I suggest @allie-tatarian @@Karangupta1994 @djmannion @dgerosa @wmvanvliet? It would be nice if you can refer to some more. Many thanks, |
👋 @allie-tatarian @@Karangupta1994 @djmannion @dgerosa @wmvanvliet, would any of you be willing to review this submission for JOSS? We carry out our checklist-driven reviews here in GitHub issues and follow these guidelines: https://joss.readthedocs.io/en/latest/review_criteria.html |
I'm a bit too busy at the moment to be a reviewer for this one. Project looks good though! |
Hi Oskar,
Sure, I will be happy to review. Please let me know how to proceed.
Thank you,
Karan
…On Mon, Jan 22, 2024 at 4:27 AM Oskar Laverny ***@***.***> wrote:
👋 @allie-tatarian <https://github.com/allie-tatarian> @@Karangupta1994
<https://github.com/Karangupta1994> @djmannion
<https://github.com/djmannion> @dgerosa <https://github.com/dgerosa>
@wmvanvliet <https://github.com/wmvanvliet>, would any of you be willing
to review this submission for JOSS? We carry out our checklist-driven
reviews here in GitHub issues and follow these guidelines:
https://joss.readthedocs.io/en/latest/review_criteria.html
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@Karangupta1994 Ok great ! Basically instructions will come later when we identified at least another reviewer. |
@editorialbot add @Karangupta1994 as reviewer |
@Karangupta1994 added to the reviewers list! |
Hi, I can help review this paper. Thanks! |
@allie-tatarian Great thanks ! I'll add you up and we will wait a bit more in case a third invited person manifests. |
@editorialbot add @allie-tatarian as reviewer |
@allie-tatarian added to the reviewers list! |
Apologies, but I won't be able to be a reviewer for this paper. |
@editorialbot start review |
OK, I've started the review over in #6294. |
Submitting author: @chenxinye (Xinye Chen)
Repository: https://github.com/nla-group/fABBA
Branch with paper.md (empty if default branch): master
Version: v1.1.0
Editor: @lrnv
Reviewers: @Karangupta1994, @allie-tatarian
Managing EiC: Arfon Smith
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