tda-mapper submission

[lucasimi]

Submitting Author: (@lucasimi)
All current maintainers: (@lucasimi)
Package Name: tda-mapper
One-Line Description of Package: A Python library implementing the Mapper algorithm for Topological Data Analysis.
Repository Link: https://github.com/lucasimi/tda-mapper-python
Version submitted: v0.9.0
EiC: Szymon Moliński (@SimonMolinsky )
Editor: TBD
Reviewer 1: TBD
Reviewer 2: TBD
Archive: TBD
JOSS DOI: TBD
Version accepted: TBD
Date accepted (month/day/year): TBD

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Description

• Include a brief paragraph describing what your package does: tda-mapper is a Python library that provides an efficient implementation of the Mapper algorithm, a powerful tool for topological data analysis. The algorithm transforms high-dimensional and complex datasets into graph representations, that are visualized through interactive plots, allowing users to explore hidden patterns, relationships, and structures within the data.

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

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existing community please check below:

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• 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):

  This library falls under the categories of "data processing/munging" and "data visualization" because it uses the Mapper algorithm to transform complex datasets into network representations, enabling users to process, analyze, and visually explore underlying structures and relationships.

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

  This package is aimed at researchers and data scientists engaged in exploratory data analysis. The Mapper algorithm is particularly useful in the early stages of data exploration, helping to uncover patterns and structures that guide further, more detailed analysis. It has been successfully applied in diverse fields, including social sciences, biology, and machine learning, to gain insights into complex datasets.

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

  Several Python packages, such as GUDHI, giotto-tda, and Kepler Mapper, offer implementations of the Mapper algorithm. However, tda-mapper differs from them by prioritizing performance and scalability in higher-dimensional spaces. Specifically, it efficiently computes Mapper on high-dimensional "lenses" that are computationally challenging for traditional methods. This approach not only enables the handling of larger and more complex datasets but also results in Mapper graphs that are easier to interpret and navigate. The approach used by tda-mapper scales better with dimension, making it faster and more responsive for interactive explorations compared to conventional techniques.

• 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:

  This is the link to the pre-submission enquiry: tda-mapper presubmission #219. I should also report that the methodology that this package is based on is explained more deeply in the preprint and is currently under review for publication in a peer-review scientific journal.

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Footnotes

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

[SimonMolinsky]

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Editor comments

[SimonMolinsky]

Hi @lucasimi

Your package is in excellent condition; we can move forward! I will start searching for an editor - but you should expect a delay because of the upcoming holidays and New Year's celebrations.

[lucasimi]

Hi @SimonMolinsky, thank you for the update and your commitment. I completely understand about the delay. Please take your time; there's no rush on my side.