tda-mapper presubmission

[lucasimi]

Submitting Author: Luca Simi (@lucasimi)
Package Name: tda-mapper
One-Line Description of Package: A Python library based on the Mapper algorithm for Topological Data Analysis.
Repository Link (if existing): https://github.com/lucasimi/tda-mapper-python
EiC: @SimonMolinsky

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Code of Conduct & Commitment to Maintain Package

• I agree to abide by pyOpenSci's Code of Conduct during the review process and in maintaining my package after should it be accepted.
• I have read and will commit to package maintenance after the review as per the pyOpenSci Policies Guidelines.

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.

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Scope

• Please indicate which category or categories this package falls under:

  • Data retrieval
  • Data extraction
  • Data processing/munging
  • Data deposition
  • Data validation and testing
  • Data visualization
  • Workflow automation
  • Citation management and bibliometrics
  • Scientific software wrappers
  • Database interoperability

Domain Specific

• Geospatial
• Education

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• Explain how and why the package falls under these categories (briefly, 1-2 sentences). For community partnerships, check also their specific guidelines as documented in the links above. Please note any areas you are unsure of: 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 the 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 similar things? 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.

• Any other questions or issues we should be aware of:

  1. The methodology of the Mapper algorithm can be found in the original paper.
  2. The methodology used in this library is covered in the preprint.
  3. A short presentation of the methodology used in this library was held at the "Fourth Edition of the Young Applied Mathematicians Conference", and is present in the book of abstracts of the conference.

P.S. Have feedback/comments about our review process? Leave a comment here

[SimonMolinsky]

Hi @lucasimi

I'm checking your package and will get back to you with the comments this week!

[lucasimi]

Hi @SimonMolinsky,

Thank you for taking care of this! If you have any question let me know.

[SimonMolinsky]

Hi @lucasimi

I've checked your package. I was wondering about the relation of tda-mapper to other packages, but you clarified it in your preprint, and for me, it is more than enough!

Other baseline requirements (scope, general package and documentation structure, scientific need, license) are met.

You can submit your package for the peer review. I will close this issue when you create review submission.