[FOSS Review] PySensors 2.0: A Python Package for Sparse Sensor Placement

[isdanni]

This issue is used for FOSS submission review: openjournals/joss-reviews#9265

Paper

• In the New Features section: the sentence "The objective that the QR optimizer is based on suffers from two limitations, it is not defined for the under-sampling..." is slightly unclear and may require further explanation.
• grammar, spelling, and stylistic errors:
  • In the Statement of Need section, "Other sensor placment packages such as": "placment" should be "placement".
  • There is a mixed usage of PySensors and Pysensors. Please use PySensors throughout the paper for consistency, especially for referring to the software package.
  • "Ref.[@klishin2023data]" should have a space after "Ref.".

Please apply the review suggestions to your paper and upload a rendered PDF version, including the complete reference list, to the GitHub repository.

[isdanni]

Documentation

For pysensors README.md:

• Please add navigation links for each section in the Table of Contents to improve structure and navigation, given that this README is longer than average.
• Use consistent markdown font size to make each section stand out visually. For example, ## Installation, ## Features
• Move a minimal ## Quick Start section to the top, showing a basic install and first example; this helps new users get started faster.
  • In addition, in the Installation section, the environment requirements for package installation could use more clarification. For example:

## Requirements

- Python 3.8, 3.9, 3.10, or 3.11
- NumPy >= 1.20
- SciPy >= 1.6
- scikit-learn >= 0.24

• State which operating systems have been tested. For example, “Tested on Ubuntu 20.04/22.04 and MacOS 12; not tested on Windows”.
• Contributing guidelines can be a separate doc, such as CONTRIBUTING.md, and linked in README.md for visibility.

[niharika2999]

Thank you so much for your review of the repository and the submitted paper. We have addressed your valuable comments as below:
Paper:

• To clarify the statement, we have added the following lines in the manuscript: In the context of reduced order models with truncation rank r, over-sampling refers to deploying more sensors than the dimensionality of the reduced subspace (i.e., p > r sensors), while under-sampling involves using fewer sensors than the model rank (p < r). The objective that the QR optimizer is based on suffers from two limitations, it is not defined for the under-sampling regime, and the underlying optimization is difficult to interpret and visualize directly. Ref.[@klishin2023data] resolves these with a new optimization technique that accounts for over- and under- sampling scenarios in sensor placement and decomposing the resulting objective into sums over the placed sensors,
• Thank you so much for catching those grammatical and spelling errors. We have fixed both of them.

Documentation

• Within the repository README, navigational links exist for each section. The README is written in .rst and not .md. This is clarified in the GitHub repository https://github.com/dynamicslab/pysensors.
• In this README.rst, the font sizes are consistent.
• Basic installation guidelines and example links already exist in this README. The contributing guidelines are easily accessible through the navigational links, and further clarification for contributions is provided in the README. (https://github.com/dynamicslab/pysensors?tab=readme-ov-file#installation, https://github.com/dynamicslab/pysensors?tab=readme-ov-file#id12)
• An additional section to specify which OS systems have Pysensors been tested on can be found here: https://github.com/dynamicslab/pysensors?tab=readme-ov-file#id7

Thank you so much.