PySensors: A Python Package for Sparse Sensor Placement

TL;DR

PySensors is a Python package that implements algorithms for optimal sparse sensor placement to improve classification and reconstruction tasks, providing users with practical tools and theoretical insights.

Contribution

The paper introduces PySensors, a Python package that implements data-driven algorithms for sparse sensor placement, combining theoretical foundations with practical features.

Findings

Implemented algorithms for SSPOR and SSPOC in PySensors

Demonstrated sensor placement optimization through code examples

Provided practical advice and potential extensions for users

Abstract

PySensors is a Python package for selecting and placing a sparse set of sensors for classification and reconstruction tasks. Specifically, PySensors implements algorithms for data-driven sparse sensor placement optimization for reconstruction (SSPOR) and sparse sensor placement optimization for classification (SSPOC). In this work we provide a brief description of the mathematical algorithms and theory for sparse sensor optimization, along with an overview and demonstration of the features implemented in PySensors (with code examples). We also include practical advice for user and a list of potential extensions to PySensors. Software is available at https://github.com/dynamicslab/pysensors.