PySensors: A Python Package for Sparse Sensor Placement
This work provides a software package for researchers and practitioners in fields like signal processing or control systems to optimize sensor placement, but it is incremental as it implements existing algorithms without new methodological breakthroughs.
The authors tackled the problem of selecting and placing sparse sensors for classification and reconstruction tasks by developing PySensors, a Python package that implements algorithms for data-driven sparse sensor placement optimization, resulting in a publicly available software tool with code examples and practical guidance.
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.