Metadata-Version: 2.4
Name: pyskylumos
Version: 0.0.5
Summary: A Python package for simulating skylight polarization sensor recordings using advanced polarization models (including Pan, Berry, and Rayleigh). Designed for researchers and engineers, this tool enables the generation of synthetic datasets for biomimetic navigation, machine learning, computer vision, and atmospheric optics. Ideal for developing and testing bio-inspired sensors, building training data for AI models, and exploring applications in robotics, remote sensing, and environmental monitoring.
Project-URL: Homepage, https://github.com/taciochi/pyskylumos
Project-URL: Issues, https://github.com/taciochi/pyskylumos/issues
Author-email: Teodor-Avram Ciochirca <sgtcioch@liverpool.ac.uk>, Daniel John Chadwick <sgdjohnc@liverpool.ac.uk>, Ian Sandall <isandall@liverpool.ac.uk>, Jason Francis Ralph <jfralph@liverpool.ac.uk>
Maintainer-email: Teodor-Avram Ciochirca <sgtcioch@liverpool.ac.uk>, Daniel John Chadwick <sgdjohnc@liverpool.ac.uk>
License-Expression: MIT
License-File: LICENSE
Keywords: athmospheric optics,bio-inspired,biomimetics,computer vision,dataset,environmental sensing,machine learning,navigation,remote sensing,robotics,sensor,simulation,skylight polarization,synthetic data
Classifier: Development Status :: 5 - Production/Stable
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Astronomy
Classifier: Topic :: Scientific/Engineering :: Image Processing
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.12.2
Requires-Dist: astropy>=7.1.0
Requires-Dist: numpy>=2.3.1
Description-Content-Type: text/markdown

# PySkyLumos

A Python package for simulating skylight polarization sensor recordings using advanced polarization models (including Pan, Berry, and Rayleigh). Designed for researchers and engineers, this tool enables the generation of synthetic datasets for biomimetic navigation, machine learning, computer vision, and atmospheric optics. Ideal for developing and testing bio-inspired sensors, building training data for AI models, and exploring applications in robotics, remote sensing, and environmental monitoring.

Documentation and others to be written.