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Solshade is a Python toolkit for terrain-aware solar illumination modeling. It combines high-resolution digital elevation models with accurate orbital solar geometry to provide detailed estimates of how sunlight interacts with complex terrain.
Features¶
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Terrain analysis - Compute slope, aspect, surface normals, hillshade maps, and horizon profiles directly from DEMs.
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Solar modeling - Generate per-pixel solar altitude/azimuth tracks and compute terrain-aware solar flux time series.
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Energy metrics - Derive integrated daily energy, total energy, peak energy, and day-of-peak maps for any region.
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Outputs & visualization - Export results as GeoTIFFs for GIS workflows, or as publication-ready plots (PNG/SVG).
Interactive plotting commands help visualize DEMs, slopes, aspects, horizons, and flux maps.
Potential Use Cases¶
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Environmental and climate research
Quantify how shading and illumination patterns affect snow/ice melt, permafrost stability, or vegetation growth. Could be of particular interest to studing and identifying microhabitats in extreme environments -
Astronomy & observatories
Understand site illumination patterns for telescope siting in remote mountainous regions. -
Archaeology & cultural heritage
Reconstruct ancient sunlight exposure of landscapes and structures. -
Renewable energy siting
Assess solar resource potential for photovoltaic installations, particularly in rugged or polar terrain. -
Education & outreach
Provide a clear, hands-on way to explore the interaction of topography and solar geometry.
Getting Started¶
Check out the Installation Guide and the Getting Started Guide for installation and basic usage examples. You can also dive into the API Reference to explore documentation straight from the code.
Project Goals¶
Solshade is designed with a few key principles in mind:
- Accuracy — Leverages high-quality DEMs and precise orbital ephemerides.
- Reproducibility — Provides tested command-line tools and Python APIs.
- Flexibility — Works for small test DEMs or large-area regional modeling.
- Open science — 100% open-source, designed to integrate with the scientific Python ecosystem.
Contributing¶
Contributions are always welcome! If you’d like to add features, improve performance, or help with documentation, please check out the repository on GitHub: