A Python toolbox for the objective analysis (interpolation) of oceanographic and meteorological data, particularly for creating gridded fields from sparse observations.
octant
- Python
- Data Analytics, Ecology and Sustainability, Fluid Dynamics, Marine Science, Mathematics
- Environmental Engineering, Environmental Impact, Machine Learning, Predictive Maintenance Algorithms, Process Optimization, Quality Management, Statistical Analysis, User-Centered Design
Features:
- Objective analysis (Optimal Interpolation),gridding of sparse data,variational analysis,data assimilation (simple forms),error estimation for gridded fields,support for n-dimensional data,integration with NumPy and xarray,tools for handling oceanographic data
Pricing:
- Free
- Specialized for oceanographic/meteorological data interpolation, implements established objective analysis techniques, useful for creating consistent gridded datasets from observations.
- Niche application, smaller user community, development activity appears somewhat limited, documentation might be focused on specific use cases or assume familiarity with objective analysis methods.
Best for:
- Oceanographers and meteorologists needing to interpolate sparse observational data onto regular grids using objective analysis methods.