fatiando/verde

Processing and gridding spatial data, machine-learning style

What it solves

Verde solves the problem of processing and interpolating spatial data—such as topography, point clouds, and geophysics surveys—onto a 2D surface (gridding). It provides a consistent, machine-learning-inspired interface to handle both scalar and vector data across Cartesian and geographic coordinates.

How it works

Verde implements an interface similar to scikit-learn, allowing users to apply various interpolation (gridding) methods. It integrates deeply with the SciPy stack (NumPy, Pandas, scikit-learn, and xarray) to process spatial data and perform analysis tasks like trend removal and blocked/windowed operations.

Who it’s for

It is designed for researchers and developers working with spatial data, such as those in geophysics, bathymetry, and topography studies.

Highlights

  • ML-inspired Interface: Uses a scikit-learn-like API for spatial interpolation.
  • Broad Data Support: Handles scalar and vector data (e.g., wind speed, GPS velocities).
  • Coordinate Systems: Supports both Cartesian and geographic coordinates.
  • Integrated Analysis: Includes tools for trend removal, cross-validation, and blocked means.
  • SciPy Stack Integration: Works seamlessly with NumPy, Pandas, and xarray.

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