Nereus Documentation
Nereus is a Python library for quick data exploration and analysis of unstructured atmospheric and ocean model data in Jupyter notebooks.
Key Features
Fast regridding from unstructured to regular grids using KD-tree based nearest neighbor interpolation
Interactive plotting with Cartopy projections for quick data visualization
Model-specific support for FESOM2, MITgcm, NEMO, IFS TCO, and HEALPix grids
Diagnostic functions for sea ice, ocean heat content, and Hovmoller diagrams
Dask integration for efficient handling of large datasets
Automatic caching of interpolation weights for repeated operations
Quick Example
import nereus as nr
import xarray as xr
# Load your unstructured data
ds = xr.open_dataset("fesom_output.nc")
# Plot with a single line
fig, ax, interp = nr.plot(
ds.temp.isel(time=0, nz1=0),
ds.lon, ds.lat,
projection="rob",
cmap="RdBu_r"
)
# Compute sea ice area
ice_area = nr.ice_area(ds.a_ice, mesh.area)
Installation
pip install nereus
# Or with conda/mamba
conda install -c conda-forge nereus
Contents
Getting Started
User Guide
- Plotting Guide
- Regridding Guide
- Basic Regridding
- Flexible Input Formats
- Automatic Coordinate Extraction
- xarray Output
- Interpolation Methods
- Resolution Options
- Grid Bounds
- The Influence Radius
- Using RegridInterpolator Directly
- Handling Multi-Dimensional Data
- Automatic Caching
- Fill Values
- Saving Regridded Data
- Performance Tips
- Comparison with Other Tools
- Diagnostics Guide
- Model Support Guide
API Reference
Development