Core Features

x4c attaches a .x accessor to xarray.Dataset and xarray.DataArray that understands CESM conventions — where the grid-cell areas live, how to find latitude and longitude on an unstructured grid, and how the vertical coordinate is spaced.

Every notebook below runs against the reduced sample case bundled with these docs, so they work from a fresh clone. See the sample case README for what was trimmed out of it. Each notebook is a single, self-contained example — small on purpose, so a page never mixes unrelated features.

• Overview — the .x accessor and what it attaches

• Annualization and seasonalization — .x.annualize(), including DJF wraparound

• Seasonal maps — DJF vs JJA, composed with .x.plot()

• Spatial means and climate indices — area-weighted .x.gm, .x.nhm, .x.shm

• Regridding: atmosphere — SE grid to regular lat/lon

• Regridding: ocean — POP grid to regular lat/lon

• Hybrid levels to pressure levels — .x.get_plev()

• Model coastlines for a paleo run — the ssv trick

• Map projections at a glance — any Cartopy projection

• Contour levels, colormaps, gridlines

• Regional maps and saving figures — latlon_range, x4c.savefig()

• Vertical sections — the meridional overturning circulation

• Zonal means — a Hovmöller-style contour

• Timeseries

• Publication styles — x4c.set_style()

• EOF analysis — grid predicates, .x.eof()

• Extracting sites — .x.nearest2d(), .x.nearest3d()

• Saving derived fields — .x.to_netcdf()