icon-grid-generator 0.3.0

Pure Python generation of ICON-style triangular grids

ICON Grid Generator

Pure Python generation of ICON-style triangular grids.

ICON Grid Generator creates spherical ICON R<n>B<k> grids, planar triangular grids, limited-area extracts, and ICON-style NetCDF files without depending on ICON model runtimes or stencil frameworks.

Quick Start

Most users only need generate_grid():

from grid_generator import generate_grid

grid = generate_grid("R2B4")
print(grid.name)
print(grid.dims)
grid.to_netcdf("icon_grid_R02B04.nc")

Example output:

R02B04
{'cell': 20480, 'vertex': 10242, 'edge': 30720}

Global grids are optimized by default and are suitable for normal ICON-style grid-file use. Use optimize_global=False only when you explicitly need the raw bisection topology for diagnostics or tests.

What You Can Generate

• Global spherical ICON grids from names such as R2B4 or R02B04.
• Planar triangular torus, channel, and parallelogram grids for experiments.
• Limited-area grids extracted from generated global parent grids.
• ICON-style NetCDF grid files when the optional netCDF4 dependency is installed.
• In-memory topology, geometry, metric, refinement, and metadata arrays for plotting, diagnostics, and downstream conversion.

Which Grid Should I Use?

Goal	Use
Standard spherical grid file	generate_grid("R2B4")
Raw topology checks	generate_grid("R2B4", optimize_global=False)
Periodic planar experiment	TorusGridSpec(...)
Open planar experiment	ChannelGridSpec(...) or ParallelogramGridSpec(...)
Regional extract from a global parent	LimitedAreaGridSpec(...)
Cut an existing in-memory grid	grid_generator.cutting.cut_grid(...)

Installation

Install from PyPI:

python -m pip install "icon-grid-generator[netcdf]"

From a local checkout:

python -m pip install -e .

Install optional NetCDF and xarray support with:

python -m pip install -e ".[netcdf,xarray]"

Install optional Numba acceleration support with:

python -m pip install -e ".[accelerate]"

Install development dependencies with:

python -m pip install -e ".[test,docs]"

Grid Naming

The ICON documentation describes grid file names with the generic nomenclature R<n>B<k>, where n is the number of root divisions and k is the number of subsequent bisections. ICON examples also commonly use zero-padded grid file names such as R02B06. This package accepts both compact names (R2B6) and zero-padded names (R02B06), then stores labels and metadata in the zero-padded form.

Resource Expectations

Global grid size grows by a factor of four with each bisection:

Grid	Cells	Edges	Vertices
R1B0	20	30	12
R1B1	80	120	42
R2B3	5,120	7,680	2,562
R2B4	20,480	30,720	10,242
R2B6	327,680	491,520	163,842

generate_grid() has a default safety limit of 2,000,000 cells. Set max_cells=None only when the allocation is intentional.

Common Recipes

Disable the default safety limit when a large allocation is intentional:

from grid_generator import generate_grid

grid = generate_grid("R2B4", max_cells=None)

Generate a raw diagnostic grid without global optimization:

raw_grid = generate_grid("R2B4", optimize_global=False)

Generate a planar torus grid:

from grid_generator import TorusGridSpec, generate_grid

grid = generate_grid(TorusGridSpec(nx=32, ny=16, edge_length=1_000.0))
print(grid.metadata["grid_geometry"])
print(grid.metadata["domain_length"])

Extract a limited-area grid from a generated global parent:

from grid_generator import LimitedAreaGridSpec, Region, generate_grid

spec = LimitedAreaGridSpec(
    parent="R02B03",
    region=Region.lonlat_box(lon_min=-20.0, lon_max=20.0, lat_min=35.0, lat_max=60.0),
    boundary_depth=2,
)
grid = generate_grid(spec, max_cells=None)
print(grid.dims)

Cut an existing grid with advanced region predicates:

from grid_generator import Region, generate_grid
from grid_generator.cutting import CutGridSpec, cut_grid

parent = generate_grid("R2B4")
cut = cut_grid(
    parent,
    CutGridSpec(regions=Region.circle(lon=8.0, lat=47.0, radius_degrees=10.0)),
)

For the common single-region case, pass the region directly:

from grid_generator import Region, generate_grid
from grid_generator.cutting import cut_grid

parent = generate_grid("R2B4")
cut = cut_grid(parent, Region.circle(lon=8.0, lat=47.0, radius_degrees=10.0))

NetCDF export requires the netcdf optional extra. See examples/write_global_grid.py, examples/write_limited_area.py, and examples/planar_torus.py for runnable scripts.

Documentation

The minimal documentation lives in docs:

• Overview
• Examples
• API overview
• Design notes
• Repository metadata

To preview the docs locally:

mkdocs serve

Development

See CONTRIBUTING.md for contribution guidelines, review expectations, and domain-specific requirements for grid math and NetCDF changes.

Run the checks used by CI:

make check

The package is laid out as a standalone Python project. If this directory is split out of a larger checkout, keep .github/, docs/, CITATION.cff, CHANGELOG.md, LICENSE, README.md, mkdocs.yml, pyproject.toml, src/, and tests/ at the new repository root.

Citation

If you use ICON Grid Generator in published work, cite it using CITATION.cff. For research releases, connect the public GitHub repository to Zenodo before creating a GitHub Release so a DOI can be minted.

Release History

See CHANGELOG.md.

License

ICON Grid Generator is distributed under the BSD 3-Clause License.