xugrid.UgridDataset.from_structured#

static UgridDataset.from_structured(dataset: Dataset, topology: dict | None = None) → UgridDataset[source]#

Create a UgridDataset from a (structured) xarray Dataset.

The spatial dimensions are flattened into a single UGRID face dimension.

By default, this method looks for:

  1. "x" and "y" dimensions.

  2. "longitude" and "latitude" dimensions.

  3. "axis" attributes of “X” or “Y” on coordinates.

  4. "standard_name" attributes of “longitude”, “latitude”, “projection_x_coordinate”, or “project_y_coordinate” on coordinate variables.

Specify the x and y coordinate names explicitly otherwise, see the examples.

Parameters:

  • dataset (xr.Dataset)

  • topology (dict, optional, default is None.) – Mapping of topology name to x and y coordinate variables. If None, defaults to {"mesh2d": (None, None)}.

Returns:

unstructured

Return type:

UgridDataset

Examples

By default, this method will look for "x" and "y" coordinates and returns a UgriDataset with a Ugrid topology named mesh2d:

>>> uds = xugrid.UgridDataset.from_structured(dataset)

In case of other names, the name of the resulting UGRID topology and the x and y coordinates must be specified:

>>> uds = xugrid.UgridDataset.from_structured(
>>>     dataset,
>>>     topology={"my_mesh2d": ("xc", "yc")},
>>> )

In case of multiple grid topologies in a single dataset, the names must be specified as well:

>>> uds = xugrid.UgridDataset.from_structured(
>>>     dataset,
>>>     topology={"mesh2d_xy": ("x", "y"), "mesh2d_lonlat": {"lon", "lat"},
>>> )