xcdat.regridder.regrid2.Regrid2Regridder

class xcdat.regridder.regrid2.Regrid2Regridder(input_grid, output_grid, **options)

__init__(input_grid, output_grid, **options)

Pure python implementation of the regrid2 horizontal regridder from CDMS2’s regrid2 module.

Regrid data from input_grid to output_grid.

Available options: None

Parameters

• input_grid (xr.Dataset) – Dataset containing the source grid.

• output_grid (xr.Dataset) – Dataset containing the destination grid.

• options (Dict[str, Any]) – Dictionary with extra parameters for the regridder.

Examples

Import xCDAT:

>>> import xcdat
>>> from xcdat.regridder import regrid2

Open a dataset:

>>> ds = xcdat.open_dataset("ts.nc")

Create output grid:

>>> output_grid = xcdat.create_gaussian_grid(32)

Create regridder:

>>> regridder = regrid2.Regrid2Regridder(ds.grid, output_grid)

Regrid data:

>>> data_new_grid = regridder.horizontal("ts", ds)

Methods

__init__(input_grid, output_grid, **options)	Pure python implementation of the regrid2 horizontal regridder from CDMS2's regrid2 module.
horizontal(data_var, ds)	Regrid data_var in ds to output grid.

horizontal(data_var, ds)

Regrid data_var in ds to output grid.

Mappings and weights between input and output grid are calculated on the first call, allowing a regridder to be applied to many input datasets.

Parameters

• data_var (str) – The name of the data variable inside the dataset to regrid.

• ds (xr.Dataset) – The dataset containing data_var.

Returns

xr.Dataset – Dataset with variable on the destination grid.

Raises

KeyError – If data variable does not exist in the Dataset.

Examples

Create output grid:

>>> output_grid = xcdat.create_gaussian_grid(32)

Create regridder:

>>> regridder = regrid2.Regrid2Regridder(ds, output_grid)

Regrid data:

>>> data_new_grid = regridder.horizontal("ts", ds)

_output_axis_sizes(da)

Maps axes to output array sizes.

Parameters

da (xr.DataArray) – Data array containing variable to be regridded.

Returns

Dict – Mapping of axis name e.g. (“X”, “Y”, etc) to output sizes.

_regrid(input_data, axis_sizes, ordered_axis_names)

Applies regridding to input data.

Parameters

• input_data (np.ndarray) – Input multi-dimensional array on source grid.

• axis_sizes (Dict[str, int]) – Mapping of axis name e.g. (“X”, “Y”, etc) to output sizes.

• ordered_axis_names (List[str]) – List of axis name in order of dimensions of input_data.

Returns

np.ndarray – Multi-dimensional array on destination grid.

_base_put_indexes(axis_sizes)

Calculates the base indexes to place cell (0, 0).

Example: For a 3D array (time, lat, lon) with the shape (2, 2, 2) the offsets to place cell (0, 0) in each time step would be [0, 4].

For a 4D array (time, plev, lat, lon) with shape (2, 2, 2, 2) the offsets to place cell (0, 0) in each time step would be [0, 4, 8, 16].

Parameters

axis_sizes (Dict[str, int]) – Mapping of axis name e.g. (“X”, “Y”, etc) to output sizes.

Returns

np.ndarray – Array containing the base indexes to be used in np.put operations.

_create_output_dataset(input_ds, data_var, output_data, axis_variable_name_map, ordered_axis_names)

Creates the output Dataset containing the new variable on the destination grid.

Parameters

• input_ds (xr.Dataset) – Input dataset containing coordinates and bounds for unmodified axes.

• data_var (str) – The name of the regridded variable.

• output_data (np.ndarray) – Output data array.

• axis_variable_name_map (Dict[str, str]) – Map of axis name e.g. (“X”, “Y”, etc) to variable name e.g. (“lon”, “lat”, etc).

• ordered_axis_names (List[str]) – List of axis names in the order observed for output_data.

Returns

xr.Dataset – Dataset containing the variable on the destination grid.

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