xcdat.regridder.regrid2.Regrid2Regridder#

class xcdat.regridder.regrid2.Regrid2Regridder(input_grid, output_grid, **options)[source]#
__init__(input_grid, output_grid, **options)[source]#

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)[source]#

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)[source]#

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)[source]#

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)[source]#

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)[source]#

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.

_abc_impl = <_abc._abc_data object>#