from collections.abc import Hashable
from typing import Any, Literal, get_args
import xarray as xr
from xgcm import Grid
from xcdat._logger import _setup_custom_logger
from xcdat.axis import get_dim_coords
from xcdat.regridder.base import BaseRegridder, _preserve_bounds
XGCMVerticalMethods = Literal["linear", "conservative", "log"]
logger = _setup_custom_logger(__name__)
[docs]
class XGCMRegridder(BaseRegridder):
[docs]
def __init__(
self,
input_grid: xr.Dataset,
output_grid: xr.Dataset,
method: XGCMVerticalMethods = "linear",
target_data: str | xr.DataArray | None = None,
grid_positions: dict[str, str] | None = None,
periodic: bool = False,
extra_init_options: dict[str, Any] | None = None,
**options: Any,
):
"""
Extension of ``xgcm`` regridder.
The ``XGCMRegridder`` extends ``xgcm`` by automatically constructing the
``Grid`` object, transposing the output data to match the dimensional
order of the input data, and ensuring bounds and metadata are preserved
in the output dataset.
Linear and log methods require a single dimension position, which can
usually be automatically derived. A custom position can be specified using
the `grid_positions` argument.
Conservative regridding requires multiple dimension positions, e.g.,
{"center": "xc", "left": "xg"} which can be passed using the `grid_positions`
argument.
``xgcm.Grid`` can be passed additional arguments using ``extra_init_options``.
These arguments can be found on `XGCM's Grid documentation <https://xgcm.readthedocs.io/en/latest/api.html#xgcm.Grid.__init__>`_.
``xgcm.Grid.transform`` can be passed additional arguments using ``options``.
These arguments can be found on `XGCM's Grid.transform documentation <https://xgcm.readthedocs.io/en/latest/api.html#xgcm.Grid.transform>`_.
Parameters
----------
input_grid : xr.Dataset
Contains source grid coordinates.
output_grid : xr.Dataset
Contains destination grid coordinates.
method : XGCMVerticalMethods
Regridding method, by default "linear". Options are
- linear (default)
- log
- conservative
target_data : str | xr.DataArray | None
Data to transform onto, if this is a str it can be the key of a
variable in the input dataset or "infer" to automatically
determine the vertical coordinate. If it is an ``xr.DataArray``, it
should be a vertical coordinate that is compatible with the
output grid. If ``None``, then the source levels are mapped to the
destination levels.
grid_positions : dict[str, str] | None
Mapping of dimension positions, by default None. If ``None`` then an
attempt is made to derive this argument.
periodic : bool
Whether the grid is periodic, by default False.
extra_init_options : dict[str, Any] | None
Extra options passed to the ``xgcm.Grid`` constructor, by default
None.
options : dict[str, Any] | None
Extra options passed to the ``xgcm.Grid.transform`` method.
Raises
------
KeyError
If data variable does not exist in the Dataset.
ValueError
If ``method`` is not valid.
Examples
--------
Import xCDAT:
>>> import xcdat
Open a dataset:
>>> ds = xcdat.open_dataset("...")
Create output grid:
>>> output_grid = xcdat.create_grid(lev=np.linspace(1000, 1, 5))
Regrid data to ``output_grid``:
>>> output_data = ds.regridder.vertical(
>>> "so", output_grid, tool="xgcm", method="linear"
>>> )
Create pressure variable:
>>> ds["pressure"] = (ds["hyam"] * ds["P0"] + ds["hybm"] * ds["PS"]).transpose(
>>> **ds["T"].dims
>>> )
Regrid data to ``output_grid`` in pressure space:
>>> output_data = ds.regridder.vertical(
>>> "so", output_grid, tool="xgcm", method="linear", target_data="pressure"
>>> )
Passing additional arguments to ``xgcm.Grid`` and ``xgcm.Grid.transform``:
>>> regridder = xgcm.XGCMRegridder(
>>> ds,
>>> output_grid,
>>> method="linear",
>>> extra_init_options={"boundary": "fill", "fill_value": 1e27},
>>> mask_edges=True
>>> )
"""
super().__init__(input_grid, output_grid)
if method not in get_args(XGCMVerticalMethods):
raise ValueError(
f"{method!r} is invalid, possible choices: "
f"{', '.join(get_args(XGCMVerticalMethods))}"
)
self._method = method
self._target_data = target_data
self._grid_positions = grid_positions
if extra_init_options is None:
extra_init_options = {}
extra_init_options["periodic"] = periodic
self._extra_init_options = extra_init_options
self._extra_options = options
[docs]
def horizontal(self, data_var: str, ds: xr.Dataset) -> xr.Dataset:
"""Placeholder for base class."""
raise NotImplementedError()
[docs]
def vertical(self, data_var: str, ds: xr.Dataset) -> xr.Dataset:
"""See documentation in :py:func:`xcdat.regridder.xgcm.XGCMRegridder`"""
try:
output_coord_z = get_dim_coords(self._output_grid, "Z")
except KeyError as e:
raise RuntimeError(
"Could not determine 'Z' coordinate in output dataset"
) from e
if self._grid_positions is None:
grid_coords = self._get_grid_positions()
else:
# correctly format argument
grid_coords = {"Z": self._grid_positions}
# NOTE: xgcm v0.9.0 introduces the arg autoparse_metadata to the Grid
# constructor, which defaults to True. This is not compatible with
# manually passing metadata such as coords, so we set it to False.
grid = Grid(
ds, coords=grid_coords, autoparse_metadata=False, **self._extra_init_options
)
target_data: str | xr.DataArray | None = None
if isinstance(self._target_data, str) and self._target_data == "infer":
target_data = self._infer_target_data(ds)
else:
target_data = self._get_target_data(ds)
output_da = grid.transform(
ds[data_var],
"Z",
output_coord_z,
target_data=target_data,
method=self._method,
**self._extra_options,
)
# when the order of dimensions are mismatched, the output data will be
# transposed to match the input dimension order
if output_da.dims != ds[data_var].dims:
input_coord_z = get_dim_coords(ds[data_var], "Z")
output_order = [
x.replace(input_coord_z.name, output_coord_z.name) # type: ignore[attr-defined]
for x in ds[data_var].dims
]
output_da = output_da.transpose(*output_order)
if target_data is None:
output_da.attrs = ds[data_var].attrs.copy()
else:
output_da.attrs = target_data.attrs.copy()
output_ds = xr.Dataset({data_var: output_da}, attrs=ds.attrs.copy())
output_ds = _preserve_bounds(ds, self._output_grid, output_ds, ["Z"])
return output_ds
[docs]
def _infer_target_data(self, ds) -> xr.DataArray | None:
"""Infer and decode the target vertical coordinate from a dataset.
Attempts to extract the CF-compliant vertical ("Z") coordinate from the
provided xarray dataset, verifies the presence and validity of the
'formula_terms' attribute, and decodes the vertical coordinate using
CF conventions.
Parameters
----------
ds : xarray.Dataset
The input dataset from which to infer and decode the vertical
coordinate.
Returns
-------
xarray.DataArray or None
The decoded vertical coordinate as a DataArray if successful,
otherwise None.
Raises
------
RuntimeError
If the 'Z' coordinate is missing, not CF-compliant, or lacks a
valid 'formula_terms' attribute.
KeyError
If required variables for decoding the vertical coordinate are
missing from the dataset.
Notes
-----
This method relies on the dataset being CF-compliant and having
appropriate attributes on the vertical coordinate variable.
"""
try:
zcoord = ds.cf["Z"]
except KeyError as e:
raise RuntimeError(
"Missing 'Z' coordinate or CF-compliant attributes on the 'Z' coordinate "
"in the dataset. Ensure the dataset has a valid 'Z' coordinate."
) from e
if "formula_terms" not in zcoord.attrs:
raise RuntimeError(
f"Vertical coordinate {zcoord.name!r} is not CF-compliant, "
"missing 'formula_terms' attribute."
)
elif zcoord.attrs["formula_terms"] == "":
raise RuntimeError(
f"Vertical coordinate {zcoord.name!r} is not CF-compliant, "
"empty 'formula_terms' attribute."
)
# will raise keyerror if there's a missing variable
ds.cf.decode_vertical_coords(outnames={zcoord.name: "decoded_vertical_coord"})
return ds.decoded_vertical_coord
[docs]
def _get_target_data(self, ds) -> xr.DataArray | None:
"""Retrieve the target data from the given xarray Dataset.
Attempts to access the target data variable from the provided dataset
using the attribute `self._target_data`. If `self._target_data` is a
string and not found in the dataset, raises a RuntimeError. If
`self._target_data` is not a string or is None, returns None. If a
ValueError occurs, returns `self._target_data` as is.
Parameters
----------
ds : xr.Dataset
The xarray Dataset from which to retrieve the target data.
Returns
-------
xr.DataArray or None
The target data as an xarray DataArray if found, otherwise None.
Raises
------
RuntimeError
If `self._target_data` is a string and not found in the dataset.
"""
try:
target_data = ds[self._target_data]
except ValueError:
target_data = self._target_data
except KeyError as e:
if self._target_data is not None and isinstance(self._target_data, str):
raise RuntimeError(
f"Could not find target variable {self._target_data!r} in dataset"
) from e
target_data = None
return target_data
[docs]
def _get_grid_positions(self) -> dict[str, Any | Hashable]:
"""
Determine the grid point positions for the "Z" axis in the input grid.
Inspects the input grid to infer the position of the "Z" coordinate
(e.g., center, left, or right) based on its relationship to the
corresponding bounds. Raises informative errors if the method is not
supported, if the "Z" coordinate or its bounds cannot be determined, or
if the position cannot be inferred automatically.
Returns
-------
dict[str, Any | Hashable]
Mapping of the "Z" axis to its grid position,
e.g., {"Z": {"center": <coord_name>}}.
Raises
------
RuntimeError
If conservative regridding is requested, if the "Z"
coordinate or bounds cannot be determined, if multiple "Z" axes are
found, or if the grid position cannot be inferred.
"""
if self._method == "conservative":
raise RuntimeError(
"Conservative regridding requires a second point position, pass these "
"manually."
)
try:
coord_z = get_dim_coords(self._input_grid, "Z")
except KeyError as e:
raise RuntimeError("Could not determine `Z` coordinate in dataset.") from e
if isinstance(coord_z, xr.Dataset):
coords = ", ".join(sorted(list(coord_z.coords.keys()))) # type: ignore[arg-type]
raise RuntimeError(
"Could not determine the `Z` coordinate in the input grid. "
f"Found multiple axes ({coords}), ensure there is only a "
"single `Z` axis in the input grid.",
list(coord_z.coords.keys()),
)
try:
bounds_z = self._input_grid.bounds.get_bounds("Z")
except KeyError as e:
raise RuntimeError("Could not determine `Z` bounds in dataset.") from e
# handle simple point positions based on point and bounds
if (coord_z[0] > bounds_z[0][0] and coord_z[0] < bounds_z[0][1]) or (
coord_z[0] < bounds_z[0][0] and coord_z[0] > bounds_z[0][1]
):
grid_positions = {"center": coord_z.name}
elif coord_z[0] == bounds_z[0][0]:
grid_positions = {"left": coord_z.name}
elif coord_z[0] == bounds_z[0][1]:
grid_positions = {"right": coord_z.name}
else:
raise RuntimeError(
"Could not determine the grid point positions, pass these manually "
"using the `grid_positions` argument"
)
return {"Z": grid_positions}
