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Merged
VeckoTheGecko merged 11 commits into from
May 29, 2026
Merged

Add SGRID metadata to generic datasets #2647

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0e01707
Add datasets_sgrid["2d_left_rotated"]
VeckoTheGecko May 27, 2026
284185c
Add SGRID metadata to normal datasets
VeckoTheGecko May 27, 2026
f569149
Disable setting of "SGRID" in "Conventions" field
VeckoTheGecko May 28, 2026
3393372
Set Conventions attr
VeckoTheGecko May 27, 2026
010d6db
Add ds_2d_outer and ds_2d_inner
VeckoTheGecko May 27, 2026
a8709e3
Rename `datasets_comodo` to `datasets`
VeckoTheGecko May 28, 2026
adc8069
Update datasets_sgrid definitions
VeckoTheGecko May 28, 2026
fdd6a71
Refactor datasets_sgrid
VeckoTheGecko May 28, 2026
018ccc9
Add SGRID metadata to 2d_left_unrolled_cone
VeckoTheGecko May 28, 2026
e76e8b8
Remove 2d_left_rotated from datasets_sgrid
VeckoTheGecko May 28, 2026
ad0d84a
Fix docs
VeckoTheGecko May 28, 2026
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4 changes: 2 additions & 2 deletions docs/development/posting-issues.md
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Original file line number Diff line number Diff line change
Expand Up @@ -46,8 +46,8 @@ As a user with access to your dataset, you would do:

# Generate an example dataset to zip. The user would use their own.
import xarray as xr
from parcels._datasets.structured.generic import datasets_comodo
datasets_comodo['ds_2d_left'].to_netcdf("my_dataset.nc")
from parcels._datasets.structured.generic import datasets
datasets['ds_2d_left'].to_netcdf("my_dataset.nc")
```

```{code-cell}
Expand Down
2 changes: 1 addition & 1 deletion src/parcels/_core/xgrid.py
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Expand Up @@ -40,7 +40,7 @@ def _drop_field_data(ds: xr.Dataset) -> xr.Dataset:
when passed to the XGCM grid, the object only functions as an in memory representation
of the grid.
"""
return ds.drop_vars(ds.data_vars)
return ds.drop_vars(set(ds.data_vars) - {"grid"}) # don't drop sgrid metadata
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@VeckoTheGecko VeckoTheGecko May 28, 2026

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This function will be removed in a future PR anyway (once from_dataset is removed)



def assert_all_field_dims_have_axis(da: xr.DataArray, xgcm_grid: xgcm.Grid) -> None:
Expand Down
277 changes: 185 additions & 92 deletions src/parcels/_datasets/structured/generic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@

from . import T, X, Y, Z

__all__ = ["T", "X", "Y", "Z", "datasets_comodo", "datasets_sgrid"]
__all__ = ["T", "X", "Y", "Z", "datasets", "datasets_sgrid"]

TIME = xr.date_range("2000", "2001", T)

Expand Down Expand Up @@ -139,8 +139,21 @@ def _unrolled_cone_curvilinear_grid():
)


datasets_comodo = {
"2d_left_rotated": _rotated_curvilinear_grid(),
datasets = {
"2d_left_rotated": _rotated_curvilinear_grid().pipe(
sgrid._attach_sgrid_metadata,
sgrid.SGrid2DMetadata(
cf_role="grid_topology",
topology_dimension=2,
node_dimensions=("XG", "YG"),
face_dimensions=(
sgrid.FaceNodePadding("XC", "XG", sgrid.Padding.HIGH),
sgrid.FaceNodePadding("YC", "YG", sgrid.Padding.HIGH),
),
node_coordinates=("lon", "lat"),
vertical_dimensions=(sgrid.FaceNodePadding("ZC", "ZG", sgrid.Padding.HIGH),),
),
),
"ds_2d_left": xr.Dataset( # MITgcm indexing style
{
"data_g": (["time", "ZG", "YG", "XG"], np.random.rand(T, Z, Y, X)),
Expand Down Expand Up @@ -182,6 +195,19 @@ def _unrolled_cone_curvilinear_grid():
"depth": (["ZG"], np.arange(Z)),
"time": (["time"], TIME, {"axis": "T"}),
},
).pipe(
sgrid._attach_sgrid_metadata,
sgrid.SGrid2DMetadata(
cf_role="grid_topology",
topology_dimension=2,
node_dimensions=("XG", "YG"),
face_dimensions=(
sgrid.FaceNodePadding("XC", "XG", sgrid.Padding.HIGH),
sgrid.FaceNodePadding("YC", "YG", sgrid.Padding.HIGH),
),
node_coordinates=("lon", "lat"),
vertical_dimensions=(sgrid.FaceNodePadding("ZC", "ZG", sgrid.Padding.HIGH),),
),
),
"ds_2d_right": xr.Dataset( # NEMO indexing style
{
Expand Down Expand Up @@ -224,105 +250,172 @@ def _unrolled_cone_curvilinear_grid():
"depth": (["ZG"], np.arange(Z)),
"time": (["time"], TIME, {"axis": "T"}),
},
),
"2d_left_unrolled_cone": _unrolled_cone_curvilinear_grid(),
}

_COMODO_TO_2D_SGRID = { # Note "2D SGRID" here is meant in the context of SGRID convention (i.e., 1D depth)
"XG": "node_dimension1",
"YG": "node_dimension2",
"XC": "face_dimension1",
"YC": "face_dimension2",
"ZG": "vertical_dimensions_dim1",
"ZC": "vertical_dimensions_dim2",
}
datasets_sgrid = {
"ds_2d_padded_high": (
datasets_comodo["ds_2d_left"]
.pipe(
sgrid._attach_sgrid_metadata,
sgrid.SGrid2DMetadata(
cf_role="grid_topology",
topology_dimension=2,
node_dimensions=("XG", "YG"),
face_dimensions=(
sgrid.FaceNodePadding("XC", "XG", sgrid.Padding.HIGH),
sgrid.FaceNodePadding("YC", "YG", sgrid.Padding.HIGH),
),
node_coordinates=("lon", "lat"),
vertical_dimensions=(sgrid.FaceNodePadding("ZC", "ZG", sgrid.Padding.HIGH),),
).pipe(
sgrid._attach_sgrid_metadata,
sgrid.SGrid2DMetadata(
cf_role="grid_topology",
topology_dimension=2,
node_dimensions=("XG", "YG"),
face_dimensions=(
sgrid.FaceNodePadding("XC", "XG", sgrid.Padding.LOW),
sgrid.FaceNodePadding("YC", "YG", sgrid.Padding.LOW),
),
)
.sgrid.rename(
_COMODO_TO_2D_SGRID,
)
node_coordinates=("lon", "lat"),
vertical_dimensions=(sgrid.FaceNodePadding("ZC", "ZG", sgrid.Padding.LOW),),
),
),
"ds_2d_padded_low": (
datasets_comodo["ds_2d_right"]
.pipe(
sgrid._attach_sgrid_metadata,
sgrid.SGrid2DMetadata(
cf_role="grid_topology",
topology_dimension=2,
node_dimensions=("XG", "YG"),
face_dimensions=(
sgrid.FaceNodePadding("XC", "XG", sgrid.Padding.LOW),
sgrid.FaceNodePadding("YC", "YG", sgrid.Padding.LOW),
),
node_coordinates=("lon", "lat"),
vertical_dimensions=(sgrid.FaceNodePadding("ZC", "ZG", sgrid.Padding.LOW),),
),
)
.sgrid.rename(
_COMODO_TO_2D_SGRID,
)
),
"ds_2d_padded_none": xr.Dataset(
"ds_2d_inner": xr.Dataset(
{
"data_g": (["node_dimension1", "node_dimension2"], np.random.rand(10, 10)),
"data_c": (["face_dimension1", "face_dimension2"], np.random.rand(9, 9)),
"grid": (
[],
np.array(0),
sgrid.SGrid2DMetadata(
cf_role="grid_topology",
topology_dimension=2,
node_dimensions=("node_dimension1", "node_dimension2"),
face_dimensions=(
sgrid.FaceNodePadding("face_dimension1", "node_dimension1", sgrid.Padding.NONE),
sgrid.FaceNodePadding("face_dimension2", "node_dimension2", sgrid.Padding.NONE),
),
node_coordinates=("lon", "lat"),
).to_attrs(),
),
"data_g": (["time", "ZG", "YG", "XG"], np.random.rand(T, Z, Y, X)),
"data_c": (["time", "ZC", "YC", "XC"], np.random.rand(T, Z - 1, Y - 1, X - 1)),
"U_A_grid": (["time", "ZG", "YG", "XG"], np.random.rand(T, Z, Y, X)),
"V_A_grid": (["time", "ZG", "YG", "XG"], np.random.rand(T, Z, Y, X)),
"U_C_grid": (["time", "ZG", "YC", "XG"], np.random.rand(T, Z, Y - 1, X)),
"V_C_grid": (["time", "ZG", "YG", "XC"], np.random.rand(T, Z, Y, X - 1)),
},
coords={
"lon": (["node_dimension1"], np.linspace(0, 1, 10)),
"lat": (["node_dimension2"], np.linspace(0, 1, 10)),
"XG": (
["XG"],
2 * np.pi / X * np.arange(0, X),
{"axis": "X", "c_grid_axis_shift": 0.5},
),
"XC": (["XC"], 2 * np.pi / X * (np.arange(0, X - 1) - 0.5), {"axis": "X"}),
"YG": (
["YG"],
2 * np.pi / (Y) * np.arange(0, Y),
{"axis": "Y", "c_grid_axis_shift": 0.5},
),
"YC": (
["YC"],
2 * np.pi / (Y) * (np.arange(0, Y - 1) - 0.5),
{"axis": "Y"},
),
"ZG": (
["ZG"],
np.arange(Z),
{"axis": "Z", "c_grid_axis_shift": 0.5},
),
"ZC": (
["ZC"],
np.arange(Z - 1) - 0.5,
{"axis": "Z"},
),
"lon": (["XG"], 2 * np.pi / X * np.arange(0, X)),
"lat": (["YG"], 2 * np.pi / (Y) * np.arange(0, Y)),
"depth": (["ZG"], np.arange(Z)),
"time": (["time"], TIME, {"axis": "T"}),
},
).pipe(
sgrid._attach_sgrid_metadata,
sgrid.SGrid2DMetadata(
cf_role="grid_topology",
topology_dimension=2,
node_dimensions=("XG", "YG"),
face_dimensions=(
sgrid.FaceNodePadding("XC", "XG", sgrid.Padding.BOTH),
sgrid.FaceNodePadding("YC", "YG", sgrid.Padding.BOTH),
),
node_coordinates=("lon", "lat"),
vertical_dimensions=(sgrid.FaceNodePadding("ZC", "ZG", sgrid.Padding.BOTH),),
),
),
"ds_2d_padded_both": xr.Dataset(
"ds_2d_outer": xr.Dataset(
{
"data_g": (["node_dimension1", "node_dimension2"], np.random.rand(10, 10)),
"data_c": (["face_dimension1", "face_dimension2"], np.random.rand(11, 11)),
"grid": (
[],
np.array(0),
sgrid.SGrid2DMetadata(
cf_role="grid_topology",
topology_dimension=2,
node_dimensions=("node_dimension1", "node_dimension2"),
face_dimensions=(
sgrid.FaceNodePadding("face_dimension1", "node_dimension1", sgrid.Padding.BOTH),
sgrid.FaceNodePadding("face_dimension2", "node_dimension2", sgrid.Padding.BOTH),
),
node_coordinates=("lon", "lat"),
).to_attrs(),
),
"data_g": (["time", "ZG", "YG", "XG"], np.random.rand(T, Z, Y, X)),
"data_c": (["time", "ZC", "YC", "XC"], np.random.rand(T, Z + 1, Y + 1, X + 1)),
"U_A_grid": (["time", "ZG", "YG", "XG"], np.random.rand(T, Z, Y, X)),
"V_A_grid": (["time", "ZG", "YG", "XG"], np.random.rand(T, Z, Y, X)),
"U_C_grid": (["time", "ZG", "YC", "XG"], np.random.rand(T, Z, Y + 1, X)),
"V_C_grid": (["time", "ZG", "YG", "XC"], np.random.rand(T, Z, Y, X + 1)),
},
coords={
"lon": (["node_dimension1"], np.linspace(0, 1, 10)),
"lat": (["node_dimension2"], np.linspace(0, 1, 10)),
"XG": (
["XG"],
2 * np.pi / X * np.arange(0, X),
{"axis": "X", "c_grid_axis_shift": -0.5},
),
"XC": (["XC"], 2 * np.pi / X * (np.arange(0, X + 1) + 0.5), {"axis": "X"}),
"YG": (
["YG"],
2 * np.pi / (Y) * np.arange(0, Y),
{"axis": "Y", "c_grid_axis_shift": -0.5},
),
"YC": (
["YC"],
2 * np.pi / (Y) * (np.arange(0, Y + 1) + 0.5),
{"axis": "Y"},
),
"ZG": (
["ZG"],
np.arange(Z),
{"axis": "Z", "c_grid_axis_shift": -0.5},
),
"ZC": (
["ZC"],
np.arange(Z + 1) + 0.5,
{"axis": "Z"},
),
"lon": (["XG"], 2 * np.pi / X * np.arange(0, X)),
"lat": (["YG"], 2 * np.pi / (Y) * np.arange(0, Y)),
"depth": (["ZG"], np.arange(Z)),
"time": (["time"], TIME, {"axis": "T"}),
},
).pipe(
sgrid._attach_sgrid_metadata,
sgrid.SGrid2DMetadata(
cf_role="grid_topology",
topology_dimension=2,
node_dimensions=("XG", "YG"),
face_dimensions=(
sgrid.FaceNodePadding("XC", "XG", sgrid.Padding.NONE),
sgrid.FaceNodePadding("YC", "YG", sgrid.Padding.NONE),
),
node_coordinates=("lon", "lat"),
vertical_dimensions=(sgrid.FaceNodePadding("ZC", "ZG", sgrid.Padding.NONE),),
),
),
"2d_left_unrolled_cone": _unrolled_cone_curvilinear_grid().pipe(
sgrid._attach_sgrid_metadata,
sgrid.SGrid2DMetadata(
cf_role="grid_topology",
topology_dimension=2,
node_dimensions=("XG", "YG"),
face_dimensions=(
sgrid.FaceNodePadding("XC", "XG", sgrid.Padding.HIGH),
sgrid.FaceNodePadding("YC", "YG", sgrid.Padding.HIGH),
),
node_coordinates=("lon", "lat"),
vertical_dimensions=(sgrid.FaceNodePadding("ZC", "ZG", sgrid.Padding.HIGH),),
),
),
}

_COMODO_TO_2D_SGRID = { # Note "2D SGRID" here is meant in the context of SGRID convention (i.e., 1D depth)
"XG": "node_dimension1",
"YG": "node_dimension2",
"XC": "face_dimension1",
"YC": "face_dimension2",
"ZG": "vertical_dimensions_dim1",
"ZC": "vertical_dimensions_dim2",
}

_DATASET_NAME_TO_SGRID_NAME = {
"ds_2d_left": "ds_2d_padded_high",
"ds_2d_right": "ds_2d_padded_low",
"ds_2d_outer": "ds_2d_padded_none",
"ds_2d_inner": "ds_2d_padded_both",
}


def create_datasets_sgrid(datasets_: dict[str, xr.Dataset]) -> dict[str, xr.Dataset]:
ret = {}
for name, ds in datasets_.items():
if name not in _DATASET_NAME_TO_SGRID_NAME:
continue
ret[_DATASET_NAME_TO_SGRID_NAME[name]] = ds.sgrid.rename(
_COMODO_TO_2D_SGRID,
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)
return ret


datasets_sgrid = create_datasets_sgrid(datasets)
2 changes: 1 addition & 1 deletion src/parcels/_sgrid/core.py
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Expand Up @@ -695,7 +695,7 @@ def _attach_sgrid_metadata(ds: xr.Dataset, grid: SGrid2DMetadata | SGrid3DMetada
0,
grid.to_attrs(),
)
ds.attrs["Conventions"] = "SGRID"
# ds.attrs["Conventions"] = "SGRID" # TODO: re-enable once XGrid.from_dataset is gone
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@VeckoTheGecko VeckoTheGecko May 28, 2026

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This was needed to get around some xgcm internals - will be re-enabled in a future PR.

return ds


Expand Down
6 changes: 3 additions & 3 deletions tests/datasets/test_structured.py
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@@ -1,12 +1,12 @@
import xgcm

from parcels._core.xgrid import _DEFAULT_XGCM_KWARGS
from parcels._datasets.structured.generic import datasets_comodo
from parcels._datasets.structured.generic import datasets


def test_left_indexed_dataset():
"""Checks that 'ds_2d_left' is right indexed on all variables."""
ds = datasets_comodo["ds_2d_left"]
ds = datasets["ds_2d_left"]
grid = xgcm.Grid(ds, **_DEFAULT_XGCM_KWARGS)

for _axis_name, axis in grid.axes.items():
Expand All @@ -16,7 +16,7 @@ def test_left_indexed_dataset():

def test_right_indexed_dataset():
"""Checks that 'ds_2d_right' is right indexed on all variables."""
ds = datasets_comodo["ds_2d_right"]
ds = datasets["ds_2d_right"]
grid = xgcm.Grid(ds, **_DEFAULT_XGCM_KWARGS)
for _axis_name, axis in grid.axes.items():
for pos, _dim_name in axis.coords.items():
Expand Down
4 changes: 2 additions & 2 deletions tests/datasets/test_utils.py
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Expand Up @@ -3,7 +3,7 @@
import xarray as xr

from parcels._datasets import utils
from parcels._datasets.structured.generic import datasets_comodo
from parcels._datasets.structured.generic import datasets


@pytest.fixture
Expand All @@ -28,7 +28,7 @@ def nonzero_ds():
)


@pytest.mark.parametrize("ds", [pytest.param(v, id=k) for k, v in datasets_comodo.items()])
@pytest.mark.parametrize("ds", [pytest.param(v, id=k) for k, v in datasets.items()])
@pytest.mark.parametrize("except_for", [None, "coords"])
def test_replace_arrays_with_zeros(ds, except_for):
# make sure doesn't error with range of datasets
Expand Down
2 changes: 1 addition & 1 deletion tests/test_field.py
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Expand Up @@ -7,7 +7,7 @@

from parcels import Field, UxGrid, VectorField, XGrid
from parcels._datasets.structured.generic import T as T_structured
from parcels._datasets.structured.generic import datasets_comodo as datasets_structured
from parcels._datasets.structured.generic import datasets as datasets_structured
from parcels._datasets.unstructured.generic import datasets as datasets_unstructured
from parcels.interpolators import (
UxConstantFaceConstantZC,
Expand Down
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