Strengthen grid parser format checks

Author: rajeeja

test/io/test_utils.py

@@ -0,0 +1,65 @@
+import numpy as np
+import pytest
+import xarray as xr
+
+from uxarray.io.utils import _parse_grid_type
+
+
+@pytest.mark.parametrize(
+    ("path_args", "expected_spec"),
+    [
+        (("exodus", "outCSne8", "outCSne8.g"), "Exodus"),
+        (("scrip", "outCSne8", "outCSne8.nc"), "Scrip"),
+        (("ugrid", "outCSne30", "outCSne30.ug"), "UGRID"),
+        (("mpas", "QU", "mesh.QU.1920km.151026.nc"), "MPAS"),
+        (("esmf", "ne30", "ne30pg3.grid.nc"), "ESMF"),
+        (("geos-cs", "c12", "test-c12.native.nc4"), "GEOS-CS"),
+        (("icon", "R02B04", "icon_grid_0010_R02B04_G.nc"), "ICON"),
+        (("fesom", "soufflet-netcdf", "grid.nc"), "FESOM2"),
+    ],
+)
+def test_parse_grid_type_detects_supported_formats(gridpath, path_args, expected_spec):
+    with xr.open_dataset(gridpath(*path_args)) as ds:
+        source_grid_spec, lon_name, lat_name = _parse_grid_type(ds)
+
+    assert source_grid_spec == expected_spec
+    assert lon_name is None
+    assert lat_name is None
+
+
+def test_parse_grid_type_detects_structured_grid():
+    lon = xr.DataArray(
+        np.array([0.0, 1.0, 2.0]),
+        dims=["lon"],
+        attrs={"standard_name": "longitude"},
+    )
+    lat = xr.DataArray(
+        np.array([-1.0, 0.0, 1.0]),
+        dims=["lat"],
+        attrs={"standard_name": "latitude"},
+    )
+    ds = xr.Dataset(coords={"lon": lon, "lat": lat})
+
+    source_grid_spec, lon_name, lat_name = _parse_grid_type(ds)
+
+    assert source_grid_spec == "Structured"
+    assert lon_name == "lon"
+    assert lat_name == "lat"
+
+
+@pytest.mark.parametrize(
+    "dataset",
+    [
+        xr.Dataset({"grid_center_lon": xr.DataArray([0.0], dims=["grid_size"])}),
+        xr.Dataset(
+            {
+                "coordx": xr.DataArray([0.0, 1.0], dims=["num_nodes"]),
+                "coordy": xr.DataArray([0.0, 1.0], dims=["num_nodes"]),
+            }
+        ),
+        xr.Dataset({"verticesOnCell": xr.DataArray([[1, 2, 3]], dims=["nCells", "nVert"])}),
+    ],
+)
+def test_parse_grid_type_rejects_incomplete_format_signals(dataset):
+    with pytest.raises(RuntimeError, match="Could not recognize dataset format"):
+        _parse_grid_type(dataset)

uxarray/io/utils.py

@@ -7,6 +7,74 @@
 from uxarray.io._ugrid import _is_ugrid, _read_ugrid
 
 
+def _is_exodus(dataset: xr.Dataset) -> bool:
+    """Check whether a dataset looks like an Exodus mesh."""
+    has_packed_coords = "coord" in dataset
+    has_split_coords = {"coordx", "coordy"}.issubset(dataset.variables)
+    has_connectivity = any(
+        name.startswith("connect") for name in dataset.variables
+    ) or any("num_nod_per_el" in dim for dim in dataset.dims)
+
+    return has_connectivity and (has_packed_coords or has_split_coords)
+
+
+def _is_scrip(dataset: xr.Dataset) -> bool:
+    """Check whether a dataset looks like an unstructured SCRIP grid."""
+    required_vars = {
+        "grid_center_lon",
+        "grid_center_lat",
+        "grid_corner_lon",
+        "grid_corner_lat",
+    }
+    unstructured_markers = {"grid_imask", "grid_rank", "grid_area"}
+
+    return required_vars.issubset(dataset.variables) and any(
+        marker in dataset for marker in unstructured_markers
+    )
+
+
+def _is_mpas(dataset: xr.Dataset) -> bool:
+    """Check whether a dataset looks like an MPAS grid."""
+    if "verticesOnCell" not in dataset:
+        return False
+
+    companion_groups = (
+        {"nEdgesOnCell"},
+        {"latCell", "lonCell"},
+        {"latVertex", "lonVertex"},
+        {"xCell", "yCell", "zCell"},
+        {"xVertex", "yVertex", "zVertex"},
+    )
+
+    return any(group.issubset(dataset.variables) for group in companion_groups)
+
+
+def _is_esmf(dataset: xr.Dataset) -> bool:
+    """Check whether a dataset looks like an ESMF mesh."""
+    return "maxNodePElement" in dataset.dims and "elementConn" in dataset
+
+
+def _is_geos_cs(dataset: xr.Dataset) -> bool:
+    """Check whether a dataset looks like a GEOS cube-sphere grid."""
+    required_dims = {"nf", "YCdim", "XCdim"}
+    required_vars = {"corner_lons", "corner_lats"}
+
+    return required_dims.issubset(dataset.sizes) and required_vars.issubset(
+        dataset.variables
+    )
+
+
+def _is_icon(dataset: xr.Dataset) -> bool:
+    """Check whether a dataset looks like an ICON grid."""
+    required_vars = {"vertex_of_cell", "clon", "clat", "vlon", "vlat"}
+    return required_vars.issubset(dataset.variables)
+
+
+def _is_fesom2(dataset: xr.Dataset) -> bool:
+    """Check whether a dataset looks like a FESOM2 grid."""
+    return "triag_nodes" in dataset
+
+
 def _parse_grid_type(dataset):
     """Checks input and contents to determine grid type. Supports detection of
     UGrid, SCRIP, Exodus, ESMF, and shape file.
@@ -31,27 +99,21 @@ def _parse_grid_type(dataset):
 
     _structured, lon_name, lat_name = _is_structured(dataset)
 
-    if "coord" in dataset:
-        # exodus with coord or coordx
-        mesh_type = "Exodus"
-    elif "coordx" in dataset:
+    if _is_exodus(dataset):
         mesh_type = "Exodus"
-    elif "grid_center_lon" in dataset:
-        # scrip with grid_center_lon
+    elif _is_scrip(dataset):
         mesh_type = "Scrip"
     elif _is_ugrid(dataset):
-        # ugrid topology is present
         mesh_type = "UGRID"
-    elif "verticesOnCell" in dataset:
+    elif _is_mpas(dataset):
         mesh_type = "MPAS"
-    elif "maxNodePElement" in dataset.dims:
+    elif _is_esmf(dataset):
         mesh_type = "ESMF"
-    elif all(key in dataset.sizes for key in ["nf", "YCdim", "XCdim"]):
-        # expected dimensions for a GEOS cube sphere grid
+    elif _is_geos_cs(dataset):
         mesh_type = "GEOS-CS"
-    elif "vertex_of_cell" in dataset:
+    elif _is_icon(dataset):
         mesh_type = "ICON"
-    elif "triag_nodes" in dataset:
+    elif _is_fesom2(dataset):
         mesh_type = "FESOM2"
     elif _structured:
         mesh_type = "Structured"