Add grid_bounds_to_polygons

Modified from
https://notebooksharing.space/view/c6c1f3a7d0c260724115eaa2bf78f3738b275f7f633c1558639e7bbd75b31456#displayOptions=

Co-authored-by: Ryan Abernathey <[email protected]>

Author: dcherian

cf_xarray/geometry.py

@@ -293,3 +293,64 @@ def cf_to_points(ds: xr.Dataset):
         j += n
 
     return xr.DataArray(geoms, dims=node_count.dims, coords=node_count.coords)
+
+
+def grid_bounds_to_polygons(ds: xr.Dataset) -> xr.DataArray:
+    """
+    Converts a regular 2D lat/lon grid to a 2D array of shapely polygons.
+
+    Modified from https://notebooksharing.space/view/c6c1f3a7d0c260724115eaa2bf78f3738b275f7f633c1558639e7bbd75b31456.
+
+    Parameters
+    ----------
+    ds : xr.Dataset
+        Dataset with "latitude" and "longitude" variables as well as their bounds variables.
+        1D "latitude" and "longitude" variables are supported. This function will automatically
+        broadcast them against each other.
+
+    Returns
+    -------
+    DataArray
+        DataArray with shapely polygon per grid cell.
+    """
+    from shapely import Polygon
+
+    grid = ds.cf[["latitude", "longitude"]].load().reset_coords()
+    bounds = ds.cf.bounds
+
+    assert "latitude" in bounds
+    assert "longitude" in bounds
+    (lon_bounds,) = bounds["longitude"]
+    (lat_bounds,) = bounds["latitude"]
+
+    (points,) = xr.broadcast(grid)
+
+    bounds_dim = grid.cf.get_bounds_dim_name("latitude")
+    points = points.transpose(..., bounds_dim)
+    assert points.sizes[bounds_dim] == 2
+
+    lonbnd = points[lon_bounds].data
+    latbnd = points[lat_bounds].data
+
+    # geopandas needs this
+    expanded_lon = lonbnd[..., [0, 0, 1, 1]]
+    mask = expanded_lon[..., 0] >= 180
+    expanded_lon[mask, :] = expanded_lon[mask, :] - 360
+
+    # these magic numbers are OK :
+    # - 4 corners to a polygon, and
+    # - 2 from stacking lat, lon along the last axis
+    # flatten here to make iteration easier. It would be nice to avoid that.
+    # potentially with just np.vectorize. The polygon creation is the real slow bit.
+    # Shapely's MultiPolygon also iterates over a list in Python...
+    blocked = np.stack([expanded_lon, latbnd[..., [0, 1, 1, 0]]], axis=-1).reshape(
+        -1, 4, 2
+    )
+    polyarray = np.array(
+        [Polygon(blocked[i, ...]) for i in range(blocked.shape[0])], dtype="O"
+    )
+    newshape = latbnd.shape[:-1]
+    polyarray = polyarray.reshape(newshape)
+    boxes = points[lon_bounds][..., 0].copy(data=polyarray)
+
+    return boxes