Remove rioxarray dependency

docs/conf.py

@@ -155,7 +155,8 @@
 napoleon_type_aliases = {
     # general terms
     "sequence": ":term:`sequence`",
-    "iterable": ":term:`iterable`",
+    "Hashable": ":term:`sequence`",
+    "iterable": "~collections.abc.Hashable",
     "callable": ":py:func:`callable`",
     "dict_like": ":term:`dict-like <mapping>`",
     "dict-like": ":term:`dict-like <mapping>`",

docs/raster_index/design_choices.md

@@ -46,3 +46,16 @@ The downside here is that CRS information is duplicated in two places explicitly
 ### Don't like it?
 
 We chose this approach to enable experimentation. It is entirely possible to experiment with other approaches. Please reach out if you have opinions on this topic.
+
+## Handling the `GeoTransform` attribute
+
+GDAL _chooses_ to track the affine transform using a `GeoTransform` attribute on a `spatial_ref` variable. The `"spatial_ref"` is a
+"grid mapping" variable (as termed by the CF-conventions). It also records CRS information. Currently, our design is that
+{py:class}`xproj.CRSIndex` controls the CRS information and handles the creation of the `"spatial_ref"` variable, or more generally,
+the grid mapping variable. Thus, {py:class}`RasterIndex` _cannot_ keep the `"GeoTransform"` attribute on `"spatial_ref"` up-to-date
+because it does not control it.
+
+Thus, {py:func}`assign_index` will delete the `"GeoTransform"` attribute on the grid mapping variable if it is detected, after using it
+to construct the affine matrix.
+
+If you wish to extract the GeoTransform attribute to write it to a location of your choosing use {py:meth}`RasterIndex.as_geotransform`.

docs/rasterize/exactextract.ipynb

@@ -28,9 +28,10 @@
    "outputs": [],
    "source": [
     "import xarray as xr\n",
+    "import xproj  # noqa\n",
     "\n",
     "ds = xr.tutorial.open_dataset(\"eraint_uvz\")\n",
-    "ds = ds.rio.write_crs(\"epsg:4326\")\n",
+    "ds = ds.proj.assign_crs(spatial_ref=\"epsg:4326\")\n",
     "ds"
    ]
   },

docs/rasterize/geometry_mask.ipynb

@@ -28,9 +28,10 @@
    "outputs": [],
    "source": [
     "import xarray as xr\n",
+    "import xproj  # noqa\n",
     "\n",
     "ds = xr.tutorial.open_dataset(\"eraint_uvz\")[[\"u\"]]\n",
-    "ds = ds.rio.write_crs(\"epsg:4326\")\n",
+    "ds = ds.proj.assign_crs(spatial_ref=\"epsg:4326\")\n",
     "ds"
    ]
   },

docs/rasterize/rasterio.ipynb

@@ -28,9 +28,10 @@
    "outputs": [],
    "source": [
     "import xarray as xr\n",
+    "import xproj  # noqa\n",
     "\n",
     "ds = xr.tutorial.open_dataset(\"eraint_uvz\")\n",
-    "ds = ds.rio.write_crs(\"epsg:4326\")\n",
+    "ds = ds.proj.assign_crs(spatial_ref=\"epsg:4326\")\n",
     "ds"
    ]
   },

pyproject.toml

@@ -43,7 +43,6 @@ test = [
   "pooch",
   "dask-geopandas",
   "rasterio",
-  "rioxarray",
   "exactextract",
   "sparse",
   "netCDF4",

src/rasterix/raster_index.py

@@ -21,6 +21,7 @@
 
 from rasterix.odc_compat import BoundingBox, bbox_intersection, bbox_union, maybe_int, snap_grid
 from rasterix.rioxarray_compat import guess_dims
+from rasterix.utils import get_affine
 
 T_Xarray = TypeVar("T_Xarray", "DataArray", "Dataset")
 
@@ -37,10 +38,14 @@ def assign_index(
 ) -> T_Xarray:
     """Assign a RasterIndex to an Xarray DataArray or Dataset.
 
+    By default, the affine transform is guessed by first looking for a ``GeoTransform`` attribute
+    on a CF "grid mapping" variable (commonly ``"spatial_ref"``). If not present, then the affine is determined from 1D coordinate
+    variables named ``x_dim`` and ``y_dim`` provided to this function.
+
     Parameters
     ----------
     obj : xarray.DataArray or xarray.Dataset
-        The object to assign the index to. Must have a rio accessor with a transform.
+        The object to assign the index to.
     x_dim : str, optional
         Name of the x dimension. If None, will be automatically detected.
     y_dim : str, optional
@@ -53,22 +58,37 @@ def assign_index(
     xarray.DataArray or xarray.Dataset
         The input object with RasterIndex coordinates assigned.
 
+    Notes
+    -----
+    The "grid mapping" variable is determined following the CF conventions:
+
+    - If a DataArray is provided, we look for an attribute named ``"grid_mapping"``.
+    - For a Dataset, we pull the first detected ``"grid_mapping"`` attribute when iterating over data variables.
+
+    The value of this attribute is a variable name containing projection information (commonly ``"spatial_ref"``).
+    We then look for a ``"GeoTransform"`` attribute on this variable (following GDAL convention).
+
+    References
+    ----------
+    - `CF conventions document <http://cfconventions.org/Data/cf-conventions/cf-conventions-1.11/cf-conventions.html#grid-mappings-and-projections>`_.
+    - `GDAL docs on GeoTransform <https://gdal.org/en/stable/tutorials/geotransforms_tut.html>`_.
+
     Examples
     --------
     >>> import xarray as xr
-    >>> import rioxarray  # Required for rio accessor
+    >>> import rioxarray  # Required for reading TIFF
     >>> da = xr.open_dataset("path/to/raster.tif", engine="rasterio")
     >>> indexed_da = assign_index(da)
     """
-    import rioxarray  # noqa
-
     if x_dim is None or y_dim is None:
         guessed_x, guessed_y = guess_dims(obj)
     x_dim = x_dim or guessed_x
     y_dim = y_dim or guessed_y
 
+    affine = get_affine(obj, x_dim=x_dim, y_dim=y_dim, clear_transform=True)
+
     index = RasterIndex.from_transform(
-        obj.rio.transform(),
+        affine,
         width=obj.sizes[x_dim],
         height=obj.sizes[y_dim],
         x_dim=x_dim,
@@ -626,6 +646,15 @@ def transform(self) -> Affine:
         """Affine transform for top-left corners."""
         return self.center_transform() * Affine.translation(-0.5, -0.5)
 
+    def as_geotransform(self, *, decimals: int | None = None) -> str:
+        """Convert the affine transform to a string suitable for saving as the GeoTransform attribute."""
+        gt = self.transform().to_gdal()
+        if decimals is not None:
+            fmt = f".{decimals}f"
+            return " ".join(f"{num:{fmt}}" for num in gt)
+        else:
+            return " ".join(map(str, gt))
+
     def center_transform(self) -> Affine:
         """Affine transform for cell centers."""
         if not self._axis_independent:

src/rasterix/rasterize/rasterio.py

@@ -15,7 +15,8 @@
 from rasterio.features import geometry_mask as geometry_mask_rio
 from rasterio.features import rasterize as rasterize_rio
 
-from .utils import XAXIS, YAXIS, clip_to_bbox, get_affine, is_in_memory, prepare_for_dask
+from ..utils import get_affine
+from .utils import XAXIS, YAXIS, clip_to_bbox, is_in_memory, prepare_for_dask
 
 F = TypeVar("F", bound=Callable[..., Any])
 
@@ -161,7 +162,7 @@ def rasterize(
         obj = clip_to_bbox(obj, geometries, xdim=xdim, ydim=ydim)
 
     rasterize_kwargs = dict(
-        all_touched=all_touched, merge_alg=merge_alg, affine=get_affine(obj, xdim=xdim, ydim=ydim), env=env
+        all_touched=all_touched, merge_alg=merge_alg, affine=get_affine(obj, x_dim=xdim, y_dim=ydim), env=env
     )
     # FIXME: box.crs == geometries.crs
 
@@ -325,7 +326,7 @@ def geometry_mask(
         obj = clip_to_bbox(obj, geometries, xdim=xdim, ydim=ydim)
 
     geometry_mask_kwargs = dict(
-        all_touched=all_touched, affine=get_affine(obj, xdim=xdim, ydim=ydim), env=env
+        all_touched=all_touched, affine=get_affine(obj, x_dim=xdim, y_dim=ydim), env=env
     )
 
     if is_in_memory(obj=obj, geometries=geometries):

src/rasterix/utils.py

@@ -0,0 +1,67 @@
+import xarray as xr
+from affine import Affine
+
+
+def get_grid_mapping_var(obj: xr.Dataset | xr.DataArray) -> xr.DataArray | None:
+    grid_mapping_var = None
+    if isinstance(obj, xr.DataArray):
+        if maybe := obj.attrs.get("grid_mapping", None):
+            if maybe in obj.coords:
+                grid_mapping_var = maybe
+    else:
+        # for datasets, grab the first one for simplicity
+        for var in obj.data_vars.values():
+            if maybe := var.attrs.get("grid_mapping"):
+                if maybe in obj.coords:
+                    # make sure it exists and is not an out-of-date attribute
+                    grid_mapping_var = maybe
+                    break
+    if grid_mapping_var is None and "spatial_ref" in obj.coords:
+        # hardcode this
+        grid_mapping_var = "spatial_ref"
+    if grid_mapping_var is not None:
+        return obj[grid_mapping_var]
+    return None
+
+
+def get_affine(
+    obj: xr.Dataset | xr.DataArray, *, x_dim="x", y_dim="y", clear_transform: bool = False
+) -> Affine:
+    """
+    Grabs an affine transform from an Xarray object.
+
+    This method will first look for the ``"GeoTransform"`` attribute on a variable named
+    ``"spatial_ref"``. If not, it will auto-guess the transform from the provided ``x_dim``,
+    and ``y_dim``.
+
+    Parameters
+    ----------
+    obj: xr.DataArray or xr.Dataset
+    x_dim: str, optional
+        Name of the X dimension coordinate variable.
+    y_dim: str, optional
+        Name of the Y dimension coordinate variable.
+    clear_transform: bool
+       Whether to delete the ``GeoTransform`` attribute if detected.
+
+    Returns
+    -------
+    affine: Affine
+    """
+    grid_mapping_var = get_grid_mapping_var(obj)
+    if grid_mapping_var is not None and (transform := grid_mapping_var.attrs.get("GeoTransform")):
+        if clear_transform:
+            del grid_mapping_var.attrs["GeoTransform"]
+        return Affine.from_gdal(*map(float, transform.split(" ")))
+    else:
+        x = obj.coords[x_dim]
+        y = obj.coords[y_dim]
+        if x.ndim != 1:
+            raise ValueError(f"Coordinate variable {x_dim=!r} must be 1D.")
+        if y.ndim != 1:
+            raise ValueError(f"Coordinate variable {y_dim=!r} must be 1D.")
+        dx = (x[1] - x[0]).item()
+        dy = (y[1] - y[0]).item()
+        return Affine.translation(
+            x[0].item() - dx / 2, (y[0] if dy < 0 else y[-1]).item() - dy / 2
+        ) * Affine.scale(dx, dy)

tests/test_exact.py

@@ -9,14 +9,15 @@
 import sparse
 import xarray as xr
 import xarray.testing as xrt
+import xproj  # noqa
 from exactextract import exact_extract
 from hypothesis import example, given, settings
 from xarray.tests import raise_if_dask_computes
 
 from rasterix.rasterize.exact import CoverageWeights, coverage, xy_to_raster_source
 
 dataset = xr.tutorial.open_dataset("eraint_uvz").rename({"latitude": "y", "longitude": "x"})
-dataset = dataset.rio.write_crs("epsg:4326")
+dataset = dataset.proj.assign_crs(spatial_ref="epsg:4326")
 world = gpd.read_file(geodatasets.get_path("naturalearth land"))
 XSIZE = dataset.x.size
 YSIZE = dataset.y.size

tests/test_raster_index.py

@@ -3,12 +3,12 @@
 import numpy as np
 import pyproj
 import pytest
-import rioxarray  # noqa
 import xarray as xr
 from affine import Affine
 from xarray.testing import assert_identical
 
 from rasterix import RasterIndex, assign_index
+from rasterix.utils import get_grid_mapping_var
 
 CRS_ATTRS = pyproj.CRS.from_epsg(4326).to_cf()
 
@@ -20,6 +20,32 @@ def dataset_from_transform(transform: str) -> xr.Dataset:
     ).pipe(assign_index)
 
 
+def test_grid_mapping_var():
+    obj = xr.DataArray()
+    assert get_grid_mapping_var(obj) is None
+
+    obj = xr.Dataset()
+    assert get_grid_mapping_var(obj) is None
+
+    obj = xr.DataArray(attrs={"grid_mapping": "spatial_ref"})
+    assert get_grid_mapping_var(obj) is None
+
+    obj = xr.DataArray(attrs={"grid_mapping": "spatial_ref"}, coords={"spatial_ref": 0})
+    assert_identical(get_grid_mapping_var(obj), obj["spatial_ref"])
+
+    obj = xr.Dataset({"foo": ((), 0, {"grid_mapping": "spatial_ref"})})
+    assert get_grid_mapping_var(obj) is None
+
+    obj = xr.Dataset(
+        {
+            "foo": ((), 0, {"grid_mapping": "spatial_ref_0"}),
+            "zoo": ((), 0, {"grid_mapping": "spatial_ref_1"}),
+        },
+        coords={"spatial_ref_1": 0},
+    )
+    assert_identical(get_grid_mapping_var(obj), obj["spatial_ref_1"])
+
+
 def test_set_xindex() -> None:
     coords = xr.Coordinates(coords={"x": np.arange(0.5, 12.5), "y": np.arange(0.5, 10.5)}, indexes={})
     ds = xr.Dataset(coords=coords)
@@ -28,26 +54,26 @@ def test_set_xindex() -> None:
         ds.set_xindex(["x", "y"], RasterIndex)
 
 
-def test_rectilinear():
-    source = "/vsicurl/https://noaadata.apps.nsidc.org/NOAA/G02135/south/daily/geotiff/2024/01_Jan/S_20240101_concentration_v3.0.tif"
-    da_no_raster_index = xr.open_dataarray(source, engine="rasterio")
-    da_raster_index = assign_index(da_no_raster_index)
-    assert da_raster_index.equals(da_no_raster_index)
-
-
 def test_raster_index_properties():
     index1 = RasterIndex.from_transform(Affine.identity(), width=12, height=10)
     assert index1.xy_shape == (12, 10)
     assert index1.xy_dims == ("x", "y")
     assert index1.xy_coord_names == ("x", "y")
+    assert index1.as_geotransform() == "0.0 1.0 0.0 0.0 0.0 1.0"
 
     index2 = RasterIndex.from_transform(Affine.identity(), width=12, height=10, x_dim="x_", y_dim="y_")
     assert index2.xy_dims == ("x_", "y_")
+    assert index2.as_geotransform() == "0.0 1.0 0.0 0.0 0.0 1.0"
 
     index3 = RasterIndex.from_transform(Affine.rotation(45.0), width=12, height=10)
     assert index3.xy_shape == (12, 10)
     assert index3.xy_dims == ("x", "y")
     assert index3.xy_coord_names == ("xc", "yc")
+    assert (
+        index3.as_geotransform()
+        == "0.0 0.7071067811865476 -0.7071067811865475 0.0 0.7071067811865475 0.7071067811865476"
+    )
+    assert index3.as_geotransform(decimals=6) == "0.000000 0.707107 -0.707107 0.000000 0.707107 0.707107"
 
 
 # TODO: parameterize over
@@ -56,18 +82,17 @@ def test_raster_index_properties():
 def test_sel_slice():
     ds = xr.Dataset({"foo": (("y", "x"), np.ones((10, 12)))})
     transform = Affine.identity()
-    ds = ds.rio.write_transform(transform)
+    ds.coords["spatial_ref"] = ((), 0, {"GeoTransform": " ".join(map(str, transform.to_gdal()))})
     ds = assign_index(ds)
-
+    assert "GeoTransform" not in ds.spatial_ref.attrs
     assert ds.xindexes["x"].transform() == transform
 
     actual = ds.sel(x=slice(4), y=slice(3, 5))
     assert isinstance(actual.xindexes["x"], RasterIndex)
     assert isinstance(actual.xindexes["y"], RasterIndex)
-    actual_transform = actual.xindexes["x"].transform()
 
-    assert actual_transform == actual.rio.transform()
-    assert actual_transform == (transform * Affine.translation(0, 3))
+    actual_transform = actual.xindexes["x"].transform()
+    assert actual_transform == transform * Affine.translation(0, 3)
 
     reverse = ds.isel(y=slice(None, None, -1))
     assert_identical(reverse.y, ds.y[::-1])