make sparse optional

pyproject.toml

@@ -26,8 +26,6 @@ dependencies = [
   "xarray",
   "flox",
   "scipy",
-  "sparse",
-  "opt-einsum",
 ]
 
 [tool.hatch.build]
@@ -41,10 +39,15 @@ Issues = "https://github.com/EXCITED-CO2/xarray-regrid/issues"
 Source = "https://github.com/EXCITED-CO2/xarray-regrid"
 
 [project.optional-dependencies]
+accel = [
+  "sparse",
+  "opt-einsum",
+]
 benchmarking = [
   "dask[distributed]",
   "matplotlib",
   "zarr",
+  "h5netcdf",
   "requests",
   "aiohttp",
 ]
@@ -71,7 +74,7 @@ docs = [  # Required for ReadTheDocs
 path = "src/xarray_regrid/__init__.py"
 
 [tool.hatch.envs.default]
-features = ["dev", "benchmarking"]
+features = ["accel", "dev", "benchmarking"]
 
 [tool.hatch.envs.default.scripts]
 lint = [

src/xarray_regrid/methods/conservative.py

@@ -5,7 +5,11 @@
 
 import numpy as np
 import xarray as xr
-from sparse import COO  # type: ignore
+
+try:
+    import sparse  # type: ignore
+except ImportError:
+    sparse = None
 
 from xarray_regrid import utils
 
@@ -126,7 +130,11 @@ def conservative_regrid_dataset(
 
         for array in data_vars.keys():
             if coord in data_vars[array].dims:
-                var_weights = sparsify_weights(weights, data_vars[array])
+                if sparse is not None:
+                    var_weights = sparsify_weights(weights, data_vars[array])
+                else:
+                    var_weights = weights
+
                 data_vars[array], valid_fracs[array] = apply_weights(
                     da=data_vars[array],
                     weights=var_weights,
@@ -200,8 +208,12 @@ def apply_weights(
             valid_frac = valid_frac.clip(0, 1)
 
     # In some cases, dot product of dask data and sparse weights fails
-    # to densify, which prevents future conversion to numpy
-    if da_reduced.chunks and isinstance(da_reduced.data._meta, COO):
+    # to automatically densify, which prevents future conversion to numpy
+    if (
+        sparse is not None
+        and da_reduced.chunks
+        and isinstance(da_reduced.data._meta, sparse.COO)
+    ):
         da_reduced.data = da_reduced.data.map_blocks(
             lambda x: x.todense(), dtype=da_reduced.dtype
         )
@@ -268,8 +280,8 @@ def sparsify_weights(weights: xr.DataArray, da: xr.DataArray) -> xr.DataArray:
     new_weights = weights.copy().astype(da.dtype)
     if da.chunks:
         chunks = {k: v for k, v in da.chunksizes.items() if k in weights.dims}
-        new_weights.data = new_weights.chunk(chunks).data.map_blocks(COO)
+        new_weights.data = new_weights.chunk(chunks).data.map_blocks(sparse.COO)
     else:
-        new_weights.data = COO(weights.data)
+        new_weights.data = sparse.COO(weights.data)
 
     return new_weights

tests/test_regrid.py

@@ -208,7 +208,7 @@ def test_conservative_nan_thresholds_against_coarsen(nan_threshold):
 
 @pytest.mark.skipif(xesmf is None, reason="xesmf required")
 def test_conservative_nan_thresholds_against_xesmf():
-    ds = xr.tutorial.open_dataset("ersstv5").sst.compute().isel(time=[0])
+    ds = xr.tutorial.open_dataset("ersstv5").sst.isel(time=[0]).compute()
     ds = ds.rename(lon="longitude", lat="latitude")
     new_grid = xarray_regrid.Grid(
         north=90,