⚗️ Benchmark cog3pio engine against rioxarray

Parametrized test to compare loading a GeoTIFF using cog3pio vs rioxarray via their respective xarray BackendEntrypoints. Made a new extras dependency group in pyproject.toml called 'benchmark', and listed both `pytest-codspeed` and `rioxarray` under it.

.github/workflows/benchmarks.yml

@@ -48,7 +48,7 @@ jobs:
       - name: Install the package
         run: |
           set -e
-          python -m pip install cog3pio[tests] --find-links dist --force-reinstall
+          python -m pip install cog3pio[benchmark,tests] --find-links dist --force-reinstall
           python -m pip list
 
       # Run the benchmark tests

pyproject.toml

@@ -21,9 +21,12 @@ dependencies = [
 dynamic = ["version"]
 
 [project.optional-dependencies]
+benchmark = [
+    "pytest-codspeed",
+    "rioxarray",
+]
 tests = [
     "pytest",
-    "pytest-codspeed",
 ]
 
 [project.entry-points."xarray.backends"]

python/tests/test_xarray_backend.py

@@ -2,13 +2,36 @@
 Tests for xarray 'cog3pio' backend engine.
 """
 
+import numpy as np
+import pytest
 import xarray as xr
 
+try:
+    import rioxarray
+
+    HAS_RIOXARRAY = True
+except ImportError:
+    HAS_RIOXARRAY = False
+
 
 # %%
-def test_xarray_backend_cog3pio():
+@pytest.mark.benchmark
+@pytest.mark.parametrize(
+    "engine",
+    [
+        "cog3pio",
+        pytest.param(
+            "rasterio",
+            marks=pytest.mark.skipif(
+                condition=not HAS_RIOXARRAY, reason="Could not import 'rioxarray'"
+            ),
+        ),
+    ],
+)
+def test_xarray_backend_open_dataarray(engine):
     """
-    Ensure that passing engine='cog3pio' to xarray.open_dataarray works.
+    Ensure that passing engine='cog3pio' to xarray.open_dataarray works, and benchmark
+    against engine="rasterio" (rioxarray).
     """
     with xr.open_dataarray(
         filename_or_obj="https://github.com/cogeotiff/rio-tiler/raw/6.4.1/tests/fixtures/cog_nodata_nan.tif",
@@ -20,3 +43,4 @@ def test_xarray_backend_cog3pio():
         assert da.y.min() == 5190440.0
         assert da.y.max() == 5300040.0
         assert da.dtype == "float32"
+        np.testing.assert_allclose(actual=da.mean(), desired=0.08855476)