[skip-ci] Add cftime groupby, resample benchmarks

.github/workflows/benchmarks.yml

@@ -45,6 +45,7 @@ jobs:
           set -x
           # ID this runner
           asv machine --yes
+          export TMPDIR=$RUNNER_TEMP
           echo "Baseline:  ${{ github.event.pull_request.base.sha }} (${{ github.event.pull_request.base.label }})"
           echo "Contender: ${GITHUB_SHA} (${{ github.event.pull_request.head.label }})"
           # Use mamba for env creation

asv_bench/asv.conf.json

@@ -30,6 +30,7 @@
     // determined by looking for tools on the PATH environment
     // variable.
     "environment_type": "conda",
+    "conda_channels": ["conda-forge"],
 
     // timeout in seconds for installing any dependencies in environment
     // defaults to 10 min

asv_bench/benchmarks/groupby.py

@@ -132,3 +132,34 @@ def setup(self, *args, **kwargs):
         super().setup(**kwargs)
         self.ds1d = self.ds1d.chunk({"time": 50})
         self.ds2d = self.ds2d.chunk({"time": 50, "z": 4})
+
+
+class ResampleCFTime:
+    def setup(self, *args, **kwargs):
+        self.ds1d = xr.Dataset(
+            {
+                "b": ("time", np.arange(365.0 * 24)),
+            },
+            coords={
+                "time": xr.date_range(
+                    "2001-01-01", freq="H", periods=365 * 24, calendar="noleap"
+                )
+            },
+        )
+        self.ds2d = self.ds1d.expand_dims(z=10)
+        self.ds1d_mean = self.ds1d.resample(time="48H").mean()
+        self.ds2d_mean = self.ds2d.resample(time="48H").mean()
+
+
+class GroupByCFTime:
+    def setup(self, *args, **kwargs):
+        arr = np.random.randn(10, 10, 365 * 30)
+        time = xr.date_range("2000", periods=30 * 365, calendar="noleap")
+        self.da = xr.DataArray(arr, dims=("y", "x", "time"), coords={"time": time})
+        self.gb = self.da.groupby("time.year")
+
+    def time_init(self, ndim):
+        self.da.groupby("time.year")
+
+    def time_mean(self):
+        self.gb.mean()