Add groupby & resample benchmarks

asv_bench/benchmarks/groupby.py

@@ -1,39 +1,98 @@
 import numpy as np
+import pandas as pd
 
 import xarray as xr
 
-from . import parameterized, requires_dask
+from . import _skip_slow, parameterized, requires_dask
 
 
 class GroupBy:
     def setup(self, *args, **kwargs):
-        self.ds = xr.Dataset(
+        self.n = 100
+        self.ds1d = xr.Dataset(
             {
-                "a": xr.DataArray(np.r_[np.arange(500.0), np.arange(500.0)]),
-                "b": xr.DataArray(np.arange(1000.0)),
+                "a": xr.DataArray(np.r_[np.repeat(1, self.n), np.repeat(2, self.n)]),
+                "b": xr.DataArray(np.arange(2 * self.n)),
             }
         )
+        self.ds2d = self.ds1d.expand_dims(z=10)
 
-    @parameterized(["method"], [("sum", "mean")])
-    def time_agg(self, method):
-        return getattr(self.ds.groupby("a"), method)()
+    @parameterized(["ndim"], [(1, 2)])
+    def time_init(self, ndim):
+        getattr(self, f"ds{ndim}d").groupby("b")
+
+    @parameterized(["method", "ndim"], [("sum", "mean"), (1, 2)])
+    def time_agg_small_num_groups(self, method, ndim):
+        ds = getattr(self, f"ds{ndim}d")
+        getattr(ds.groupby("a"), method)()
+
+    @parameterized(["method", "ndim"], [("sum", "mean"), (1, 2)])
+    def time_agg_large_num_groups(self, method, ndim):
+        ds = getattr(self, f"ds{ndim}d")
+        getattr(ds.groupby("b"), method)()
 
 
 class GroupByDask(GroupBy):
     def setup(self, *args, **kwargs):
         requires_dask()
         super().setup(**kwargs)
-        self.ds = self.ds.chunk({"dim_0": 50})
+        self.ds1d = self.ds1d.sel(dim_0=slice(None, None, 2)).chunk({"dim_0": 50})
+        self.ds2d = self.ds2d.sel(dim_0=slice(None, None, 2)).chunk(
+            {"dim_0": 50, "z": 5}
+        )
 
 
-class GroupByDataFrame(GroupBy):
+class GroupByPandasDataFrame(GroupBy):
+    """Run groupby tests using pandas DataFrame."""
+
     def setup(self, *args, **kwargs):
+        # Skip testing in CI as it won't ever change in a commit:
+        _skip_slow()
+
         super().setup(**kwargs)
-        self.ds = self.ds.to_dataframe()
+        self.ds1d = self.ds1d.to_dataframe()
 
 
 class GroupByDaskDataFrame(GroupBy):
+    """Run groupby tests using dask DataFrame."""
+
+    def setup(self, *args, **kwargs):
+        # Skip testing in CI as it won't ever change in a commit:
+        _skip_slow()
+
+        requires_dask()
+        super().setup(**kwargs)
+        self.ds1d = self.ds1d.chunk({"dim_0": 50}).to_dataframe()
+
+
+class Resample:
+    def setup(self, *args, **kwargs):
+        self.ds1d = xr.Dataset(
+            {
+                "b": ("time", np.arange(365.0 * 24)),
+            },
+            coords={"time": pd.date_range("2001-01-01", freq="H", periods=365 * 24)},
+        )
+        self.ds2d = self.ds1d.expand_dims(z=10)
+
+    @parameterized(["ndim"], [(1, 2)])
+    def time_init(self, ndim):
+        getattr(self, f"ds{ndim}d").resample(time="D")
+
+    @parameterized(["method", "ndim"], [("sum", "mean"), (1, 2)])
+    def time_agg_small_num_groups(self, method, ndim):
+        ds = getattr(self, f"ds{ndim}d")
+        getattr(ds.resample(time="3M"), method)()
+
+    @parameterized(["method", "ndim"], [("sum", "mean"), (1, 2)])
+    def time_agg_large_num_groups(self, method, ndim):
+        ds = getattr(self, f"ds{ndim}d")
+        getattr(ds.resample(time="48H"), method)()
+
+
+class ResampleDask(Resample):
     def setup(self, *args, **kwargs):
         requires_dask()
         super().setup(**kwargs)
-        self.ds = self.ds.chunk({"dim_0": 50}).to_dataframe()
+        self.ds1d = self.ds1d.chunk({"time": 50})
+        self.ds2d = self.ds2d.chunk({"time": 50, "z": 4})