Enable taking the mean of dask-backed cftime arrays (#6940)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Deepak Cherian <dcherian@users.noreply.github.com>

Author: 3 people

ci/requirements/min-all-deps.yml

@@ -15,8 +15,8 @@ dependencies:
   - cfgrib=0.9
   - cftime=1.4
   - coveralls
-  - dask-core=2021.04
-  - distributed=2021.04
+  - dask-core=2021.08.0
+  - distributed=2021.08.0
   - flox=0.5
   - h5netcdf=0.11
   - h5py=3.1

doc/whats-new.rst

@@ -21,7 +21,10 @@ v2022.07.0 (unreleased)
 
 New Features
 ~~~~~~~~~~~~
-
+- Enable taking the mean of dask-backed :py:class:`cftime.datetime` arrays
+  (:pull:`6556`, :pull:`6940`).  By `Deepak Cherian
+  <https://github.com/dcherian>`_ and `Spencer Clark
+  <https://github.com/spencerkclark>`_.
 
 Breaking changes
 ~~~~~~~~~~~~~~~~
@@ -53,6 +56,9 @@ Bug fixes
   By `Oliver Lopez <https://github.com/lopezvoliver>`_.
 - Fix bug where index variables would be changed inplace (:issue:`6931`, :pull:`6938`)
   By `Michael Niklas <https://github.com/headtr1ck>`_.
+- Allow taking the mean over non-time dimensions of datasets containing
+  dask-backed cftime arrays (:issue:`5897`, :pull:`6950`).  By `Spencer Clark
+  <https://github.com/spencerkclark>`_.
 - Harmonize returned multi-indexed indexes when applying ``concat`` along new dimension (:issue:`6881`, :pull:`6889`)
   By `Fabian Hofmann <https://github.com/FabianHofmann>`_.
 - Fix step plots with ``hue`` arg. (:pull:`6944`)

xarray/core/duck_array_ops.py

@@ -426,7 +426,6 @@ def datetime_to_numeric(array, offset=None, datetime_unit=None, dtype=float):
     though some calendars would allow for them (e.g. no_leap). This is because there
     is no `cftime.timedelta` object.
     """
-    # TODO: make this function dask-compatible?
     # Set offset to minimum if not given
     if offset is None:
         if array.dtype.kind in "Mm":
@@ -531,7 +530,10 @@ def pd_timedelta_to_float(value, datetime_unit):
 
 
 def _timedelta_to_seconds(array):
-    return np.reshape([a.total_seconds() for a in array.ravel()], array.shape) * 1e6
+    if isinstance(array, datetime.timedelta):
+        return array.total_seconds() * 1e6
+    else:
+        return np.reshape([a.total_seconds() for a in array.ravel()], array.shape) * 1e6
 
 
 def py_timedelta_to_float(array, datetime_unit):
@@ -565,12 +567,6 @@ def mean(array, axis=None, skipna=None, **kwargs):
             + offset
         )
     elif _contains_cftime_datetimes(array):
-        if is_duck_dask_array(array):
-            raise NotImplementedError(
-                "Computing the mean of an array containing "
-                "cftime.datetime objects is not yet implemented on "
-                "dask arrays."
-            )
         offset = min(array)
         timedeltas = datetime_to_numeric(array, offset, datetime_unit="us")
         mean_timedeltas = _mean(timedeltas, axis=axis, skipna=skipna, **kwargs)

xarray/tests/test_duck_array_ops.py

@@ -323,18 +323,51 @@ def test_datetime_mean(dask: bool) -> None:
 
 
 @requires_cftime
-def test_cftime_datetime_mean():
+@pytest.mark.parametrize("dask", [False, True])
+def test_cftime_datetime_mean(dask):
+    if dask and not has_dask:
+        pytest.skip("requires dask")
+
     times = cftime_range("2000", periods=4)
     da = DataArray(times, dims=["time"])
+    da_2d = DataArray(times.values.reshape(2, 2))
 
-    assert da.isel(time=0).mean() == da.isel(time=0)
+    if dask:
+        da = da.chunk({"time": 2})
+        da_2d = da_2d.chunk({"dim_0": 2})
+
+    expected = da.isel(time=0)
+    # one compute needed to check the array contains cftime datetimes
+    with raise_if_dask_computes(max_computes=1):
+        result = da.isel(time=0).mean()
+    assert_dask_array(result, dask)
+    assert_equal(result, expected)
 
     expected = DataArray(times.date_type(2000, 1, 2, 12))
-    result = da.mean()
+    with raise_if_dask_computes(max_computes=1):
+        result = da.mean()
+    assert_dask_array(result, dask)
     assert_equal(result, expected)
 
-    da_2d = DataArray(times.values.reshape(2, 2))
-    result = da_2d.mean()
+    with raise_if_dask_computes(max_computes=1):
+        result = da_2d.mean()
+    assert_dask_array(result, dask)
+    assert_equal(result, expected)
+
+
+@requires_cftime
+@requires_dask
+def test_mean_over_non_time_dim_of_dataset_with_dask_backed_cftime_data():
+    # Regression test for part two of GH issue 5897: averaging over a non-time
+    # dimension still fails if the time variable is dask-backed.
+    ds = Dataset(
+        {
+            "var1": (("time",), cftime_range("2021-10-31", periods=10, freq="D")),
+            "var2": (("x",), list(range(10))),
+        }
+    )
+    expected = ds.mean("x")
+    result = ds.chunk({}).mean("x")
     assert_equal(result, expected)
 
 
@@ -372,15 +405,6 @@ def test_cftime_datetime_mean_long_time_period():
     assert_equal(result, expected)
 
 
-@requires_cftime
-@requires_dask
-def test_cftime_datetime_mean_dask_error():
-    times = cftime_range("2000", periods=4)
-    da = DataArray(times, dims=["time"]).chunk()
-    with pytest.raises(NotImplementedError):
-        da.mean()
-
-
 def test_empty_axis_dtype():
     ds = Dataset()
     ds["pos"] = [1, 2, 3]
@@ -742,6 +766,17 @@ def test_datetime_to_numeric_cftime(dask):
     expected = 24 * np.arange(0, 35, 7).astype(dtype)
     np.testing.assert_array_equal(result, expected)
 
+    with raise_if_dask_computes():
+        if dask:
+            time = dask.array.asarray(times[1])
+        else:
+            time = np.asarray(times[1])
+        result = duck_array_ops.datetime_to_numeric(
+            time, offset=times[0], datetime_unit="h", dtype=int
+        )
+    expected = np.array(24 * 7).astype(int)
+    np.testing.assert_array_equal(result, expected)
+
 
 @requires_cftime
 def test_datetime_to_numeric_potential_overflow():