handle all nan case for loess smoothing (#1699)

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### Pull Request Checklist:
- [ ] This PR addresses an already opened issue (for bug fixes /
features)
    - This PR fixes #xyz
- [x] Tests for the changes have been added (for bug fixes / features)
- [ ] (If applicable) Documentation has been added / updated (for bug
fixes / features)
- [x] CHANGES.rst has been updated (with summary of main changes)
- [x] Link to issue (:issue:`number`) and pull request (:pull:`number`)
has been added

### What kind of change does this PR introduce?

* Trying to fix https://stackoverflowteams.com/c/ouranos/questions/103
* When the axis is only nan and skipna=True, don't even try to calculate
a loess (because it fails), just return nan.

### Does this PR introduce a breaking change?
before the all nan case would fail.

### Other information:

CHANGES.rst

@@ -4,7 +4,7 @@ Changelog
 
 v0.49.0 (unreleased)
 --------------------
-Contributors to this version: Trevor James Smith (:user:`Zeitsperre`), Pascal Bourgault (:user:`aulemahal`).
+Contributors to this version: Trevor James Smith (:user:`Zeitsperre`), Pascal Bourgault (:user:`aulemahal`), Juliette Lavoie (:user:`juliettelavoie`).
 
 Announcements
 ^^^^^^^^^^^^^
@@ -14,6 +14,7 @@ Bug fixes
 ^^^^^^^^^
 * Fixed an bug in sdba's ``map_groups`` that prevented passing DataArrays with cftime coordinates if the ``sdba_encode_cf`` option was True. (:issue:`1673`, :pull:`1674`).
 * Fixed bug (:issue:`1678`, :pull:`1679`) in sdba where a loaded training dataset could not be used for adjustment
+* Fixed bug with loess smoothing for an array full of NaNs. (:pull:`1699`).
 * Fixed and adapted ``time_bnds`` to the newest xarray. (:pull:`1700`)
 
 Internal changes

tests/test_sdba/test_loess.py

@@ -1,7 +1,9 @@
 from __future__ import annotations
 
 import numpy as np
+import pandas as pd
 import pytest
+import xarray as xr
 
 from xclim.sdba.loess import _constant_regression  # noqa
 from xclim.sdba.loess import _gaussian_weighting  # noqa
@@ -62,3 +64,22 @@ def test_loess_smoothing(use_dask, open_dataset):
     # Same but we force not to use the optimization
     tasmooth3 = loess_smoothing(tas, f=0.1, equal_spacing=False)
     np.testing.assert_allclose(tasmooth, tasmooth3, rtol=1e-3, atol=1e-3)
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize("use_dask", [True, False])
+def test_loess_smoothing_nan(use_dask):
+    # create data with one axis full of nan
+    data = np.random.randn(2, 2, 10)
+    data[0, 0] = [np.nan] * 10
+    da = xr.DataArray(
+        data,
+        dims=["scenario", "model", "time"],
+        coords={"time": pd.date_range("2000-01-01", periods=10, freq="YS")},
+    ).chunk({"time": -1})
+
+    out = loess_smoothing(da)
+
+    assert out.dims == da.dims
+    # check that the output is all nan on the axis with nan in the input
+    assert np.isnan(out.values[0, 0]).all()

xclim/sdba/loess.py

@@ -95,6 +95,8 @@ def _loess_nb(
         out = np.full(x.size, np.NaN)
         y = y[~nan]
         x = x[~nan]
+        if x.size == 0:
+            return out
 
     n = x.size
     yest = np.zeros(n)