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[PR]: Enable skipna for spatial and temporal mean operations #655

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[PR]: Enable skipna for spatial and temporal mean operations #655

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f3ef9df
add skipna parameter
lee1043 May 31, 2024
ad5e9e4
add skipna parameter
lee1043 May 31, 2024
8204188
add skipna where it was missed
lee1043 May 31, 2024
b65545b
add parameter documentation
lee1043 Jun 5, 2024
888b294
make sure time diff type is timedelta: convert timedelta64 to timedel…
lee1043 Jun 6, 2024
7b6da1d
Use `Union` for skipna to allow bool or None, instead of using `|` op…
lee1043 Jun 6, 2024
8493958
Use `Union` for skipna to allow bool or None, instead of using `|` op…
lee1043 Jun 6, 2024
4bdd333
retract the change for timedelta conversion to make a separate PR
lee1043 Jun 6, 2024
c85c309
Add `skipna` arg to all temporal APIs
tomvothecoder Jun 24, 2024
1ce0fe1
Add annotations import to `temporal.py`
tomvothecoder Nov 6, 2024
9847dd7
Restore code in `bounds.py` that was accidentally deleted
tomvothecoder Nov 6, 2024
e554f6f
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Jan 31, 2025
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22 changes: 22 additions & 0 deletions tests/test_spatial.py
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Original file line number Diff line number Diff line change
Expand Up @@ -180,6 +180,28 @@ def test_spatial_average_for_lat_region(self):

assert result.identical(expected)

def test_spatial_average_for_lat_region_and_skipna(self):
ds = self.ds.copy(deep=True)
ds.ts[0] = np.nan

# Specifying axis as a str instead of list of str.
result = ds.spatial.average("ts", axis=["Y"], lat_bounds=(-5.0, 5), skipna=True)

expected = self.ds.copy()
expected["ts"] = xr.DataArray(
data=np.array(
[
[np.nan, np.nan, np.nan, np.nan],
[1.0, 1.0, 1.0, 1.0],
[1.0, 1.0, 1.0, 1.0],
]
),
coords={"time": expected.time, "lon": expected.lon},
dims=["time", "lon"],
)

assert result.identical(expected)

def test_spatial_average_for_domain_wrapping_p_meridian_non_cf_conventions(
self,
):
Expand Down
169 changes: 169 additions & 0 deletions tests/test_temporal.py
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Original file line number Diff line number Diff line change
Expand Up @@ -523,6 +523,57 @@ def test_weighted_annual_averages(self):
assert result.ts.attrs == expected.ts.attrs
assert result.time.attrs == expected.time.attrs

def test_weighted_annual_averages_and_skipna(self):
ds = self.ds.copy(deep=True)
ds.ts[0] = np.nan

result = ds.temporal.group_average("ts", "year", skipna=True)
expected = ds.copy()
expected = expected.drop_dims("time")
expected["ts"] = xr.DataArray(
name="ts",
data=np.array([[[1]], [[2.0]]]),
coords={
"lat": expected.lat,
"lon": expected.lon,
"time": xr.DataArray(
data=np.array(
[
cftime.DatetimeGregorian(2000, 1, 1),
cftime.DatetimeGregorian(2001, 1, 1),
],
),
coords={
"time": np.array(
[
cftime.DatetimeGregorian(2000, 1, 1),
cftime.DatetimeGregorian(2001, 1, 1),
],
)
},
dims=["time"],
attrs={
"axis": "T",
"long_name": "time",
"standard_name": "time",
"bounds": "time_bnds",
},
),
},
dims=["time", "lat", "lon"],
attrs={
"test_attr": "test",
"operation": "temporal_avg",
"mode": "group_average",
"freq": "year",
"weighted": "True",
},
)

xr.testing.assert_allclose(result, expected)
assert result.ts.attrs == expected.ts.attrs
assert result.time.attrs == expected.time.attrs

@requires_dask
def test_weighted_annual_averages_with_chunking(self):
ds = self.ds.copy().chunk({"time": 2})
Expand Down Expand Up @@ -1455,6 +1506,68 @@ def test_raises_deprecation_warning_with_drop_incomplete_djf_season_config(self)

xr.testing.assert_identical(result, expected)

def test_weighted_seasonal_climatology_with_DJF_and_skipna(self):
ds = self.ds.copy(deep=True)

# Replace all MAM values with np.nan.
djf_months = [3, 4, 5]
for mon in djf_months:
ds["ts"] = ds.ts.where(ds.ts.time.dt.month != mon, np.nan)

result = ds.temporal.climatology(
"ts",
"season",
season_config={"dec_mode": "DJF", "drop_incomplete_djf": True},
skipna=True,
)

expected = ds.copy()
expected = expected.drop_dims("time")
expected_time = xr.DataArray(
data=np.array(
[
cftime.DatetimeGregorian(1, 1, 1),
cftime.DatetimeGregorian(1, 4, 1),
cftime.DatetimeGregorian(1, 7, 1),
cftime.DatetimeGregorian(1, 10, 1),
],
),
coords={
"time": np.array(
[
cftime.DatetimeGregorian(1, 1, 1),
cftime.DatetimeGregorian(1, 4, 1),
cftime.DatetimeGregorian(1, 7, 1),
cftime.DatetimeGregorian(1, 10, 1),
],
),
},
attrs={
"axis": "T",
"long_name": "time",
"standard_name": "time",
"bounds": "time_bnds",
},
)
expected["ts"] = xr.DataArray(
name="ts",
data=np.ones((4, 4, 4)),
coords={"lat": expected.lat, "lon": expected.lon, "time": expected_time},
dims=["time", "lat", "lon"],
attrs={
"operation": "temporal_avg",
"mode": "climatology",
"freq": "season",
"weighted": "True",
"dec_mode": "DJF",
"drop_incomplete_djf": "True",
},
)
expected.ts[1] = np.nan

# MAM should be np.nan
assert result.identical(expected)

@requires_dask
def test_chunked_weighted_seasonal_climatology_with_DJF(self):
ds = self.ds.copy().chunk({"time": 2})
Expand Down Expand Up @@ -2296,6 +2409,62 @@ def test_weighted_seasonal_departures_with_DJF(self):

xr.testing.assert_identical(result, expected)

def test_weighted_seasonal_departures_with_DJF_and_skipna(self):
ds = self.ds.copy(deep=True)

# Replace all MAM values with np.nan.
djf_months = [3, 4, 5]
for mon in djf_months:
ds["ts"] = ds.ts.where(ds.ts.time.dt.month != mon, np.nan)

result = ds.temporal.departures(
"ts",
"season",
weighted=True,
season_config={"dec_mode": "DJF", "drop_incomplete_djf": True},
skipna=True,
)

expected = ds.copy()
expected = expected.drop_dims("time")
expected["ts"] = xr.DataArray(
name="ts",
data=np.array([[[np.nan]], [[0.0]], [[0.0]], [[0.0]]]),
coords={
"lat": expected.lat,
"lon": expected.lon,
"time": xr.DataArray(
data=np.array(
[
cftime.DatetimeGregorian(2000, 4, 1),
cftime.DatetimeGregorian(2000, 7, 1),
cftime.DatetimeGregorian(2000, 10, 1),
cftime.DatetimeGregorian(2001, 1, 1),
],
),
dims=["time"],
attrs={
"axis": "T",
"long_name": "time",
"standard_name": "time",
"bounds": "time_bnds",
},
),
},
dims=["time", "lat", "lon"],
attrs={
"test_attr": "test",
"operation": "temporal_avg",
"mode": "departures",
"freq": "season",
"weighted": "True",
"dec_mode": "DJF",
"drop_incomplete_djf": "True",
},
)

assert result.identical(expected)

def test_weighted_seasonal_departures_with_DJF_and_keep_weights(self):
ds = self.ds.copy()

Expand Down
22 changes: 18 additions & 4 deletions xcdat/spatial.py
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Original file line number Diff line number Diff line change
Expand Up @@ -76,6 +76,7 @@ def average(
keep_weights: bool = False,
lat_bounds: Optional[RegionAxisBounds] = None,
lon_bounds: Optional[RegionAxisBounds] = None,
skipna: Union[bool, None] = None,
) -> xr.Dataset:
"""
Calculates the spatial average for a rectilinear grid over an optionally
Expand Down Expand Up @@ -125,6 +126,11 @@ def average(
ignored if ``weights`` are supplied. The lower bound can be larger
than the upper bound (e.g., across the prime meridian, dateline), by
default None.
skipna : bool or None, optional
If True, skip missing values (as marked by NaN). By default, only
skips missing values for float dtypes; other dtypes either do not
have a sentinel missing value (int) or ``skipna=True`` has not been
implemented (object, datetime64 or timedelta64).

Returns
-------
Expand Down Expand Up @@ -196,7 +202,7 @@ def average(
self._weights = weights

self._validate_weights(dv, axis)
ds[dv.name] = self._averager(dv, axis)
ds[dv.name] = self._averager(dv, axis, skipna=skipna)

if keep_weights:
ds[self._weights.name] = self._weights
Expand Down Expand Up @@ -702,8 +708,11 @@ def _validate_weights(
)

def _averager(
self, data_var: xr.DataArray, axis: List[SpatialAxis] | Tuple[SpatialAxis, ...]
):
self,
data_var: xr.DataArray,
axis: List[SpatialAxis] | Tuple[SpatialAxis, ...],
skipna: bool | None = None,
) -> xr.DataArray:
"""Perform a weighted average of a data variable.

This method assumes all specified keys in ``axis`` exists in the data
Expand All @@ -721,6 +730,11 @@ def _averager(
Data variable inside a Dataset.
axis : List[SpatialAxis] | Tuple[SpatialAxis, ...]
List of axis dimensions to average over.
skipna : bool or None, optional
If True, skip missing values (as marked by NaN). By default, only
skips missing values for float dtypes; other dtypes either do not
have a sentinel missing value (int) or ``skipna=True`` has not been
implemented (object, datetime64 or timedelta64).

Returns
-------
Expand All @@ -739,6 +753,6 @@ def _averager(
dim.append(get_dim_keys(data_var, key))

with xr.set_options(keep_attrs=True):
weighted_mean = data_var.cf.weighted(weights).mean(dim=dim)
weighted_mean = data_var.cf.weighted(weights).mean(dim=dim, skipna=skipna)

return weighted_mean
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