Use duck array ops in more places (#8267)

* Use duck array ops for `reshape`

* Use duck array ops for `sum`

* Use duck array ops for `astype`

* Use duck array ops for `ravel`

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Update what's new

---------

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

doc/whats-new.rst

@@ -56,6 +56,10 @@ Documentation
 Internal Changes
 ~~~~~~~~~~~~~~~~
 
+- More improvements to support the Python `array API standard <https://data-apis.org/array-api/latest/>`_
+  by using duck array ops in more places in the codebase. (:pull:`8267`)
+  By `Tom White <https://github.com/tomwhite>`_.
+
 
 .. _whats-new.2023.09.0:
 

xarray/core/accessor_dt.py

@@ -7,6 +7,7 @@
 import pandas as pd
 
 from xarray.coding.times import infer_calendar_name
+from xarray.core import duck_array_ops
 from xarray.core.common import (
     _contains_datetime_like_objects,
     is_np_datetime_like,
@@ -50,7 +51,7 @@ def _access_through_cftimeindex(values, name):
     from xarray.coding.cftimeindex import CFTimeIndex
 
     if not isinstance(values, CFTimeIndex):
-        values_as_cftimeindex = CFTimeIndex(values.ravel())
+        values_as_cftimeindex = CFTimeIndex(duck_array_ops.ravel(values))
     else:
         values_as_cftimeindex = values
     if name == "season":
@@ -69,7 +70,7 @@ def _access_through_series(values, name):
     """Coerce an array of datetime-like values to a pandas Series and
     access requested datetime component
     """
-    values_as_series = pd.Series(values.ravel(), copy=False)
+    values_as_series = pd.Series(duck_array_ops.ravel(values), copy=False)
     if name == "season":
         months = values_as_series.dt.month.values
         field_values = _season_from_months(months)
@@ -148,10 +149,10 @@ def _round_through_series_or_index(values, name, freq):
     from xarray.coding.cftimeindex import CFTimeIndex
 
     if is_np_datetime_like(values.dtype):
-        values_as_series = pd.Series(values.ravel(), copy=False)
+        values_as_series = pd.Series(duck_array_ops.ravel(values), copy=False)
         method = getattr(values_as_series.dt, name)
     else:
-        values_as_cftimeindex = CFTimeIndex(values.ravel())
+        values_as_cftimeindex = CFTimeIndex(duck_array_ops.ravel(values))
         method = getattr(values_as_cftimeindex, name)
 
     field_values = method(freq=freq).values
@@ -195,7 +196,7 @@ def _strftime_through_cftimeindex(values, date_format: str):
     """
     from xarray.coding.cftimeindex import CFTimeIndex
 
-    values_as_cftimeindex = CFTimeIndex(values.ravel())
+    values_as_cftimeindex = CFTimeIndex(duck_array_ops.ravel(values))
 
     field_values = values_as_cftimeindex.strftime(date_format)
     return field_values.values.reshape(values.shape)
@@ -205,7 +206,7 @@ def _strftime_through_series(values, date_format: str):
     """Coerce an array of datetime-like values to a pandas Series and
     apply string formatting
     """
-    values_as_series = pd.Series(values.ravel(), copy=False)
+    values_as_series = pd.Series(duck_array_ops.ravel(values), copy=False)
     strs = values_as_series.dt.strftime(date_format)
     return strs.values.reshape(values.shape)
 

xarray/core/computation.py

@@ -2123,7 +2123,8 @@ def _calc_idxminmax(
         chunkmanager = get_chunked_array_type(array.data)
         chunks = dict(zip(array.dims, array.chunks))
         dask_coord = chunkmanager.from_array(array[dim].data, chunks=chunks[dim])
-        res = indx.copy(data=dask_coord[indx.data.ravel()].reshape(indx.shape))
+        data = dask_coord[duck_array_ops.ravel(indx.data)]
+        res = indx.copy(data=duck_array_ops.reshape(data, indx.shape))
         # we need to attach back the dim name
         res.name = dim
     else:

xarray/core/duck_array_ops.py

@@ -337,6 +337,10 @@ def reshape(array, shape):
     return xp.reshape(array, shape)
 
 
+def ravel(array):
+    return reshape(array, (-1,))
+
+
 @contextlib.contextmanager
 def _ignore_warnings_if(condition):
     if condition:
@@ -363,7 +367,7 @@ def f(values, axis=None, skipna=None, **kwargs):
         values = asarray(values)
 
         if coerce_strings and values.dtype.kind in "SU":
-            values = values.astype(object)
+            values = astype(values, object)
 
         func = None
         if skipna or (skipna is None and values.dtype.kind in "cfO"):

xarray/core/nanops.py

@@ -4,7 +4,7 @@
 
 import numpy as np
 
-from xarray.core import dtypes, nputils, utils
+from xarray.core import dtypes, duck_array_ops, nputils, utils
 from xarray.core.duck_array_ops import (
     astype,
     count,
@@ -21,12 +21,16 @@ def _maybe_null_out(result, axis, mask, min_count=1):
     xarray version of pandas.core.nanops._maybe_null_out
     """
     if axis is not None and getattr(result, "ndim", False):
-        null_mask = (np.take(mask.shape, axis).prod() - mask.sum(axis) - min_count) < 0
+        null_mask = (
+            np.take(mask.shape, axis).prod()
+            - duck_array_ops.sum(mask, axis)
+            - min_count
+        ) < 0
         dtype, fill_value = dtypes.maybe_promote(result.dtype)
         result = where(null_mask, fill_value, astype(result, dtype))
 
     elif getattr(result, "dtype", None) not in dtypes.NAT_TYPES:
-        null_mask = mask.size - mask.sum()
+        null_mask = mask.size - duck_array_ops.sum(mask)
         result = where(null_mask < min_count, np.nan, result)
 
     return result

xarray/core/variable.py

@@ -2324,7 +2324,7 @@ def coarsen_reshape(self, windows, boundary, side):
             else:
                 shape.append(variable.shape[i])
 
-        return variable.data.reshape(shape), tuple(axes)
+        return duck_array_ops.reshape(variable.data, shape), tuple(axes)
 
     def isnull(self, keep_attrs: bool | None = None):
         """Test each value in the array for whether it is a missing value.

xarray/tests/test_coarsen.py

@@ -6,6 +6,7 @@
 
 import xarray as xr
 from xarray import DataArray, Dataset, set_options
+from xarray.core import duck_array_ops
 from xarray.tests import (
     assert_allclose,
     assert_equal,
@@ -272,21 +273,24 @@ def test_coarsen_construct(self, dask: bool) -> None:
         expected = xr.Dataset(attrs={"foo": "bar"})
         expected["vart"] = (
             ("year", "month"),
-            ds.vart.data.reshape((-1, 12)),
+            duck_array_ops.reshape(ds.vart.data, (-1, 12)),
             {"a": "b"},
         )
         expected["varx"] = (
             ("x", "x_reshaped"),
-            ds.varx.data.reshape((-1, 5)),
+            duck_array_ops.reshape(ds.varx.data, (-1, 5)),
             {"a": "b"},
         )
         expected["vartx"] = (
             ("x", "x_reshaped", "year", "month"),
-            ds.vartx.data.reshape(2, 5, 4, 12),
+            duck_array_ops.reshape(ds.vartx.data, (2, 5, 4, 12)),
             {"a": "b"},
         )
         expected["vary"] = ds.vary
-        expected.coords["time"] = (("year", "month"), ds.time.data.reshape((-1, 12)))
+        expected.coords["time"] = (
+            ("year", "month"),
+            duck_array_ops.reshape(ds.time.data, (-1, 12)),
+        )
 
         with raise_if_dask_computes():
             actual = ds.coarsen(time=12, x=5).construct(

xarray/tests/test_variable.py

@@ -916,7 +916,7 @@ def test_pad_constant_values(self, xr_arg, np_arg):
 
         actual = v.pad(**xr_arg)
         expected = np.pad(
-            np.array(v.data.astype(float)),
+            np.array(duck_array_ops.astype(v.data, float)),
             np_arg,
             mode="constant",
             constant_values=np.nan,