numpy 2.0 copy-keyword and trapz vs trapezoid

doc/whats-new.rst

@@ -59,9 +59,14 @@ Bug fixes
   By `Kai Mühlbauer <https://github.com/kmuehlbauer>`_.
 - do not cast `_FillValue`/`missing_value` in `CFMaskCoder` if `_Unsigned` is provided
   (:issue:`8844`, :pull:`8852`).
-- Adapt handling of copy keyword argument in scipy backend for numpy >= 2.0dev
-  (:issue:`8844`, :pull:`8851`).
+- Adapt handling of copy keyword argument for numpy >= 2.0dev
+  (:issue:`8844`, :pull:`8851`, :pull:`8865``).
   By `Kai Mühlbauer <https://github.com/kmuehlbauer>`_.
+- import trapz/trapezoid depending on numpy version.
+  (:issue:`8844`, :pull:`8865`).
+  By `Kai Mühlbauer <https://github.com/kmuehlbauer>`_.
+
+
 
 Documentation
 ~~~~~~~~~~~~~

xarray/coding/strings.py

@@ -15,10 +15,13 @@
     unpack_for_encoding,
 )
 from xarray.core import indexing
+from xarray.core.utils import module_available
 from xarray.core.variable import Variable
 from xarray.namedarray.parallelcompat import get_chunked_array_type
 from xarray.namedarray.pycompat import is_chunked_array
 
+HAS_NUMPY_2_0 = module_available("numpy", minversion="2.0.0.dev0")
+
 
 def create_vlen_dtype(element_type):
     if element_type not in (str, bytes):
@@ -156,8 +159,12 @@ def bytes_to_char(arr):
 
 def _numpy_bytes_to_char(arr):
     """Like netCDF4.stringtochar, but faster and more flexible."""
+    # adapt handling of copy-kwarg to numpy 2.0
+    # see https://github.com/numpy/numpy/issues/25916
+    # and https://github.com/numpy/numpy/pull/25922
+    copy = None if HAS_NUMPY_2_0 else False
     # ensure the array is contiguous
-    arr = np.array(arr, copy=False, order="C", dtype=np.bytes_)
+    arr = np.array(arr, copy=copy, order="C", dtype=np.bytes_)
     return arr.reshape(arr.shape + (1,)).view("S1")
 
 
@@ -199,8 +206,12 @@ def char_to_bytes(arr):
 
 def _numpy_char_to_bytes(arr):
     """Like netCDF4.chartostring, but faster and more flexible."""
+    # adapt handling of copy-kwarg to numpy 2.0
+    # see https://github.com/numpy/numpy/issues/25916
+    # and https://github.com/numpy/numpy/pull/25922
+    copy = None if HAS_NUMPY_2_0 else False
     # based on: http://stackoverflow.com/a/10984878/809705
-    arr = np.array(arr, copy=False, order="C")
+    arr = np.array(arr, copy=copy, order="C")
     dtype = "S" + str(arr.shape[-1])
     return arr.view(dtype).reshape(arr.shape[:-1])
 

xarray/tests/test_dataset.py

@@ -80,6 +80,13 @@
 except ImportError:
     pass
 
+# from numpy version 2.0 trapz is deprecated and renamed to trapezoid
+# remove once numpy 2.0 is the oldest supported version
+try:
+    from numpy import trapezoid  # type: ignore[attr-defined,unused-ignore]
+except ImportError:
+    from numpy import trapz as trapezoid
+
 sparse_array_type = array_type("sparse")
 
 pytestmark = [
@@ -6999,7 +7006,7 @@ def test_integrate(dask) -> None:
     actual = da.integrate("x")
     # coordinate that contains x should be dropped.
     expected_x = xr.DataArray(
-        np.trapz(da.compute(), da["x"], axis=0),
+        trapezoid(da.compute(), da["x"], axis=0),
         dims=["y"],
         coords={k: v for k, v in da.coords.items() if "x" not in v.dims},
     )
@@ -7012,7 +7019,7 @@ def test_integrate(dask) -> None:
     # along y
     actual = da.integrate("y")
     expected_y = xr.DataArray(
-        np.trapz(da, da["y"], axis=1),
+        trapezoid(da, da["y"], axis=1),
         dims=["x"],
         coords={k: v for k, v in da.coords.items() if "y" not in v.dims},
     )
@@ -7093,7 +7100,7 @@ def test_cumulative_integrate(dask) -> None:
 @pytest.mark.filterwarnings("ignore:Converting non-nanosecond")
 @pytest.mark.parametrize("dask", [True, False])
 @pytest.mark.parametrize("which_datetime", ["np", "cftime"])
-def test_trapz_datetime(dask, which_datetime) -> None:
+def test_trapezoid_datetime(dask, which_datetime) -> None:
     rs = np.random.RandomState(42)
     if which_datetime == "np":
         coord = np.array(
@@ -7124,7 +7131,7 @@ def test_trapz_datetime(dask, which_datetime) -> None:
         da = da.chunk({"time": 4})
 
     actual = da.integrate("time", datetime_unit="D")
-    expected_data = np.trapz(
+    expected_data = trapezoid(
         da.compute().data,
         duck_array_ops.datetime_to_numeric(da["time"].data, datetime_unit="D"),
         axis=0,