more engine environment tricks in preparation for numpy>=2

ci/install-upstream-wheels.sh

@@ -1,13 +1,13 @@
 #!/usr/bin/env bash
 
 # install cython for building cftime without build isolation
-micromamba install "cython>=0.29.20" py-cpuinfo
+micromamba install "cython>=0.29.20" py-cpuinfo setuptools-scm
 # temporarily (?) remove numbagg and numba
 micromamba remove -y numba numbagg sparse
 # temporarily remove numexpr
 micromamba remove -y numexpr
 # temporarily remove backends
-micromamba remove -y cf_units hdf5 h5py netcdf4
+micromamba remove -y cf_units hdf5 h5py netcdf4 pydap
 # forcibly remove packages to avoid artifacts
 micromamba remove -y --force \
     numpy \
@@ -31,7 +31,8 @@ python -m pip install \
     numpy \
     scipy \
     matplotlib \
-    pandas
+    pandas \
+    h5py
 # for some reason pandas depends on pyarrow already.
 # Remove once a `pyarrow` version compiled with `numpy>=2.0` is on `conda-forge`
 python -m pip install \
@@ -70,6 +71,6 @@ python -m pip install \
     git+https://github.com/intake/filesystem_spec \
     git+https://github.com/SciTools/nc-time-axis \
     git+https://github.com/xarray-contrib/flox \
+    git+https://github.com/h5netcdf/h5netcdf \
     git+https://github.com/dgasmith/opt_einsum
     # git+https://github.com/pydata/sparse
-    # git+https://github.com/h5netcdf/h5netcdf

xarray/tests/__init__.py

@@ -142,8 +142,36 @@ def _importorskip(
     not has_scipy_or_netCDF4, reason="requires scipy or netCDF4"
 )
 has_numpy_array_api, requires_numpy_array_api = _importorskip("numpy", "1.26.0")
-has_h5netcdf_ros3, requires_h5netcdf_ros3 = _importorskip("h5netcdf", "1.3.0")
 
+
+def _importorskip_h5netcdf_ros3():
+    try:
+        import h5netcdf
+
+        has_h5netcdf = True
+    except ImportError:
+        has_h5netcdf = False
+
+    if not has_h5netcdf:
+        return has_h5netcdf, pytest.mark.skipif(
+            not has_h5netcdf, reason="requires h5netcdf"
+        )
+
+    h5netcdf_with_ros3 = Version(h5netcdf.__version__) >= Version("1.3.0")
+
+    import h5py
+
+    h5py_with_ros3 = h5py.get_config().ros3
+
+    has_h5netcdf_ros3 = h5netcdf_with_ros3 and h5py_with_ros3
+
+    return has_h5netcdf_ros3, pytest.mark.skipif(
+        not has_h5netcdf_ros3,
+        reason="requires h5netcdf>=1.3.0 and h5py with ros3 support",
+    )
+
+
+has_h5netcdf_ros3, requires_h5netcdf_ros3 = _importorskip_h5netcdf_ros3()
 has_netCDF4_1_6_2_or_above, requires_netCDF4_1_6_2_or_above = _importorskip(
     "netCDF4", "1.6.2"
 )

xarray/tests/test_formatting.py

@@ -6,7 +6,6 @@
 import numpy as np
 import pandas as pd
 import pytest
-from numpy.core import defchararray
 
 import xarray as xr
 from xarray.core import formatting
@@ -770,9 +769,9 @@ def test_repr_file_collapsed(tmp_path) -> None:
 )
 def test__mapping_repr(display_max_rows, n_vars, n_attr) -> None:
     long_name = "long_name"
-    a = defchararray.add(long_name, np.arange(0, n_vars).astype(str))
-    b = defchararray.add("attr_", np.arange(0, n_attr).astype(str))
-    c = defchararray.add("coord", np.arange(0, n_vars).astype(str))
+    a = np.char.add(long_name, np.arange(0, n_vars).astype(str))
+    b = np.char.add("attr_", np.arange(0, n_attr).astype(str))
+    c = np.char.add("coord", np.arange(0, n_vars).astype(str))
     attrs = {k: 2 for k in b}
     coords = {_c: np.array([0, 1], dtype=np.uint64) for _c in c}
     data_vars = dict()