Merge branch 'main' into grouper-public-api

* main:
  exclude the bots from the release notes (pydata#9235)
  switch the documentation to run with `numpy>=2` (pydata#9177)
  `numpy` 2 compatibility in the iris code paths (pydata#9156)
  `numpy` 2 compatibility in the `netcdf4` and `h5netcdf` backends (pydata#9136)
  Fix time indexing regression in `convert_calendar` (pydata#9192)
  Use duckarray assertions in test_coding_times (pydata#9226)
  Use reshape and ravel from duck_array_ops in coding/times.py (pydata#9225)
  Cleanup test_coding_times.py (pydata#9223)
  Only use necessary dims when creating temporary dataarray (pydata#9206)
  Fix two bugs in DataTree.update() (pydata#9214)
  Use numpy 2.0-compat `np.complex64` dtype in test (pydata#9217)

Author: dcherian

.github/release.yml

@@ -0,0 +1,5 @@
+changelog:
+  exclude:
+    authors:
+      - dependabot
+      - pre-commit-ci

ci/install-upstream-wheels.sh

@@ -13,7 +13,7 @@ $conda remove -y numba numbagg sparse
 # temporarily remove numexpr
 $conda remove -y numexpr
 # temporarily remove backends
-$conda remove -y cf_units hdf5 h5py netcdf4 pydap
+$conda remove -y pydap
 # forcibly remove packages to avoid artifacts
 $conda remove -y --force \
     numpy \
@@ -37,8 +37,7 @@ python -m pip install \
     numpy \
     scipy \
     matplotlib \
-    pandas \
-    h5py
+    pandas
 # 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 \

ci/requirements/all-but-dask.yml

@@ -22,7 +22,7 @@ dependencies:
   - netcdf4
   - numba
   - numbagg
-  - numpy<2
+  - numpy
   - packaging
   - pandas
   - pint>=0.22

ci/requirements/doc.yml

@@ -21,7 +21,7 @@ dependencies:
   - nbsphinx
   - netcdf4>=1.5
   - numba
-  - numpy>=1.21,<2
+  - numpy>=2
   - packaging>=21.3
   - pandas>=1.4,!=2.1.0
   - pooch

ci/requirements/environment-windows.yml

@@ -23,7 +23,7 @@ dependencies:
   - netcdf4
   - numba
   - numbagg
-  - numpy<2
+  - numpy
   - packaging
   - pandas
   # - pint>=0.22

ci/requirements/environment.yml

@@ -26,7 +26,7 @@ dependencies:
   - numba
   - numbagg
   - numexpr
-  - numpy<2
+  - numpy
   - opt_einsum
   - packaging
   - pandas

doc/getting-started-guide/faq.rst

@@ -352,9 +352,9 @@ Some packages may have additional functionality beyond what is shown here. You c
 How does xarray handle missing values?
 --------------------------------------
 
-**xarray can handle missing values using ``np.NaN``**
+**xarray can handle missing values using ``np.nan``**
 
-- ``np.NaN`` is  used to represent missing values in labeled arrays and datasets. It is a commonly used standard for representing missing or undefined numerical data in scientific computing. ``np.NaN`` is a constant value in NumPy that represents "Not a Number" or missing values.
+- ``np.nan`` is  used to represent missing values in labeled arrays and datasets. It is a commonly used standard for representing missing or undefined numerical data in scientific computing. ``np.nan`` is a constant value in NumPy that represents "Not a Number" or missing values.
 
 - Most of xarray's computation methods are designed to automatically handle missing values appropriately.
 

doc/user-guide/computation.rst

@@ -426,7 +426,7 @@ However, the functions also take missing values in the data into account:
 
 .. ipython:: python
 
-    data = xr.DataArray([np.NaN, 2, 4])
+    data = xr.DataArray([np.nan, 2, 4])
     weights = xr.DataArray([8, 1, 1])
 
     data.weighted(weights).mean()
@@ -444,7 +444,7 @@ If the weights add up to to 0, ``sum`` returns 0:
 
     data.weighted(weights).sum()
 
-and ``mean``, ``std`` and ``var`` return ``NaN``:
+and ``mean``, ``std`` and ``var`` return ``nan``:
 
 .. ipython:: python
 

doc/user-guide/interpolation.rst

@@ -292,8 +292,8 @@ Let's see how :py:meth:`~xarray.DataArray.interp` works on real data.
     axes[0].set_title("Raw data")
 
     # Interpolated data
-    new_lon = np.linspace(ds.lon[0], ds.lon[-1], ds.sizes["lon"] * 4)
-    new_lat = np.linspace(ds.lat[0], ds.lat[-1], ds.sizes["lat"] * 4)
+    new_lon = np.linspace(ds.lon[0].item(), ds.lon[-1].item(), ds.sizes["lon"] * 4)
+    new_lat = np.linspace(ds.lat[0].item(), ds.lat[-1].item(), ds.sizes["lat"] * 4)
     dsi = ds.interp(lat=new_lat, lon=new_lon)
     dsi.air.plot(ax=axes[1])
     @savefig interpolation_sample3.png width=8in

doc/user-guide/testing.rst

@@ -239,9 +239,10 @@ If the array type you want to generate has an array API-compliant top-level name
 you can use this neat trick:
 
 .. ipython:: python
-    :okwarning:
 
-    from numpy import array_api as xp  # available in numpy 1.26.0
+    import numpy as xp  # compatible in numpy 2.0
+
+    # use `import numpy.array_api as xp` in numpy>=1.23,<2.0
 
     from hypothesis.extra.array_api import make_strategies_namespace