Make parallel documentation builds threadsafe

.gitignore

@@ -84,6 +84,9 @@ doc/team-panel.txt
 doc/external-examples-gallery.txt
 doc/notebooks-examples-gallery.txt
 doc/videos-gallery.txt
+doc/*.zarr
+doc/*.nc
+doc/*.h5
 
 # Until we support this properly, excluding from gitignore. (adding it to
 # gitignore to make it _easier_ to work with `uv`, not as an indication that I

doc/conf.py

@@ -178,11 +178,6 @@
 # mermaid config
 mermaid_version = "11.6.0"
 
-# sphinx-llm config
-# Some jupyter-execute cells are not thread-safe, so we need to build sequentially.
-# See https://github.com/pydata/xarray/pull/11003#issuecomment-3641648868
-llms_txt_build_parallel = False
-
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ["_templates", sphinx_autosummary_accessors.templates_path]
 

doc/getting-started-guide/quick-overview.rst

@@ -213,17 +213,32 @@ You can directly read and write xarray objects to disk using :py:meth:`~xarray.D
 
 .. jupyter-execute::
 
-    ds.to_netcdf("example.nc")
-    reopened = xr.open_dataset("example.nc")
-    reopened
+    filename = "example.nc"
 
 .. jupyter-execute::
     :hide-code:
 
-    import os
+    # Ensure the file is located in a unique temporary directory
+    # so that it doesn't conflict with parallel builds of the
+    # documentation.
+
+    import tempfile
+    import os.path
+
+    tempdir = tempfile.TemporaryDirectory()
+    filename = os.path.join(tempdir.name, filename)
+
+.. jupyter-execute::
+
+    ds.to_netcdf(filename)
+    reopened = xr.open_dataset(filename)
+    reopened
+
+.. jupyter-execute::
+    :hide-code:
 
     reopened.close()
-    os.remove("example.nc")
+    tempdir.cleanup()
 
 
 It is common for datasets to be distributed across multiple files (commonly one file per timestep). Xarray supports this use-case by providing the :py:meth:`~xarray.open_mfdataset` and the :py:meth:`~xarray.save_mfdataset` methods. For more, see :ref:`io`.

doc/internals/time-coding.rst

@@ -459,59 +459,94 @@ Default Time Unit
 
 The current default time unit of xarray is ``'ns'``. When setting keyword argument ``time_unit`` unit to ``'s'`` (the lowest resolution pandas allows) datetimes will be converted to at least ``'s'``-resolution, if possible. The same holds true for ``'ms'`` and ``'us'``.
 
+.. jupyter-execute::
+
+    datetimes1_filename = "test-datetimes1.nc"
+
+.. jupyter-execute::
+    :hide-code:
+
+    # Ensure the file is located in a unique temporary directory
+    # so that it doesn't conflict with parallel builds of the
+    # documentation.
+
+    import tempfile
+    import os.path
+
+    tempdir = tempfile.TemporaryDirectory()
+    datetimes1_filename = os.path.join(tempdir.name, datetimes1_filename)
+
 .. jupyter-execute::
 
     attrs = {"units": "hours since 2000-01-01"}
     ds = xr.Dataset({"time": ("time", [0, 1, 2, 3], attrs)})
-    ds.to_netcdf("test-datetimes1.nc")
+    ds.to_netcdf(datetimes1_filename)
 
 .. jupyter-execute::
 
-    xr.open_dataset("test-datetimes1.nc")
+    xr.open_dataset(datetimes1_filename)
 
 .. jupyter-execute::
 
     coder = xr.coders.CFDatetimeCoder(time_unit="s")
-    xr.open_dataset("test-datetimes1.nc", decode_times=coder)
+    xr.open_dataset(datetimes1_filename, decode_times=coder)
 
 If a coarser unit is requested the datetimes are decoded into their native
 on-disk resolution, if possible.
 
+.. jupyter-execute::
+
+    datetimes2_filename = "test-datetimes2.nc"
+
+.. jupyter-execute::
+    :hide-code:
+
+    datetimes2_filename = os.path.join(tempdir.name, datetimes2_filename)
+
 .. jupyter-execute::
 
     attrs = {"units": "milliseconds since 2000-01-01"}
     ds = xr.Dataset({"time": ("time", [0, 1, 2, 3], attrs)})
-    ds.to_netcdf("test-datetimes2.nc")
+    ds.to_netcdf(datetimes2_filename)
 
 .. jupyter-execute::
 
-    xr.open_dataset("test-datetimes2.nc")
+    xr.open_dataset(datetimes2_filename)
 
 .. jupyter-execute::
 
     coder = xr.coders.CFDatetimeCoder(time_unit="s")
-    xr.open_dataset("test-datetimes2.nc", decode_times=coder)
+    xr.open_dataset(datetimes2_filename, decode_times=coder)
 
 Similar logic applies for decoding timedelta values. The default resolution is
 ``"ns"``:
 
+.. jupyter-execute::
+
+    timedeltas1_filename = "test-timedeltas1.nc"
+
+.. jupyter-execute::
+    :hide-code:
+
+    timedeltas1_filename = os.path.join(tempdir.name, timedeltas1_filename)
+
 .. jupyter-execute::
 
     attrs = {"units": "hours"}
     ds = xr.Dataset({"time": ("time", [0, 1, 2, 3], attrs)})
-    ds.to_netcdf("test-timedeltas1.nc")
+    ds.to_netcdf(timedeltas1_filename)
 
 .. jupyter-execute::
     :stderr:
 
-    xr.open_dataset("test-timedeltas1.nc")
+    xr.open_dataset(timedeltas1_filename)
 
 By default, timedeltas will be decoded to the same resolution as datetimes:
 
 .. jupyter-execute::
 
     coder = xr.coders.CFDatetimeCoder(time_unit="s")
-    xr.open_dataset("test-timedeltas1.nc", decode_times=coder, decode_timedelta=True)
+    xr.open_dataset(timedeltas1_filename, decode_times=coder, decode_timedelta=True)
 
 but if one would like to decode timedeltas to a different resolution, one can
 provide a coder specifically for timedeltas to ``decode_timedelta``:
@@ -520,32 +555,41 @@ provide a coder specifically for timedeltas to ``decode_timedelta``:
 
     timedelta_coder = xr.coders.CFTimedeltaCoder(time_unit="ms")
     xr.open_dataset(
-        "test-timedeltas1.nc", decode_times=coder, decode_timedelta=timedelta_coder
+        timedeltas1_filename, decode_times=coder, decode_timedelta=timedelta_coder
     )
 
 As with datetimes, if a coarser unit is requested the timedeltas are decoded
 into their native on-disk resolution, if possible:
 
+.. jupyter-execute::
+
+    timedeltas2_filename = "test-timedeltas2.nc"
+
+.. jupyter-execute::
+    :hide-code:
+
+    timedeltas2_filename = os.path.join(tempdir.name, timedeltas2_filename)
+
 .. jupyter-execute::
 
     attrs = {"units": "milliseconds"}
     ds = xr.Dataset({"time": ("time", [0, 1, 2, 3], attrs)})
-    ds.to_netcdf("test-timedeltas2.nc")
+    ds.to_netcdf(timedeltas2_filename)
 
 .. jupyter-execute::
 
-    xr.open_dataset("test-timedeltas2.nc", decode_timedelta=True)
+    xr.open_dataset(timedeltas2_filename, decode_timedelta=True)
 
 .. jupyter-execute::
 
     coder = xr.coders.CFDatetimeCoder(time_unit="s")
-    xr.open_dataset("test-timedeltas2.nc", decode_times=coder, decode_timedelta=True)
+    xr.open_dataset(timedeltas2_filename, decode_times=coder, decode_timedelta=True)
 
 To opt-out of timedelta decoding (see issue `Undesired decoding to timedelta64 <https://github.com/pydata/xarray/issues/1621>`_) pass ``False`` to ``decode_timedelta``:
 
 .. jupyter-execute::
 
-    xr.open_dataset("test-timedeltas2.nc", decode_timedelta=False)
+    xr.open_dataset(timedeltas2_filename, decode_timedelta=False)
 
 .. note::
     Note that in the future the default value of ``decode_timedelta`` will be
@@ -557,13 +601,4 @@ To opt-out of timedelta decoding (see issue `Undesired decoding to timedelta64 <
     :hide-code:
 
     # Cleanup
-    import os
-
-    for f in [
-        "test-datetimes1.nc",
-        "test-datetimes2.nc",
-        "test-timedeltas1.nc",
-        "test-timedeltas2.nc",
-    ]:
-        if os.path.exists(f):
-            os.remove(f)
+    tempdir.cleanup()

doc/internals/zarr-encoding-spec.rst

@@ -90,6 +90,18 @@ with zarr-python.
 
 **Example 1: Zarr V2 Format**
 
+.. jupyter-execute::
+
+    zarr_v2_filename = "example_v2.zarr"
+
+.. jupyter-execute::
+    :hide-code:
+
+    import tempfile
+    import os.path
+    tempdir = tempfile.TemporaryDirectory()
+    zarr_v2_filename = os.path.join(tempdir.name, zarr_v2_filename)
+
 .. jupyter-execute::
 
     import os
@@ -98,30 +110,33 @@ with zarr-python.
 
     # Load tutorial dataset and write as Zarr V2
     ds = xr.tutorial.load_dataset("rasm")
-    ds.to_zarr("rasm_v2.zarr", mode="w", consolidated=False, zarr_format=2)
+    ds.to_zarr(zarr_v2_filename, mode="w", consolidated=False, zarr_format=2)
 
     # Open with zarr-python and examine attributes
-    zgroup = zarr.open("rasm_v2.zarr")
+    zgroup = zarr.open(zarr_v2_filename)
     print("Zarr V2 - Tair attributes:")
     tair_attrs = dict(zgroup["Tair"].attrs)
     for key, value in tair_attrs.items():
         print(f"  '{key}': {repr(value)}")
 
+**Example 2: Zarr V3 Format**
+
 .. jupyter-execute::
-    :hide-code:
 
-    import shutil
-    shutil.rmtree("rasm_v2.zarr")
+    zarr_v3_filename = "example_v3.zarr"
 
-**Example 2: Zarr V3 Format**
+.. jupyter-execute::
+    :hide-code:
+
+    zarr_v3_filename = os.path.join(tempdir.name, zarr_v3_filename)
 
 .. jupyter-execute::
 
     # Write the same dataset as Zarr V3
-    ds.to_zarr("rasm_v3.zarr", mode="w", consolidated=False, zarr_format=3)
+    ds.to_zarr(zarr_v3_filename, mode="w", consolidated=False, zarr_format=3)
 
     # Open with zarr-python and examine attributes
-    zgroup = zarr.open("rasm_v3.zarr")
+    zgroup = zarr.open(zarr_v3_filename)
     print("Zarr V3 - Tair attributes:")
     tair_attrs = dict(zgroup["Tair"].attrs)
     for key, value in tair_attrs.items():
@@ -131,12 +146,6 @@ with zarr-python.
     tair_array = zgroup["Tair"]
     print(f"\nZarr V3 - dimension_names in metadata: {tair_array.metadata.dimension_names}")
 
-.. jupyter-execute::
-    :hide-code:
-
-    import shutil
-    shutil.rmtree("rasm_v3.zarr")
-
 
 Chunk Key Encoding
 ------------------
@@ -148,6 +157,16 @@ dimension separator in chunk keys.
 
 For example, to specify a custom separator for chunk keys:
 
+
+.. jupyter-execute::
+
+    example_filename = "example.zarr"
+
+.. jupyter-execute::
+    :hide-code:
+
+    example_filename = os.path.join(tempdir.name, example_filename)
+
 .. jupyter-execute::
 
     import xarray as xr
@@ -161,7 +180,7 @@ For example, to specify a custom separator for chunk keys:
     arr = np.ones((42, 100))
     ds = xr.DataArray(arr, name="var1").to_dataset()
     ds.to_zarr(
-        "example.zarr",
+        example_filename,
         zarr_format=2,
         mode="w",
         encoding={"var1": {"chunks": (42, 50), "chunk_key_encoding": enc}},
@@ -179,8 +198,6 @@ when working with tools that expect a particular chunk key format.
     chunk key encoding based on the store's format and configuration.
 
 .. jupyter-execute::
-    :hide-code:
-
-    import shutil
+   :hide-code:
 
-    shutil.rmtree("example.zarr")
+   tempdir.cleanup()