Unpin dask 2

docs/src/whatsnew/latest.rst

@@ -95,6 +95,11 @@ This document explains the changes made to Iris for this release
 #. `@stephenworsley`_ dropped support for ``py310`` and adopted support for ``py313``
    as per the `SPEC 0`_ schedule. (:pull:`6195`)
 
+#. `@stephenworsley`_ and `@valeriupredoi`_ removed the pin from dask since newer
+   versions of dask fix the bug casuing the pin. Introduced a minimum pin (2025.1.0)
+   to avoid this bug. (:pull:`6342`)
+
+
 📚 Documentation
 ================
 
@@ -137,6 +142,7 @@ This document explains the changes made to Iris for this release
 .. _@fnattino: https://github.com/fnattino
 .. _@jrackham-mo: https://github.com/jrackham-mo
 .. _@stefsmeets: https://github.com/stefsmeets
+.. _@valeriupredoi: https://github.com/valeriupredoi
 
 .. comment
     Whatsnew resources in alphabetical order:

lib/iris/common/resolve.py

@@ -2592,9 +2592,9 @@ def shape(self):
                     source                      'Data from Met Office Unified Model 6.05'
             >>> Resolve().shape is None
             True
-            >>> Resolve(cube1, cube2).shape  # doctest: +SKIP
+            >>> Resolve(cube1, cube2).shape
             (240, 37, 49)
-            >>> Resolve(cube2, cube1).shape  # doctest: +SKIP
+            >>> Resolve(cube2, cube1).shape
             (240, 37, 49)
 
         """  # noqa: D214, D406, D407, D410, D411

lib/iris/tests/__init__.py

@@ -86,6 +86,7 @@
 
 #: Basepath for test results.
 _RESULT_PATH = os.path.join(os.path.dirname(__file__), "results")
+MIN_PICKLE_PROTOCOL = 4
 
 if "--data-files-used" in sys.argv:
     sys.argv.remove("--data-files-used")

lib/iris/tests/integration/test_pickle.py

@@ -7,52 +7,47 @@
 # Import iris.tests first so that some things can be initialised before
 # importing anything else.
 import iris.tests as tests  # isort:skip
-
 import pickle
 
-import iris
-
+import pytest
 
-class Common:
-    def pickle_cube(self, protocol):
-        # Ensure that data proxies are pickleable.
-        cube = iris.load(self.path)[0]
-        with self.temp_filename(".pkl") as filename:
-            with open(filename, "wb") as f:
-                pickle.dump(cube, f, protocol)
-            with open(filename, "rb") as f:
-                ncube = pickle.load(f)
-        self.assertEqual(ncube, cube)
+import iris
+from iris.tests import MIN_PICKLE_PROTOCOL
 
-    def test_protocol_0(self):
-        self.pickle_cube(0)
+TESTED_PROTOCOLS = list(range(MIN_PICKLE_PROTOCOL, pickle.HIGHEST_PROTOCOL + 1))
 
-    def test_protocol_1(self):
-        self.pickle_cube(1)
 
-    def test_protocol_2(self):
-        self.pickle_cube(2)
+def pickle_cube(path, protocol, filename):
+    # Ensure that data proxies are pickleable.
+    cube = iris.load(path)[0]
+    with open(filename, "wb") as f:
+        pickle.dump(cube, f, protocol)
+    with open(filename, "rb") as f:
+        ncube = pickle.load(f)
+    assert ncube == cube
 
 
+@pytest.mark.parametrize("protocol", TESTED_PROTOCOLS)
 @tests.skip_data
-class test_netcdf(Common, tests.IrisTest):
-    def setUp(self):
-        self.path = tests.get_data_path(
-            ("NetCDF", "global", "xyt", "SMALL_hires_wind_u_for_ipcc4.nc")
-        )
+def test_netcdf(protocol, tmp_path):
+    path = tests.get_data_path(
+        ("NetCDF", "global", "xyt", "SMALL_hires_wind_u_for_ipcc4.nc")
+    )
+    tmp_file = tmp_path / "netcdf.pkl"
+    pickle_cube(path, protocol, tmp_file)
 
 
+@pytest.mark.parametrize("protocol", TESTED_PROTOCOLS)
 @tests.skip_data
-class test_pp(Common, tests.IrisTest):
-    def setUp(self):
-        self.path = tests.get_data_path(("PP", "aPPglob1", "global.pp"))
+def test_pp(protocol, tmp_path):
+    path = tests.get_data_path(("PP", "aPPglob1", "global.pp"))
+    tmp_file = tmp_path / "pp.pkl"
+    pickle_cube(path, protocol, tmp_file)
 
 
+@pytest.mark.parametrize("protocol", TESTED_PROTOCOLS)
 @tests.skip_data
-class test_ff(Common, tests.IrisTest):
-    def setUp(self):
-        self.path = tests.get_data_path(("FF", "n48_multi_field"))
-
-
-if __name__ == "__main__":
-    tests.main()
+def test_ff(protocol, tmp_path):
+    path = tests.get_data_path(("FF", "n48_multi_field"))
+    tmp_file = tmp_path / "ff.pkl"
+    pickle_cube(path, protocol, tmp_file)

lib/iris/tests/test_pickling.py

@@ -7,7 +7,6 @@
 # Import iris tests first so that some things can be initialised
 # before importing anything else.
 import iris.tests as tests  # isort:skip
-
 import io
 import pickle
 
@@ -16,12 +15,13 @@
 
 import iris
 from iris._lazy_data import as_concrete_data, as_lazy_data
+from iris.tests import MIN_PICKLE_PROTOCOL
 
 
 class TestPickle(tests.IrisTest):
     def pickle_then_unpickle(self, obj):
         """Returns a generator of ("pickle protocol number", object) tuples."""
-        for protocol in range(1 + pickle.HIGHEST_PROTOCOL):
+        for protocol in range(MIN_PICKLE_PROTOCOL, pickle.HIGHEST_PROTOCOL + 1):
             bio = io.BytesIO()
             pickle.dump(obj, bio, protocol)
 

lib/iris/tests/unit/analysis/test_PERCENTILE.py

@@ -162,7 +162,7 @@ def test_default_kwargs_passed(self, mocked_mquantiles):
         for key in ["alphap", "betap"]:
             self.assertEqual(mocked_mquantiles.call_args.kwargs[key], 1)
 
-    @mock.patch("scipy.stats.mstats.mquantiles")
+    @mock.patch("scipy.stats.mstats.mquantiles", return_value=np.array([2, 4]))
     def test_chosen_kwargs_passed(self, mocked_mquantiles):
         data = np.arange(5)
         percent = [42, 75]
@@ -356,7 +356,7 @@ def test_numpy_percentile_called(self, mocked_percentile):
         # Check we have left "method" keyword to numpy's default.
         self.assertNotIn("method", mocked_percentile.call_args.kwargs)
 
-    @mock.patch("numpy.percentile")
+    @mock.patch("numpy.percentile", return_value=np.array([2, 4]))
     def test_chosen_method_kwarg_passed(self, mocked_percentile):
         data = da.arange(5)
         percent = [42, 75]

lib/iris/tests/unit/fileformats/pp/test__interpret_field.py

@@ -117,9 +117,9 @@ def test_landsea_unpacking_uses_dask(self):
         # Get the 'main' calculation entry.
         mask_toplev_item = lazy_mask_array.dask[mask_toplev_key]
         # This should be a task (a simple fetch).
-        self.assertTrue(callable(mask_toplev_item[0]))
+        self.assertTrue(callable(mask_toplev_item))
         # Get the key (name) of the array that it fetches.
-        mask_data_name = mask_toplev_item[1]
+        mask_data_name = mask_toplev_item.args[0].key
 
         # Check that the item this refers to is a PPDataProxy.
         self.assertIsInstance(lazy_mask_array.dask[mask_data_name], pp.PPDataProxy)

requirements/locks/py311-linux-64.lock

@@ -1,6 +1,6 @@
 # Generated by conda-lock.
 # platform: linux-64
-# input_hash: 484c12b1591315f270b3e15b42ed2cba4c973b701897ae7eb79b8b9442d99a96
+# input_hash: 0e2b46335d407c1dacba3b0965f0164093e232b456197e26651a7fd261b99f5b
 @EXPLICIT
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@@ -268,9 +268,9 @@ https://conda.anaconda.org/conda-forge/linux-64/yarl-1.18.3-py311h2dc5d0c_1.cond
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 https://conda.anaconda.org/conda-forge/noarch/beautifulsoup4-4.13.3-pyha770c72_0.conda#373374a3ed20141090504031dc7b693e
+https://conda.anaconda.org/conda-forge/noarch/dask-core-2025.2.0-pyhd8ed1ab_0.conda#3bc22d25e3ee83d709804a2040b4463c
 https://conda.anaconda.org/conda-forge/linux-64/gnutls-3.8.9-h5746830_0.conda#0e776b108cd87ee80618acc5ee64c07f
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-https://conda.anaconda.org/conda-forge/noarch/importlib_metadata-8.6.1-hd8ed1ab_0.conda#7f46575a91b1307441abc235d01cab66
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@@ -291,7 +291,6 @@ https://conda.anaconda.org/conda-forge/linux-64/at-spi2-core-2.40.3-h0630a04_0.t
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-https://conda.anaconda.org/conda-forge/noarch/dask-core-2024.8.2-pyhd8ed1ab_0.conda#8e7524a2fb561506260db789806c7ee9
 https://conda.anaconda.org/conda-forge/noarch/identify-2.6.8-pyhd8ed1ab_0.conda#153a6ad50ad9db7bb4e042ee52a56f87
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@@ -303,6 +302,7 @@ https://conda.anaconda.org/conda-forge/linux-64/pandas-2.2.3-py311h7db5c69_1.con
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+https://conda.anaconda.org/conda-forge/linux-64/python-stratify-0.3.0-py311h9f3472d_3.conda#a7c4169b1c920361597ddacb461350fd
 https://conda.anaconda.org/conda-forge/linux-64/pywavelets-1.8.0-py311h9f3472d_0.conda#17334e5c12abdf2db6b25bd4187cd3e4
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@@ -311,7 +311,7 @@ https://conda.anaconda.org/conda-forge/noarch/urllib3-2.3.0-pyhd8ed1ab_0.conda#3
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-https://conda.anaconda.org/conda-forge/noarch/distributed-2024.8.2-pyhd8ed1ab_0.conda#44d22b5d98a219a4c35cafe9bf3b9ce2
+https://conda.anaconda.org/conda-forge/noarch/distributed-2025.2.0-pyhd8ed1ab_0.conda#54562a2b30c8f357097e2be75295601e
 https://conda.anaconda.org/conda-forge/linux-64/elfutils-0.192-h7f4e02f_1.conda#369ce48a589a2aac91906c9ed89dd6e8
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@@ -331,7 +331,6 @@ https://conda.anaconda.org/conda-forge/linux-64/librsvg-2.58.4-h49af25d_2.conda#
 https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.10.0-py311h2b939e6_0.conda#79239585ea50c427415ef629534bb3aa
 https://conda.anaconda.org/conda-forge/linux-64/netcdf4-1.7.2-nompi_py311h7c29e4f_101.conda#d966f11d28c699da7e9de2aa2f323a4f
 https://conda.anaconda.org/conda-forge/noarch/pre-commit-4.1.0-pyha770c72_0.conda#5353f5eb201a9415b12385e35ed1148d
-https://conda.anaconda.org/conda-forge/linux-64/python-stratify-0.3.0-py311h9f3472d_3.conda#a7c4169b1c920361597ddacb461350fd
 https://conda.anaconda.org/conda-forge/noarch/requests-2.32.3-pyhd8ed1ab_1.conda#a9b9368f3701a417eac9edbcae7cb737
 https://conda.anaconda.org/conda-forge/linux-64/sdl3-3.2.4-h3083f51_0.conda#c0d92f268209e0a0fd27954a5667c11d
 https://conda.anaconda.org/conda-forge/noarch/adwaita-icon-theme-47.0-unix_0.conda#49436a5c604f99058473d84580f0e341
@@ -360,4 +359,3 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-htmlhelp-2.1.0-pyhd8
 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-qthelp-2.0.0-pyhd8ed1ab_1.conda#00534ebcc0375929b45c3039b5ba7636
 https://conda.anaconda.org/conda-forge/noarch/sphinx-8.1.3-pyhd8ed1ab_1.conda#1a3281a0dc355c02b5506d87db2d78ac
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-

requirements/locks/py312-linux-64.lock

@@ -1,6 +1,6 @@
 # Generated by conda-lock.
 # platform: linux-64
-# input_hash: 99c5c493d72abab1088c22d89abdd9b5e70bb415845b13643a962d169660fe74
+# input_hash: bafcb30412f96e27e0bcbc91571bf16dce0b108578822ebc707b3d82114d2320
 @EXPLICIT
 https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81
 https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2025.1.31-hbcca054_0.conda#19f3a56f68d2fd06c516076bff482c52
@@ -259,8 +259,8 @@ https://conda.anaconda.org/conda-forge/linux-64/yarl-1.18.3-py312h178313f_1.cond
 https://conda.anaconda.org/conda-forge/linux-64/aiohttp-3.11.13-py312h178313f_0.conda#0ea623ee1f29a7e1d703bc3b0ef82306
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 https://conda.anaconda.org/conda-forge/noarch/beautifulsoup4-4.13.3-pyha770c72_0.conda#373374a3ed20141090504031dc7b693e
+https://conda.anaconda.org/conda-forge/noarch/dask-core-2025.2.0-pyhd8ed1ab_0.conda#3bc22d25e3ee83d709804a2040b4463c
 https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-10.3.0-h76408a6_0.conda#0a06f278e5d9242057673b1358a75e8f
-https://conda.anaconda.org/conda-forge/noarch/importlib_metadata-8.6.1-hd8ed1ab_0.conda#7f46575a91b1307441abc235d01cab66
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@@ -281,7 +281,6 @@ https://conda.anaconda.org/conda-forge/linux-64/at-spi2-core-2.40.3-h0630a04_0.t
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-https://conda.anaconda.org/conda-forge/noarch/dask-core-2024.8.2-pyhd8ed1ab_0.conda#8e7524a2fb561506260db789806c7ee9
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@@ -292,6 +291,7 @@ https://conda.anaconda.org/conda-forge/linux-64/pandas-2.2.3-py312hf9745cd_1.con
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+https://conda.anaconda.org/conda-forge/linux-64/python-stratify-0.3.0-py312hc0a28a1_3.conda#81bbcb20ea4a53b05a8cf51f31496038
 https://conda.anaconda.org/conda-forge/linux-64/pywavelets-1.8.0-py312hc0a28a1_0.conda#3f62987017ad18e9e7dadce9899de9ef
 https://conda.anaconda.org/conda-forge/linux-64/scipy-1.15.2-py312ha707e6e_0.conda#00b999c5f9d01fb633db819d79186bd4
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@@ -300,7 +300,7 @@ https://conda.anaconda.org/conda-forge/noarch/urllib3-2.3.0-pyhd8ed1ab_0.conda#3
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-https://conda.anaconda.org/conda-forge/noarch/distributed-2024.8.2-pyhd8ed1ab_0.conda#44d22b5d98a219a4c35cafe9bf3b9ce2
+https://conda.anaconda.org/conda-forge/noarch/distributed-2025.2.0-pyhd8ed1ab_0.conda#54562a2b30c8f357097e2be75295601e
 https://conda.anaconda.org/conda-forge/linux-64/esmf-8.7.0-nompi_h6063b07_1.conda#15e28a0e5e651ba11495c87608652316
 https://conda.anaconda.org/conda-forge/noarch/imagehash-4.3.2-pyhd8ed1ab_0.conda#efbc812363856906a80344b496389d2e
 https://conda.anaconda.org/conda-forge/linux-64/libopenvino-auto-batch-plugin-2025.0.0-h4d9b6c2_0.conda#62d563673a912579d277d27f0067b689
@@ -319,7 +319,6 @@ https://conda.anaconda.org/conda-forge/linux-64/librsvg-2.58.4-h49af25d_2.conda#
 https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.10.0-py312hd3ec401_0.conda#c27a17a8c54c0d35cf83bbc0de8f7f77
 https://conda.anaconda.org/conda-forge/linux-64/netcdf4-1.7.2-nompi_py312ha728dd9_101.conda#7e41ca6012a6bf609539aec0dfee93f7
 https://conda.anaconda.org/conda-forge/noarch/pre-commit-4.1.0-pyha770c72_0.conda#5353f5eb201a9415b12385e35ed1148d
-https://conda.anaconda.org/conda-forge/linux-64/python-stratify-0.3.0-py312hc0a28a1_3.conda#81bbcb20ea4a53b05a8cf51f31496038
 https://conda.anaconda.org/conda-forge/noarch/requests-2.32.3-pyhd8ed1ab_1.conda#a9b9368f3701a417eac9edbcae7cb737
 https://conda.anaconda.org/conda-forge/linux-64/sdl3-3.2.4-h3083f51_0.conda#c0d92f268209e0a0fd27954a5667c11d
 https://conda.anaconda.org/conda-forge/linux-64/vtk-base-9.3.1-egl_py312hc001bbe_13.conda#559a8d091b4e8806520f7f2f797c66de
@@ -347,4 +346,3 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-htmlhelp-2.1.0-pyhd8
 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-qthelp-2.0.0-pyhd8ed1ab_1.conda#00534ebcc0375929b45c3039b5ba7636
 https://conda.anaconda.org/conda-forge/noarch/sphinx-8.1.3-pyhd8ed1ab_1.conda#1a3281a0dc355c02b5506d87db2d78ac
 https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-serializinghtml-1.1.10-pyhd8ed1ab_1.conda#3bc61f7161d28137797e038263c04c54
-