Repository: SciTools/iris (language: python)

Issue:
Lockfile updates
## 🚀 Pull Request

### Description
<!-- Provide a clear description about your awesome pull request -->
<!-- Tell us all about your new feature, improvement, or bug fix -->

* All lockfiles updated (from #4967).
* Changes to a test based on changes in Cartopy `v0.21.0`:
  * The specific change is Cartopy adopting a new default Transverse Mercator projection - changed in  SciTools/cartopy@fcb784d, which was planned since SciTools/cartopy@8860a81. Read those for more context.
  * Our test was written to alert us to any changes in behaviour, rather than to enforce a 'correct' answer. @pp-mo, @bjlittle and I have reviewed the changed behaviour and accepted it by changing the expected test result.
  * The test 'area' has been also moved West to better demonstrate the intended masking behaviour, as the original area was showing very few masks since the Cartopy change.
* NetCDF4 `!=1.6.1` pin to avoid segfaults.

TODO:

- [x] What's New
- [x] Minimum Cartopy pin (`>=0.21`)
- [x] Diagnose intermittent GHA worker crashes

---
[Consult Iris pull request check list]( https://scitools-iris.readthedocs.io/en/latest/developers_guide/contributing_pull_request_checklist.html)



Discussion / hints:
The intermittent HDF5 errors and GHA worker crashes appear associated with the same tests - those that read NetCDF files. I'm certain they are dependency related as I have simultaneously re-run another GHA run with our existing lockfiles ([run `3060581803`](https://github.com/SciTools/iris/actions/runs/3060581803)) and those all passed.

My guess is either the netCDF4 or NumPy changes.

I can't reproduce locally.
NetCDF4 confirmed, it seems. See also:

* Unidata/netcdf4-python#1192
* conda-forge/netcdf4-feedstock#141
* yt-project/yt#4128

Fix patch:
diff --git a/docs/src/whatsnew/3.3.rst b/docs/src/whatsnew/3.3.rst
index 47674b7272..49e222a494 100644
--- a/docs/src/whatsnew/3.3.rst
+++ b/docs/src/whatsnew/3.3.rst
@@ -52,6 +52,14 @@ v3.3.1 |build_date| [unreleased]
     #. `@pp-mo`_ ensured that :data:`iris.cube.Cube.cell_methods` must always be an iterable
        of :class:`iris.coords.CellMethod` objects (:pull:`4933`).
 
+    #. `@trexfeathers`_ advanced the Cartopy pin to ``>=0.21``, as Cartopy's
+       change to default Transverse Mercator projection affects an Iris test.
+       See `SciTools/cartopy@fcb784d`_ and `SciTools/cartopy@8860a81`_ for more
+       details. (:pull:`4992`)
+
+    #. `@trexfeathers`_ introduced the ``netcdf4!=1.6.1`` pin to avoid a
+       problem with segfaults. (:pull:`4992`)
+
 
 📢 Announcements
 ================
@@ -361,3 +369,5 @@ v3.3.1 |build_date| [unreleased]
 .. _PyData Sphinx Theme: https://pydata-sphinx-theme.readthedocs.io/en/stable/index.html
 .. _pytest: https://docs.pytest.org
 .. _setuptools-scm: https://github.com/pypa/setuptools_scm
+.. _SciTools/cartopy@fcb784d: https://github.com/SciTools/cartopy/commit/fcb784daa65d95ed9a74b02ca292801c02bc4108
+.. _SciTools/cartopy@8860a81: https://github.com/SciTools/cartopy/commit/8860a8186d4dc62478e74c83f3b2b3e8f791372e
diff --git a/lib/iris/analysis/cartography.py b/lib/iris/analysis/cartography.py
index 44129ff175..f38e48354d 100644
--- a/lib/iris/analysis/cartography.py
+++ b/lib/iris/analysis/cartography.py
@@ -1008,8 +1008,8 @@ def _transform_distance_vectors_tolerance_mask(
     u_one_t, v_zero_t = _transform_distance_vectors(ones, zeros, ds, dx2, dy2)
     u_zero_t, v_one_t = _transform_distance_vectors(zeros, ones, ds, dx2, dy2)
     # Squared magnitudes should be equal to one within acceptable tolerance.
-    # A value of atol=2e-3 is used, which corresponds to a change in magnitude
-    # of approximately 0.1%.
+    # A value of atol=2e-3 is used, which masks any magnitude changes >0.5%
+    #  (approx percentage - based on experimenting).
     sqmag_1_0 = u_one_t**2 + v_zero_t**2
     sqmag_0_1 = u_zero_t**2 + v_one_t**2
     mask = np.logical_not(
diff --git a/requirements/ci/nox.lock/py310-linux-64.lock b/requirements/ci/nox.lock/py310-linux-64.lock
index b29cac464f..ae15d4f1c2 100644
--- a/requirements/ci/nox.lock/py310-linux-64.lock
+++ b/requirements/ci/nox.lock/py310-linux-64.lock
@@ -1,9 +1,9 @@
 # Generated by conda-lock.
 # platform: linux-64
-# input_hash: e79be0c1a5de550c1c2e192396e0e5cfda2ba23a154e1c4531d893da416f8cee
+# input_hash: 661b2aefa2ce2746dbc82fa542204349290bb7a1180eea0d9424f51ef38f3899
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+https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2022.9.24-ha878542_0.tar.bz2#41e4e87062433e283696cf384f952ef6
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+https://conda.anaconda.org/conda-forge/linux-64/gettext-0.19.8.1-h27087fc_1009.tar.bz2#17f91dc8bb7a259b02be5bfb2cd2395f
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diff --git a/requirements/ci/nox.lock/py38-linux-64.lock b/requirements/ci/nox.lock/py38-linux-64.lock
index 1508ea0f55..a0c6a16c70 100644
--- a/requirements/ci/nox.lock/py38-linux-64.lock
+++ b/requirements/ci/nox.lock/py38-linux-64.lock
@@ -1,9 +1,9 @@
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+# input_hash: accba76141f130ac5ecec2b0497bb7643998124920fcdd4c6d635380d98104bb
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diff --git a/requirements/ci/nox.lock/py39-linux-64.lock b/requirements/ci/nox.lock/py39-linux-64.lock
index ef20dfd33b..4a5eecb4f2 100644
--- a/requirements/ci/nox.lock/py39-linux-64.lock
+++ b/requirements/ci/nox.lock/py39-linux-64.lock
@@ -1,9 +1,9 @@
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 # platform: linux-64
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+# input_hash: 74897fb3a84b5ce31dd22bb2c2de5b25aac891b5398acc3e5773b55f8807565b
 @EXPLICIT
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-https://conda.anaconda.org/conda-forge/linux-64/scipy-1.9.0-py39h8ba3f38_0.tar.bz2#b098a256777cb9e2605451f183c78768
+https://conda.anaconda.org/conda-forge/linux-64/scipy-1.9.1-py39h8ba3f38_0.tar.bz2#beed054d4979cd70690aea2b257a6d55
 https://conda.anaconda.org/conda-forge/noarch/setuptools-scm-7.0.5-pyhd8ed1ab_0.tar.bz2#743074b7a216807886f7e8f6d497cceb
 https://conda.anaconda.org/conda-forge/linux-64/shapely-1.8.4-py39h68ae834_0.tar.bz2#e871ee7de5bfa95095256e95e30be2a6
 https://conda.anaconda.org/conda-forge/linux-64/sip-6.6.2-py39h5a03fae_0.tar.bz2#e37704c6be07b8b14ffc1ce912802ce0
@@ -232,25 +233,25 @@ https://conda.anaconda.org/conda-forge/linux-64/ukkonen-1.0.1-py39hf939315_2.tar
 https://conda.anaconda.org/conda-forge/linux-64/cf-units-3.1.1-py39hd257fcd_0.tar.bz2#e0f1f1d3013be31359d3ac635b288469
 https://conda.anaconda.org/conda-forge/linux-64/esmf-8.2.0-mpi_mpich_h5a1934d_102.tar.bz2#bb8bdfa5e3e9e3f6ec861f05cd2ad441
 https://conda.anaconda.org/conda-forge/linux-64/gtk2-2.24.33-h90689f9_2.tar.bz2#957a0255ab58aaf394a91725d73ab422
-https://conda.anaconda.org/conda-forge/noarch/identify-2.5.3-pyhd8ed1ab_0.tar.bz2#682f05a8e4b047ce4bdcec9d69c12551
-https://conda.anaconda.org/conda-forge/noarch/imagehash-4.2.1-pyhd8ed1ab_0.tar.bz2#01cc8698b6e1a124dc4f585516c27643
+https://conda.anaconda.org/conda-forge/noarch/identify-2.5.5-pyhd8ed1ab_0.tar.bz2#985ef0c4ed7a26731c419818080ef6ce
+https://conda.anaconda.org/conda-forge/noarch/imagehash-4.3.0-pyhd8ed1ab_0.tar.bz2#aee564f0021a2a0ab12239fbdd28e209
 https://conda.anaconda.org/conda-forge/linux-64/librsvg-2.54.4-h7abd40a_0.tar.bz2#921e53675ed5ea352f022b79abab076a
-https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.5.3-py39h700656a_1.tar.bz2#43191b76750399349519f53feac83094
-https://conda.anaconda.org/conda-forge/linux-64/netcdf4-1.6.0-nompi_py39h71b8e10_101.tar.bz2#91e01aa93a2bcca96c9d64d2ce4f65f0
-https://conda.anaconda.org/conda-forge/noarch/pyopenssl-22.0.0-pyhd8ed1ab_0.tar.bz2#1d7e241dfaf5475e893d4b824bb71b44
+https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.6.0-py39hf9fd14e_0.tar.bz2#bdc55b4069ab9d2f938525c4cf90def0
+https://conda.anaconda.org/conda-forge/linux-64/netcdf4-1.6.0-nompi_py39h6ced12a_102.tar.bz2#b92600d0fef7f12f426935d87d6413e6
+https://conda.anaconda.org/conda-forge/noarch/pyopenssl-22.0.0-pyhd8ed1ab_1.tar.bz2#2e7e3630919d29c8216bfa2cd643d79e
 https://conda.anaconda.org/conda-forge/linux-64/pyqt5-sip-12.11.0-py39h5a03fae_0.tar.bz2#1fd9112714d50ee5be3dbf4fd23964dc
 https://conda.anaconda.org/conda-forge/noarch/pytest-forked-1.4.0-pyhd8ed1ab_0.tar.bz2#95286e05a617de9ebfe3246cecbfb72f
-https://conda.anaconda.org/conda-forge/linux-64/qt-main-5.15.4-ha5833f6_2.tar.bz2#dd3aa6715b9e9efaf842febf18ce4261
-https://conda.anaconda.org/conda-forge/linux-64/cartopy-0.20.3-py39hed214b2_1.tar.bz2#7264fa97520021d1a01f5be9741493c2
+https://conda.anaconda.org/conda-forge/linux-64/qt-main-5.15.6-hc525480_0.tar.bz2#abd0f27f5e84cd0d5ae14d22b08795d7
+https://conda.anaconda.org/conda-forge/linux-64/cartopy-0.21.0-py39hf5d525c_0.tar.bz2#b99ba7383d1c9dd18445dfff08439c48
 https://conda.anaconda.org/conda-forge/linux-64/esmpy-8.2.0-mpi_mpich_py39h8bb458d_101.tar.bz2#347f324dd99dfb0b1479a466213b55bf
-https://conda.anaconda.org/conda-forge/linux-64/graphviz-5.0.1-h5abf519_0.tar.bz2#03f22ca50fcff4bbee39da0943ab8475
+https://conda.anaconda.org/conda-forge/linux-64/graphviz-6.0.1-h5abf519_0.tar.bz2#123c55da3e9ea8664f73c70e13ef08c2
 https://conda.anaconda.org/conda-forge/noarch/nc-time-axis-1.4.1-pyhd8ed1ab_0.tar.bz2#281b58948bf60a2582de9e548bcc5369
 https://conda.anaconda.org/conda-forge/linux-64/pre-commit-2.20.0-py39hf3d152e_0.tar.bz2#314c8cb1538706f62ec36cf64370f2b2
 https://conda.anaconda.org/conda-forge/linux-64/pyqt-5.15.7-py39h18e9c17_0.tar.bz2#5ed8f83afff3b64fa91f7a6af8d7ff04
 https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-2.5.0-pyhd8ed1ab_0.tar.bz2#1fdd1f3baccf0deb647385c677a1a48e
 https://conda.anaconda.org/conda-forge/noarch/urllib3-1.26.11-pyhd8ed1ab_0.tar.bz2#0738978569b10669bdef41c671252dd1
-https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.5.3-py39hf3d152e_1.tar.bz2#bed9a319c4a3ac57376f5d95ba923d78
-https://conda.anaconda.org/conda-forge/noarch/requests-2.28.1-pyhd8ed1ab_0.tar.bz2#70d6e72856de9551f83ae0f2de689a7a
+https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.6.0-py39hf3d152e_0.tar.bz2#93f29e4d6f852de18384412b0e0d03b5
+https://conda.anaconda.org/conda-forge/noarch/requests-2.28.1-pyhd8ed1ab_1.tar.bz2#089382ee0e2dc2eae33a04cc3c2bddb0
 https://conda.anaconda.org/conda-forge/noarch/sphinx-4.5.0-pyh6c4a22f_0.tar.bz2#46b38d88c4270ff9ba78a89c83c66345
 https://conda.anaconda.org/conda-forge/noarch/pydata-sphinx-theme-0.8.1-pyhd8ed1ab_0.tar.bz2#7d8390ec71225ea9841b276552fdffba
 https://conda.anaconda.org/conda-forge/noarch/sphinx-copybutton-0.5.0-pyhd8ed1ab_0.tar.bz2#4c969cdd5191306c269490f7ff236d9c
diff --git a/requirements/ci/py310.yml b/requirements/ci/py310.yml
index 16b8afb67a..98426d3fff 100644
--- a/requirements/ci/py310.yml
+++ b/requirements/ci/py310.yml
@@ -11,12 +11,12 @@ dependencies:
   - setuptools-scm >=7
 
 # Core dependencies.
-  - cartopy >=0.20
+  - cartopy >=0.21
   - cf-units >=3.1
   - cftime >=1.5
   - dask-core >=2
   - matplotlib
-  - netcdf4
+  - netcdf4 !=1.6.1
   - numpy >=1.19
   - python-xxhash
   - pyproj
diff --git a/requirements/ci/py38.yml b/requirements/ci/py38.yml
index faecbd63ce..0c49fd1094 100644
--- a/requirements/ci/py38.yml
+++ b/requirements/ci/py38.yml
@@ -11,12 +11,12 @@ dependencies:
   - setuptools-scm >=7
 
 # Core dependencies.
-  - cartopy >=0.20
+  - cartopy >=0.21
   - cf-units >=3.1
   - cftime >=1.5
   - dask-core >=2
   - matplotlib
-  - netcdf4
+  - netcdf4 !=1.6.1
   - numpy >=1.19
   - python-xxhash
   - pyproj
diff --git a/requirements/ci/py39.yml b/requirements/ci/py39.yml
index f26804e92b..42d47c0d8d 100644
--- a/requirements/ci/py39.yml
+++ b/requirements/ci/py39.yml
@@ -11,12 +11,12 @@ dependencies:
   - setuptools-scm >=7
 
 # Core dependencies.
-  - cartopy >=0.20
+  - cartopy >=0.21
   - cf-units >=3.1
   - cftime >=1.5
   - dask-core >=2
   - matplotlib
-  - netcdf4
+  - netcdf4 !=1.6.1
   - numpy >=1.19
   - python-xxhash
   - pyproj
diff --git a/setup.cfg b/setup.cfg
index 672cfc24d2..ddbb245183 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -45,12 +45,12 @@ version = attr: iris.__version__
 [options]
 include_package_data = True
 install_requires =
-    cartopy>=0.20
+    cartopy>=0.21
     cf-units>=3.1
     cftime>=1.5.0
     dask[array]>=2
     matplotlib
-    netcdf4
+    netcdf4!=1.6.1
     numpy>=1.19
     scipy
     shapely!=1.8.3


Test patch:
diff --git a/lib/iris/tests/unit/analysis/cartography/test_rotate_winds.py b/lib/iris/tests/unit/analysis/cartography/test_rotate_winds.py
index 7bd8fdb597..7952b3bb46 100644
--- a/lib/iris/tests/unit/analysis/cartography/test_rotate_winds.py
+++ b/lib/iris/tests/unit/analysis/cartography/test_rotate_winds.py
@@ -16,6 +16,7 @@
 import cartopy.crs as ccrs
 import numpy as np
 import numpy.ma as ma
+import pytest
 
 from iris.analysis.cartography import rotate_winds, unrotate_pole
 import iris.coord_systems
@@ -410,7 +411,11 @@ def test_transposed(self):
 class TestMasking(tests.IrisTest):
     def test_rotated_to_osgb(self):
         # Rotated Pole data with large extent.
-        x = np.linspace(311.9, 391.1, 10)
+        # A 'correct' answer is not known for this test; it is therefore
+        #  written as a 'benchmark' style test - a change in behaviour will
+        #  cause a test failure, requiring developers to approve/reject the
+        #  new behaviour.
+        x = np.linspace(221.9, 301.1, 10)
         y = np.linspace(-23.6, 24.8, 8)
         u, v = uv_cubes(x, y)
         ut, vt = rotate_winds(u, v, iris.coord_systems.OSGB())
@@ -422,14 +427,14 @@ def test_rotated_to_osgb(self):
         # Snapshot of mask with fixed tolerance of atol=2e-3
         expected_mask = np.array(
             [
-                [1, 1, 1, 0, 0, 0, 0, 0, 0, 1],
-                [1, 1, 1, 0, 0, 0, 0, 0, 0, 1],
-                [1, 1, 1, 1, 0, 0, 0, 0, 1, 1],
-                [1, 1, 1, 1, 0, 0, 0, 0, 1, 1],
-                [1, 1, 1, 1, 0, 0, 0, 0, 1, 1],
-                [1, 1, 1, 1, 1, 0, 0, 1, 1, 1],
-                [1, 1, 1, 1, 1, 0, 0, 1, 1, 1],
-                [1, 1, 1, 1, 1, 0, 0, 1, 1, 1],
+                [0, 0, 0, 1, 1, 1, 0, 0, 0, 1],
+                [0, 0, 0, 0, 1, 1, 1, 0, 1, 1],
+                [0, 0, 0, 0, 0, 1, 1, 1, 1, 1],
+                [0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
+                [0, 0, 0, 1, 1, 1, 1, 1, 0, 0],
+                [0, 1, 1, 1, 1, 1, 1, 0, 0, 0],
+                [0, 1, 1, 1, 0, 1, 1, 1, 0, 0],
+                [0, 1, 0, 0, 0, 0, 1, 1, 1, 0],
             ],
             np.bool_,
         )
@@ -443,7 +448,7 @@ def test_rotated_to_osgb(self):
         # Calculate percentage error (note there are no zero magnitudes
         # so we can divide safely).
         anom = 100.0 * np.abs(res_mag - expected_mag) / expected_mag
-        self.assertTrue(anom[~ut.data.mask].max() < 0.1)
+        assert anom[~ut.data.mask].max() == pytest.approx(0.3227935)
 
     def test_rotated_to_unrotated(self):
         # Suffiently accurate so that no mask is introduced.