Lockfile updates (#4968)

* Updated environment lockfiles

* Adjustments for Cartopy v0.21.0 (SciTools/cartopy@fcb784d).

* Cartopy >=0.21 pin.

* What's New entry.

* WIP try netCDF4 pin.

* Try pip installing netcdf4==1.6.1 as requested by @ocefpaf.

* Revert "Try pip installing netcdf4==1.6.1 as requested by @ocefpaf."

This reverts commit ce9f890.

* netcdf4!=1.6.1

* Align Conda YAML formatting.

Co-authored-by: Lockfile bot <[email protected]>

docs/src/whatsnew/latest.rst

@@ -50,7 +50,7 @@ This document explains the changes made to Iris for this release
 #. `@bjlittle`_ and `@lbdreyer`_ (reviewer) fixed the building of the CF
    Standard Names module ``iris.std_names`` for the ``setup.py`` commands
    ``develop`` and ``std_names``. (:issue:`4951`, :pull:`4952`)
-   
+
 #. `@lbdreyer`_ and `@pp-mo`_ (reviewer) fixed the cube print out such that
    scalar ancillary variables are displayed in a dedicated section rather than
    being added to the vector ancillary variables section. Further, ancillary
@@ -83,6 +83,13 @@ This document explains the changes made to Iris for this release
 
 #. `@rcomer`_ introduced the ``dask >=2.26`` minimum pin, so that Iris can benefit
    from Dask's support for `NEP13`_ and `NEP18`_. (:pull:`4905`)
+#. `@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:`4968`)
+#. `@trexfeathers`_ introduced the ``netcdf4!=1.6.1`` pin to avoid a problem
+   with segfaults. (:pull:`4968`)
 
 
 📚 Documentation
@@ -117,4 +124,6 @@ This document explains the changes made to Iris for this release
 
 .. _NEP13: https://numpy.org/neps/nep-0013-ufunc-overrides.html
 .. _NEP18: https://numpy.org/neps/nep-0018-array-function-protocol.html
-.. _pypa/setuptools#1684: https://github.com/pypa/setuptools/issues/1684
+.. _pypa/setuptools#1684: https://github.com/pypa/setuptools/issues/1684
+.. _SciTools/cartopy@fcb784d: https://github.com/SciTools/cartopy/commit/fcb784daa65d95ed9a74b02ca292801c02bc4108
+.. _SciTools/cartopy@8860a81: https://github.com/SciTools/cartopy/commit/8860a8186d4dc62478e74c83f3b2b3e8f791372e

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(

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.