Update the mesh-data-model feature branch wrt main

.cirrus.yml

@@ -38,7 +38,7 @@ env:
   # Conda packages to be installed.
   CONDA_CACHE_PACKAGES: "nox pip"
   # Git commit hash for iris test data.
-  IRIS_TEST_DATA_VERSION: "2.0.0"
+  IRIS_TEST_DATA_VERSION: "2.2"
   # Base directory for the iris-test-data.
   IRIS_TEST_DATA_DIR: ${HOME}/iris-test-data
 
@@ -193,4 +193,4 @@ linkcheck_task:
     - mkdir -p ${MPL_RC_DIR}
     - echo "backend : agg" > ${MPL_RC_FILE}
     - echo "image.cmap : viridis" >> ${MPL_RC_FILE}
-    - nox --session linkcheck -- --verbose
+    - nox --session linkcheck -- --verbose

MANIFEST.in

@@ -4,6 +4,7 @@ include CHANGES COPYING COPYING.LESSER
 # Files from setup.py package_data that are not automatically added to source distributions
 recursive-include lib/iris/tests/results *.cml *.cdl *.txt *.xml *.json
 recursive-include lib/iris/etc *
+include lib/iris/tests/stock/file_headers/*
 
 recursive-include requirements *
 

docs/src/sphinxext/generate_package_rst.py

@@ -13,7 +13,7 @@
 # list of tuples for modules to exclude.  Useful if the documentation throws
 # warnings, especially for experimental modules.
 exclude_modules = [
-    ("experimental/raster", "iris.experimental.raster")  # gdal conflicts
+    ("experimental/raster", "iris.experimental.raster"),  # gdal conflicts
 ]
 
 

docs/src/userguide/cube_statistics.rst

@@ -23,9 +23,9 @@ Collapsing Entire Data Dimensions
 
 In the :doc:`subsetting_a_cube` section we saw how to extract a subset of a
 cube in order to reduce either its dimensionality or its resolution.
-Instead of simply extracting a sub-region of the data, 
-we can produce statistical functions of the data values 
-across a particular dimension, 
+Instead of simply extracting a sub-region of the data,
+we can produce statistical functions of the data values
+across a particular dimension,
 such as a 'mean over time' or 'minimum over latitude'.
 
 .. _cube-statistics_forecast_printout:
@@ -57,9 +57,9 @@ For instance, suppose we have a cube:
               um_version: 7.3
 
 
-In this case we have a 4 dimensional cube; 
-to mean the vertical (z) dimension down to a single valued extent 
-we can pass the coordinate name and the aggregation definition to the 
+In this case we have a 4 dimensional cube;
+to mean the vertical (z) dimension down to a single valued extent
+we can pass the coordinate name and the aggregation definition to the
 :meth:`Cube.collapsed() <iris.cube.Cube.collapsed>` method:
 
     >>> import iris.analysis
@@ -88,8 +88,8 @@ we can pass the coordinate name and the aggregation definition to the
               mean: model_level_number
 
 
-Similarly other analysis operators such as ``MAX``, ``MIN`` and ``STD_DEV`` 
-can be used instead of ``MEAN``, see :mod:`iris.analysis` for a full list 
+Similarly other analysis operators such as ``MAX``, ``MIN`` and ``STD_DEV``
+can be used instead of ``MEAN``, see :mod:`iris.analysis` for a full list
 of currently supported operators.
 
 For an example of using this functionality, the
@@ -103,14 +103,14 @@ in the gallery takes a zonal mean of an ``XYT`` cube by using the
 Area Averaging
 ^^^^^^^^^^^^^^
 
-Some operators support additional keywords to the ``cube.collapsed`` method. 
-For example, :func:`iris.analysis.MEAN <iris.analysis.MEAN>` supports 
-a weights keyword which can be combined with 
+Some operators support additional keywords to the ``cube.collapsed`` method.
+For example, :func:`iris.analysis.MEAN <iris.analysis.MEAN>` supports
+a weights keyword which can be combined with
 :func:`iris.analysis.cartography.area_weights` to calculate an area average.
 
-Let's use the same data as was loaded in the previous example. 
-Since ``grid_latitude`` and ``grid_longitude`` were both point coordinates 
-we must guess bound positions for them 
+Let's use the same data as was loaded in the previous example.
+Since ``grid_latitude`` and ``grid_longitude`` were both point coordinates
+we must guess bound positions for them
 in order to calculate the area of the grid boxes::
 
     import iris.analysis.cartography
@@ -155,24 +155,24 @@ including an example on taking a :ref:`global area-weighted mean
 Partially Reducing Data Dimensions
 ----------------------------------
 
-Instead of completely collapsing a dimension, other methods can be applied 
-to reduce or filter the number of data points of a particular dimension. 
+Instead of completely collapsing a dimension, other methods can be applied
+to reduce or filter the number of data points of a particular dimension.
 
 
 Aggregation of Grouped Data
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-The :meth:`Cube.aggregated_by <iris.cube.Cube.aggregated_by>` operation 
-combines data for all points with the same value of a given coordinate. 
-To do this, you need a coordinate whose points take on only a limited set 
-of different values -- the *number* of these then determines the size of the 
+The :meth:`Cube.aggregated_by <iris.cube.Cube.aggregated_by>` operation
+combines data for all points with the same value of a given coordinate.
+To do this, you need a coordinate whose points take on only a limited set
+of different values -- the *number* of these then determines the size of the
 reduced dimension.
-The :mod:`iris.coord_categorisation` module can be used to make such 
-'categorical' coordinates out of ordinary ones: The most common use is 
-to aggregate data over regular *time intervals*, 
+The :mod:`iris.coord_categorisation` module can be used to make such
+'categorical' coordinates out of ordinary ones: The most common use is
+to aggregate data over regular *time intervals*,
 such as by calendar month or day of the week.
 
-For example, let's create two new coordinates on the cube 
+For example, let's create two new coordinates on the cube
 to represent the climatological seasons and the season year respectively::
 
     import iris
@@ -188,8 +188,8 @@ to represent the climatological seasons and the season year respectively::
 
 .. note::
 
-    The 'season year' is not the same as year number, because (e.g.) the months 
-    Dec11, Jan12 + Feb12 all belong to 'DJF-12'.  
+    The 'season year' is not the same as year number, because (e.g.) the months
+    Dec11, Jan12 + Feb12 all belong to 'DJF-12'.
     See :meth:`iris.coord_categorisation.add_season_year`.
 
 
@@ -206,10 +206,10 @@ to represent the climatological seasons and the season year respectively::
     iris.coord_categorisation.add_season_year(cube, 'time', name='season_year')
 
     annual_seasonal_mean = cube.aggregated_by(
-         ['clim_season', 'season_year'], 
+         ['clim_season', 'season_year'],
          iris.analysis.MEAN)
 
-    
+
 Printing this cube now shows that two extra coordinates exist on the cube:
 
 .. doctest:: aggregation
@@ -238,20 +238,20 @@ These two coordinates can now be used to aggregate by season and climate-year:
 .. doctest:: aggregation
 
     >>> annual_seasonal_mean = cube.aggregated_by(
-    ...     ['clim_season', 'season_year'], 
+    ...     ['clim_season', 'season_year'],
     ...     iris.analysis.MEAN)
     >>> print(repr(annual_seasonal_mean))
     <iris 'Cube' of surface_temperature / (K) (time: 19; latitude: 18; longitude: 432)>
-    
-The primary change in the cube is that the cube's data has been 
-reduced in the 'time' dimension by aggregation (taking means, in this case). 
-This has collected together all data points with the same values of season and 
+
+The primary change in the cube is that the cube's data has been
+reduced in the 'time' dimension by aggregation (taking means, in this case).
+This has collected together all data points with the same values of season and
 season-year.
 The results are now indexed by the 19 different possible values of season and
 season-year in a new, reduced 'time' dimension.
 
-We can see this by printing the first 10 values of season+year 
-from the original cube:  These points are individual months, 
+We can see this by printing the first 10 values of season+year
+from the original cube:  These points are individual months,
 so adjacent ones are often in the same season:
 
 .. doctest:: aggregation
@@ -271,7 +271,7 @@ so adjacent ones are often in the same season:
     djf 2007
     djf 2007
 
-Compare this with the first 10 values of the new cube's coordinates:  
+Compare this with the first 10 values of the new cube's coordinates:
 All the points now have distinct season+year values:
 
 .. doctest:: aggregation
@@ -294,7 +294,7 @@ All the points now have distinct season+year values:
 
 Because the original data started in April 2006 we have some incomplete seasons
 (e.g. there were only two months worth of data for 'mam-2006').
-In this case we can fix this by removing all of the resultant 'times' which 
+In this case we can fix this by removing all of the resultant 'times' which
 do not cover a three month period (note: judged here as > 3*28 days):
 
 .. doctest:: aggregation
@@ -306,7 +306,7 @@ do not cover a three month period (note: judged here as > 3*28 days):
     >>> full_season_means
     <iris 'Cube' of surface_temperature / (K) (time: 17; latitude: 18; longitude: 432)>
 
-The final result now represents the seasonal mean temperature for 17 seasons 
+The final result now represents the seasonal mean temperature for 17 seasons
 from jja-2006 to jja-2010:
 
 .. doctest:: aggregation