Change FUTURE flag netcdf_promote behaviour

docs/iris/example_code/General/SOI_filtering.py

@@ -52,10 +52,6 @@ def low_pass_weights(window, cutoff):
 
 
 def main():
-    # Enable a future option, to ensure that the netcdf load works the same way
-    # as in future Iris versions.
-    iris.FUTURE.netcdf_promote = True
-
     # Load the monthly-valued Southern Oscillation Index (SOI) time-series.
     fname = iris.sample_data_path('SOI_Darwin.nc')
     soi = iris.load_cube(fname)

docs/iris/example_code/General/anomaly_log_colouring.py

@@ -36,10 +36,6 @@
 
 
 def main():
-    # Enable a future option, to ensure that the netcdf load works the same way
-    # as in future Iris versions.
-    iris.FUTURE.netcdf_promote = True
-
     # Load a sample air temperatures sequence.
     file_path = iris.sample_data_path('E1_north_america.nc')
     temperatures = iris.load_cube(file_path)

docs/iris/example_code/General/cross_section.py

@@ -15,10 +15,6 @@
 
 
 def main():
-    # Enable a future option, to ensure that the netcdf load works the same way
-    # as in future Iris versions.
-    iris.FUTURE.netcdf_promote = True
-
     # Load some test data.
     fname = iris.sample_data_path('hybrid_height.nc')
     theta = iris.load_cube(fname, 'air_potential_temperature')

docs/iris/example_code/General/custom_aggregation.py

@@ -65,10 +65,6 @@ def count_spells(data, threshold, axis, spell_length):
 
 
 def main():
-    # Enable a future option, to ensure that the netcdf load works the same way
-    # as in future Iris versions.
-    iris.FUTURE.netcdf_promote = True
-
     # Load the whole time-sequence as a single cube.
     file_path = iris.sample_data_path('E1_north_america.nc')
     cube = iris.load_cube(file_path)

docs/iris/example_code/General/inset_plot.py

@@ -17,9 +17,7 @@
 
 
 def main():
-    # Load the data
-    with iris.FUTURE.context(netcdf_promote=True):
-        cube1 = iris.load_cube(iris.sample_data_path('ostia_monthly.nc'))
+    cube1 = iris.load_cube(iris.sample_data_path('ostia_monthly.nc'))
     # Slice into cube to retrieve data for the inset map showing the
     # data region
     region = cube1[-1, :, :]

docs/iris/example_code/General/orca_projection.py

@@ -22,10 +22,6 @@
 
 
 def main():
-    # Enable a future option, to ensure that the netcdf load works the same way
-    # as in future Iris versions.
-    iris.FUTURE.netcdf_promote = True
-
     # Load data
     filepath = iris.sample_data_path('orca2_votemper.nc')
     cube = iris.load_cube(filepath)

docs/iris/example_code/General/polynomial_fit.py

@@ -16,10 +16,6 @@
 
 
 def main():
-    # Enable a future option, to ensure that the netcdf load works the same way
-    # as in future Iris versions.
-    iris.FUTURE.netcdf_promote = True
-
     # Load some test data.
     fname = iris.sample_data_path('A1B_north_america.nc')
     cube = iris.load_cube(fname)

docs/iris/example_code/General/projections_and_annotations.py

@@ -106,10 +106,6 @@ def make_plot(projection_name, projection_crs):
 
 
 def main():
-    # Enable a future option, to ensure that the netcdf load works the same way
-    # as in future Iris versions.
-    iris.FUTURE.netcdf_promote = True
-
     # Demonstrate with two different display projections.
     make_plot('Equidistant Cylindrical', ccrs.PlateCarree())
     make_plot('North Polar Stereographic', ccrs.NorthPolarStereo())

docs/iris/example_code/General/rotated_pole_mapping.py

@@ -21,10 +21,6 @@
 
 
 def main():
-    # Enable a future option, to ensure that the netcdf load works the same way
-    # as in future Iris versions.
-    iris.FUTURE.netcdf_promote = True
-
     # Load some test data.
     fname = iris.sample_data_path('rotated_pole.nc')
     air_pressure = iris.load_cube(fname)

docs/iris/example_code/Meteorology/COP_1d_plot.py

@@ -36,10 +36,6 @@
 
 
 def main():
-    # Enable a future option, to ensure that the netcdf load works the same way
-    # as in future Iris versions.
-    iris.FUTURE.netcdf_promote = True
-
     # Load data into three Cubes, one for each set of NetCDF files.
     e1 = iris.load_cube(iris.sample_data_path('E1_north_america.nc'))
 

docs/iris/example_code/Meteorology/TEC.py

@@ -20,10 +20,6 @@
 
 
 def main():
-    # Enable a future option, to ensure that the netcdf load works the same way
-    # as in future Iris versions.
-    iris.FUTURE.netcdf_promote = True
-
     # Load the "total electron content" cube.
     filename = iris.sample_data_path('space_weather.nc')
     cube = iris.load_cube(filename, 'total electron content')

docs/iris/example_code/Meteorology/hovmoller.py

@@ -17,10 +17,6 @@
 
 
 def main():
-    # Enable a future option, to ensure that the netcdf load works the same way
-    # as in future Iris versions.
-    iris.FUTURE.netcdf_promote = True
-
     # load a single cube of surface temperature between +/- 5 latitude
     fname = iris.sample_data_path('ostia_monthly.nc')
     cube = iris.load_cube(fname,

docs/iris/example_code/Oceanography/atlantic_profiles.py

@@ -22,10 +22,6 @@
 
 
 def main():
-    # Enable a future option, to ensure that the netcdf load works the same way
-    # as in future Iris versions.
-    iris.FUTURE.netcdf_promote = True
-
     # Load the gridded temperature and salinity data.
     fname = iris.sample_data_path('atlantic_profiles.nc')
     cubes = iris.load(fname)

docs/iris/src/userguide/interpolation_and_regridding.rst

@@ -123,7 +123,8 @@ For instance, the :class:`iris.analysis.Linear` scheme requires 1D numeric,
 monotonic, coordinates. Supposing we have a single column cube such as
 the one defined below:
 
-    >>> column = iris.load_cube(iris.sample_data_path('hybrid_height.nc'))[:, 0, 0]
+    >>> cube = iris.load_cube(iris.sample_data_path('hybrid_height.nc'), 'air_potential_temperature')
+    >>> column = cube[:, 0, 0]
     >>> print(column.summary(shorten=True))
     air_potential_temperature / (K)     (model_level_number: 15)
 

docs/iris/src/userguide/real_and_lazy_data.rst

@@ -186,7 +186,7 @@ coordinates' lazy points and bounds:
 
 .. doctest::
 
-    >>> cube = iris.load_cube(iris.sample_data_path('hybrid_height.nc'))
+    >>> cube = iris.load_cube(iris.sample_data_path('hybrid_height.nc'), 'air_potential_temperature')
 
     >>> dim_coord = cube.coord('model_level_number')
     >>> print(dim_coord.has_lazy_points())

docs/iris/src/whatsnew/contributions_2.0/incompatiblechange_2017-Oct-24_netcdf-promote.txt

@@ -0,0 +1,5 @@
+* Deprecated FUTURE flag :attr:`iris.Future.netcdf_promote`.
+
+* Removed deprecated behaviour that does not automatically promote NetCDF variables to cubes.
+  This change means that NetCDF variables that define reference surfaces for dimensionless vertical coordinates
+  will always be promoted and loaded as independent cubes.

lib/iris/__init__.py

@@ -143,7 +143,7 @@ def callback(cube, field, filename):
 class Future(threading.local):
     """Run-time configuration controller."""
 
-    def __init__(self, cell_datetime_objects=True, netcdf_promote=False,
+    def __init__(self, cell_datetime_objects=True, netcdf_promote=True,
                  netcdf_no_unlimited=False, clip_latitudes=False):
         """
         A container for run-time options controls.
@@ -172,9 +172,15 @@ def __init__(self, cell_datetime_objects=True, netcdf_promote=False,
 
         For more details, see :ref:`using-time-constraints`.
 
-        The option `netcdf_promote` controls whether the netCDF loader
-        will expose variables which define reference surfaces for
-        dimensionless vertical coordinates as independent Cubes.
+        .. deprecated:: 2.0.0
+
+            The option `netcdf_promote` is deprecated and will be removed in a
+            future release and the deprecated code paths this option used to
+            toggle have been removed.
+
+            The option `netcdf_promote` controlled whether the netCDF loader
+            exposed variables that defined reference surfaces for
+            dimensionless vertical coordinates as independent Cubes.
 
         The option `netcdf_no_unlimited`, when True, changes the
         behaviour of the netCDF saver, such that no dimensions are set to
@@ -197,13 +203,18 @@ def __repr__(self):
         return msg.format(self.cell_datetime_objects, self.netcdf_promote,
                           self.netcdf_no_unlimited, self.clip_latitudes)
 
-    deprecated_options = {'cell_datetime_objects': 'warning'}
+    deprecated_options = {'cell_datetime_objects': 'warning',
+                          'netcdf_promote': 'error'}
 
     def __setattr__(self, name, value):
         if name in self.deprecated_options:
             level = self.deprecated_options[name]
             if level == 'error':
-                pass
+                emsg = ("setting the 'Future' property {prop!r} has been "
+                        "deprecated to be removed in a future release, and "
+                        "deprecated {prop!r} behaviour has been removed. "
+                        "Please remove code that sets this property.")
+                raise AttributeError(emsg.format(prop=name))
             else:
                 msg = ("setting the 'Future' property {!r} is deprecated "
                        "and will be removed in a future release. "

lib/iris/fileformats/cf.py

@@ -1008,9 +1008,14 @@ def _build(cf_variable):
             for variable_type in self._variable_types:
                 # Prevent grid mapping variables being mis-identified as
                 # CF coordinate variables.
-                ignore = None if issubclass(variable_type, CFGridMappingVariable) else coordinate_names
-                match = variable_type.identify(self._dataset.variables, ignore=ignore,
-                                               target=cf_variable.cf_name, warn=False)
+                if issubclass(variable_type, CFGridMappingVariable):
+                    ignore = None
+                else:
+                    ignore = coordinate_names
+                match = variable_type.identify(self._dataset.variables,
+                                               ignore=ignore,
+                                               target=cf_variable.cf_name,
+                                               warn=False)
                 # Sanity check dimensionality coverage.
                 for cf_name, cf_var in six.iteritems(match):
                     if cf_var.spans(cf_variable):
@@ -1030,7 +1035,8 @@ def _build(cf_variable):
             # Build CF data variable relationships.
             if isinstance(cf_variable, CFDataVariable):
                 # Add global netCDF attributes.
-                cf_group.global_attributes.update(self.cf_group.global_attributes)
+                cf_group.global_attributes.update(
+                    self.cf_group.global_attributes)
                 # Add appropriate "dimensioned" CF coordinate variables.
                 cf_group.update({cf_name: self.cf_group[cf_name] for cf_name
                                     in cf_variable.dimensions if cf_name in
@@ -1043,7 +1049,8 @@ def _build(cf_variable):
                 # Add appropriate formula terms.
                 for cf_var in six.itervalues(self.cf_group.formula_terms):
                     for cf_root in cf_var.cf_terms_by_root:
-                        if cf_root in cf_group and cf_var.cf_name not in cf_group:
+                        if (cf_root in cf_group and
+                                cf_var.cf_name not in cf_group):
                             # Sanity check dimensionality.
                             if cf_var.spans(cf_variable):
                                 cf_group[cf_var.cf_name] = cf_var
@@ -1073,42 +1080,38 @@ def _build(cf_variable):
             _build(cf_variable)
 
         # Determine whether there are any formula terms that
-        # may be promoted to a CFDataVariable.
-        if iris.FUTURE.netcdf_promote:
-            # Restrict promotion to only those formula terms
-            # that are reference surface/phenomenon.
-            for cf_var in six.itervalues(self.cf_group.formula_terms):
-                for cf_root, cf_term in six.iteritems(cf_var.cf_terms_by_root):
-                    cf_root_var = self.cf_group[cf_root]
-                    name = cf_root_var.standard_name or cf_root_var.long_name
-                    terms = reference_terms.get(name, [])
-                    if isinstance(terms, six.string_types) or \
-                            not isinstance(terms, Iterable):
-                        terms = [terms]
-                    cf_var_name = cf_var.cf_name
-                    if cf_term in terms and \
-                            cf_var_name not in self.cf_group.promoted:
-                        data_var = CFDataVariable(cf_var_name, cf_var.cf_data)
-                        self.cf_group.promoted[cf_var_name] = data_var
-                        _build(data_var)
-                        break
-            # Promote any ignored variables.
-            promoted = set()
-            not_promoted = ignored.difference(promoted)
-            while not_promoted:
-                cf_name = not_promoted.pop()
-                if cf_name not in self.cf_group.data_variables and \
-                        cf_name not in self.cf_group.promoted:
-                    data_var = CFDataVariable(cf_name,
-                                              self.cf_group[cf_name].cf_data)
-                    self.cf_group.promoted[cf_name] = data_var
+        # may be promoted to a CFDataVariable and restrict promotion to only
+        # those formula terms that are reference surface/phenomenon.
+        for cf_var in six.itervalues(self.cf_group.formula_terms):
+            for cf_root, cf_term in six.iteritems(cf_var.cf_terms_by_root):
+                cf_root_var = self.cf_group[cf_root]
+                name = cf_root_var.standard_name or cf_root_var.long_name
+                terms = reference_terms.get(name, [])
+                if isinstance(terms, six.string_types) or \
+                        not isinstance(terms, Iterable):
+                    terms = [terms]
+                cf_var_name = cf_var.cf_name
+                if cf_term in terms and \
+                        cf_var_name not in self.cf_group.promoted:
+                    data_var = CFDataVariable(cf_var_name, cf_var.cf_data)
+                    self.cf_group.promoted[cf_var_name] = data_var
                     _build(data_var)
-                # Determine whether there are still any ignored variables
-                # yet to be promoted.
-                promoted.add(cf_name)
-                not_promoted = ignored.difference(promoted)
-        else:
-            _netcdf_promote_warning()
+                    break
+        # Promote any ignored variables.
+        promoted = set()
+        not_promoted = ignored.difference(promoted)
+        while not_promoted:
+            cf_name = not_promoted.pop()
+            if cf_name not in self.cf_group.data_variables and \
+                    cf_name not in self.cf_group.promoted:
+                data_var = CFDataVariable(cf_name,
+                                          self.cf_group[cf_name].cf_data)
+                self.cf_group.promoted[cf_name] = data_var
+                _build(data_var)
+            # Determine whether there are still any ignored variables
+            # yet to be promoted.
+            promoted.add(cf_name)
+            not_promoted = ignored.difference(promoted)
 
 
     def _reset(self):
@@ -1132,12 +1135,3 @@ def _getncattr(dataset, attr, default=None):
     except AttributeError:
         value = default
     return value
-
-
-def _netcdf_promote_warning():
-    msg = ('NetCDF default loading behaviour currently does not expose '
-           'variables which define reference surfaces for dimensionless '
-           'vertical coordinates as independent Cubes. This behaviour is '
-           'deprecated in favour of automatic promotion to Cubes. To switch '
-           'to the new behaviour, set iris.FUTURE.netcdf_promote to True.')
-    warn_deprecated(msg)

lib/iris/tests/integration/test_cube.py

@@ -34,7 +34,7 @@ class Test_aggregated_by(tests.IrisTest):
     def test_agg_by_aux_coord(self):
         problem_test_file = tests.get_data_path(('NetCDF', 'testing',
                                                 'small_theta_colpex.nc'))
-        cube = iris.load_cube(problem_test_file)
+        cube = iris.load_cube(problem_test_file, 'air_potential_temperature')
 
         # Test aggregating by aux coord, notably the `forecast_period` aux
         # coord on `cube`, whose `_points` attribute is a lazy array.

lib/iris/tests/integration/test_netcdf.py

@@ -74,9 +74,10 @@ def test_save_load_loop(self):
         with self.temp_filename(suffix='.nc') as filename, \
                 self.temp_filename(suffix='.nc') as other_filename:
             iris.save(self.cube, filename)
-            cube = iris.load_cube(filename)
+            cube = iris.load_cube(filename, 'air_potential_temperature')
             iris.save(cube, other_filename)
-            other_cube = iris.load_cube(other_filename)
+            other_cube = iris.load_cube(other_filename,
+                                        'air_potential_temperature')
             self.assertEqual(cube, other_cube)
 
 
@@ -120,7 +121,7 @@ def test_hybrid_height_cubes(self):
         sa.points = sa.points * 10
         with self.temp_filename('.nc') as fname:
             iris.save([hh1, hh2], fname)
-            cubes = iris.load(fname)
+            cubes = iris.load(fname, 'air_temperature')
             cubes = sorted(cubes, key=lambda cube: cube.attributes['cube'])
             self.assertCML(cubes)
 
@@ -203,7 +204,7 @@ class TestLazySave(tests.IrisTest):
     def test_lazy_preserved_save(self):
         fpath = tests.get_data_path(('NetCDF', 'label_and_climate',
                                      'small_FC_167_mon_19601101.nc'))
-        acube = iris.load_cube(fpath)
+        acube = iris.load_cube(fpath, 'air_temperature')
         self.assertTrue(acube.has_lazy_data())
         with self.temp_filename('.nc') as nc_path:
             with Saver(nc_path, 'NETCDF4') as saver:

lib/iris/tests/system_test.py

@@ -74,7 +74,7 @@ def system_test_supported_filetypes(self):
         filetypes = ('.nc', '.pp')
         if tests.GRIB_AVAILABLE:
             filetypes += ('.grib2',)
-        with iris.FUTURE.context(netcdf_no_unlimited=True, netcdf_promote=True):
+        with iris.FUTURE.context(netcdf_no_unlimited=True):
             for filetype in filetypes:
                 saved_tmpfile = iris.util.create_temp_filename(suffix=filetype)
                 iris.save(cm, saved_tmpfile)