Fix remaining skips

lib/iris/analysis/trajectory.py

@@ -342,8 +342,6 @@ def interpolate(cube, sample_points, method=None):
             # This is **not** proper mask handling, because we cannot produce a
             # masked result, but it ensures we use a "filled" version of the
             # input in this case.
-            if cube.fill_value is not None:
-                source_data.fill_value = cube.fill_value
             source_data = source_data.filled()
         new_cube.data[:] = source_data
         # NOTE: we assign to "new_cube.data[:]" and *not* just "new_cube.data",

lib/iris/fileformats/pp.py

@@ -976,9 +976,8 @@ def _data_bytes_to_shaped_array(data_bytes, lbpack, boundary_packing,
         # condition" array, which is split into 4 quartiles, North
         # East, South, West and where North and South contain the corners.
         compressed_data = data
-        if data_type.kind != 'i':
-            data_type = np.dtype('f8')
-        data = np.full(data_shape, np.nan, dtype=data_type)
+        data = np.ma.masked_all(data_shape)
+        data.fill_value = mdi
 
         boundary_height = boundary_packing.y_halo + boundary_packing.rim_width
         boundary_width = boundary_packing.x_halo + boundary_packing.rim_width
@@ -1019,9 +1018,8 @@ def _data_bytes_to_shaped_array(data_bytes, lbpack, boundary_packing,
                              'Could not load.')
         land_mask = mask.data.astype(np.bool)
         sea_mask = ~land_mask
-        if data_type.kind != 'i':
-            data_type = np.dtype('f8')
-        new_data = np.full(land_mask.shape, np.nan, dtype=data_type)
+        new_data = np.ma.masked_all(land_mask.shape)
+        new_data.fill_value = mdi
         if lbpack.n3 == 1:
             # Land mask packed data.
             # Sometimes the data comes in longer than it should be (i.e. it

lib/iris/tests/__init__.py

@@ -1156,16 +1156,6 @@ class MyPlotTests(test.GraphicsTest):
     return skip(fn)
 
 
-# Control for @skip_dask_mask : Set to False to run the dask mask failing
-# tests.
-_SKIP_DASK_MASK_TESTS = True
-
-
-skip_dask_mask = unittest.skipIf(_SKIP_DASK_MASK_TESTS,
-                                 'Test(s) currently fail on Dask Mask Array '
-                                 'feature branch.')
-
-
 skip_grib = unittest.skipIf(not GRIB_AVAILABLE, 'Test(s) require "gribapi", '
                                                 'which is not available.')
 

lib/iris/tests/integration/test_ff.py

@@ -80,7 +80,6 @@ def test_cube_data(self):
                       [4.626897, 6.520156]]),
             atol=1.0e-6)
 
-    @tests.skip_dask_mask
     def test_cube_mask(self):
         # Check the data mask : should be just the centre 6x2 section.
         cube = self.test_cube

lib/iris/tests/integration/test_trajectory.py

@@ -172,7 +172,6 @@ def setUp(self):
         self.sample_points = [('longitude', longitudes),
                               ('latitude', latitudes)]
 
-    @tests.skip_dask_mask
     def test_tri_polar(self):
         # extract
         sampled_cube = traj_interpolate(self.cube, self.sample_points)
@@ -204,7 +203,7 @@ def test_tri_polar__nearest(self):
         # TODO: arguably, we should support masked data properly in the
         # interpolation routine.  In the legacy code, that is unfortunately
         # just not the case.
-        test_cube.fill_value = 0
+        test_cube.data.fill_value = 0
 
         # Test points on a regular global grid, with unrelated steps + offsets
         # and an extended range of longitude values.

lib/iris/tests/unit/fileformats/grib/save_rules/test_data_section.py

@@ -93,11 +93,10 @@ def test_simple(self):
         # Check the correct data values have been set.
         self.assertValues(grib_api, np.arange(5))
 
-    @tests.skip_dask_mask
     def test_masked_with_finite_fill_value(self):
         cube = iris.cube.Cube(np.ma.MaskedArray([1.0, 2.0, 3.0, 1.0, 2.0, 3.0],
-                                                mask=[0, 0, 0, 1, 1, 1]),
-                              fill_value=2000)
+                                                mask=[0, 0, 0, 1, 1, 1],
+                                                fill_value=2000))
         grib_message = mock.sentinel.GRIB_MESSAGE
         with mock.patch(GRIB_API) as grib_api:
             data_section(cube, grib_message)
@@ -107,11 +106,10 @@ def test_masked_with_finite_fill_value(self):
         # Check the correct data values have been set.
         self.assertValues(grib_api, [1, 2, 3, FILL, FILL, FILL])
 
-    @tests.skip_dask_mask
     def test_masked_with_nan_fill_value(self):
         cube = iris.cube.Cube(np.ma.MaskedArray([1.0, 2.0, 3.0, 1.0, 2.0, 3.0],
-                                                mask=[0, 0, 0, 1, 1, 1]),
-                              fill_value=np.nan)
+                                                mask=[0, 0, 0, 1, 1, 1],
+                                                fill_value=np.nan))
         grib_message = mock.sentinel.GRIB_MESSAGE
         with mock.patch(GRIB_API) as grib_api:
             data_section(cube, grib_message)
@@ -134,14 +132,13 @@ def test_scaled(self):
         # Check the correct data values have been set.
         self.assertValues(grib_api, np.arange(5) * 1000)
 
-    @tests.skip_dask_mask
     def test_scaled_with_finite_fill_value(self):
         # When re-scaling masked data with a finite fill value, ensure
         # the fill value and any filled values are also re-scaled.
         cube = iris.cube.Cube(np.ma.MaskedArray([1.0, 2.0, 3.0, 1.0, 2.0, 3.0],
-                                                mask=[0, 0, 0, 1, 1, 1]),
-                              standard_name='geopotential_height', units='km',
-                              fill_value=2000)
+                                                mask=[0, 0, 0, 1, 1, 1],
+                                                fill_value=2000),
+                              standard_name='geopotential_height', units='km')
         grib_message = mock.sentinel.GRIB_MESSAGE
         with mock.patch(GRIB_API) as grib_api:
             data_section(cube, grib_message)
@@ -151,15 +148,14 @@ def test_scaled_with_finite_fill_value(self):
         # Check the correct data values have been set.
         self.assertValues(grib_api, [1000, 2000, 3000, FILL, FILL, FILL])
 
-    @tests.skip_dask_mask
     def test_scaled_with_nan_fill_value(self):
         # When re-scaling masked data with a NaN fill value, ensure
         # a fill value is chosen which allows for the scaling, and any
         # filled values match the chosen fill value.
         cube = iris.cube.Cube(np.ma.MaskedArray([-1.0, 2.0, -1.0, 2.0],
-                                                mask=[0, 0, 1, 1]),
-                              standard_name='geopotential_height', units='km',
-                              fill_value=np.nan)
+                                                mask=[0, 0, 1, 1],
+                                                fill_value=np.nan),
+                              standard_name='geopotential_height', units='km')
         grib_message = mock.sentinel.GRIB_MESSAGE
         with mock.patch(GRIB_API) as grib_api:
             data_section(cube, grib_message)

lib/iris/tests/unit/fileformats/netcdf/test__load_cube.py

@@ -32,84 +32,6 @@
 from iris.tests import mock
 
 
-@tests.skip_dask_mask
-class TestFillValue(tests.IrisTest):
-    def setUp(self):
-        name = 'iris.fileformats.netcdf._assert_case_specific_facts'
-        patch = mock.patch(name)
-        patch.start()
-        self.addCleanup(patch.stop)
-
-        self.engine = mock.Mock()
-        self.cf = None
-        self.filename = 'DUMMY'
-
-    def _make_cf_var(self, dtype):
-        variable = mock.Mock(spec=netCDF4.Variable,
-                             dtype=dtype)
-        cf_var = mock.MagicMock(spec=iris.fileformats.cf.CFVariable,
-                                cf_data=variable,
-                                cf_name='DUMMY_VAR',
-                                cf_group=mock.Mock(),
-                                dtype=dtype,
-                                shape=(1,))
-        return cf_var
-
-    def _test(self, cf_var, expected_fill_value):
-        cube = _load_cube(self.engine, self.cf, cf_var, self.filename)
-        self.assertEqual(cube.fill_value, expected_fill_value)
-
-    def test_from_attribute_dtype_f4(self):
-        # A _FillValue attribute on the netCDF variable should end up as
-        # the fill_value for the cube.
-        dtype = np.dtype('f4')
-        fill_value = 9999.
-        cf_var = self._make_cf_var(dtype)
-        cf_var.cf_data._FillValue = fill_value
-        self._test(cf_var, fill_value)
-
-    def test_from_default_dtype_f4(self):
-        # Without an explicit _FillValue attribute on the netCDF
-        # variable, the fill value should be set to `None`.
-        dtype = np.dtype('f4')
-        cf_var = self._make_cf_var(dtype)
-        self._test(cf_var, None)
-
-    def test_from_attribute_dtype_i4(self):
-        # A _FillValue attribute on the netCDF variable should end up as
-        # the fill_value for the cube.
-        dtype = np.dtype('i4')
-        fill_value = 9999
-        cf_var = self._make_cf_var(dtype)
-        cf_var.cf_data._FillValue = fill_value
-        self._test(cf_var, fill_value)
-
-    def test_from_default_dtype_i4(self):
-        # Without an explicit _FillValue attribute on the netCDF
-        # variable, the fill value should be set to `None`.
-        dtype = np.dtype('i4')
-        cf_var = self._make_cf_var(dtype)
-        self._test(cf_var, None)
-
-    def test_from_attribute_with_scale_offset(self):
-        # The _FillValue attribute still takes priority even when an
-        # offset/scale transformation takes place on the data.
-        dtype = np.dtype('i2')
-        fill_value = 999
-        cf_var = self._make_cf_var(dtype)
-        cf_var.scale_factor = np.float64(1.5)
-        cf_var.cf_data._FillValue = fill_value
-        self._test(cf_var, fill_value)
-
-    def test_from_default_with_scale_offset(self):
-        # Without an explicit _FillValue attribute on the netCDF
-        # variable, the fill value should be set to `None`.
-        dtype = np.dtype('i2')
-        cf_var = self._make_cf_var(dtype)
-        cf_var.scale_factor = np.float64(1.5)
-        self._test(cf_var, None)
-
-
 class TestCoordAttributes(tests.IrisTest):
     @staticmethod
     def _patcher(engine, cf, cf_group):