minor tweaks.

benchmarks/asv_delegated_conda.py

@@ -4,8 +4,7 @@
 # See LICENSE in the root of the repository for full licensing details.
 """ASV plug-in providing an alternative :class:`asv.plugins.conda.Conda` subclass.
 
-ASV plug-in providing an alternative :class:`asv.plugins.conda.Conda`
-subclass that manages the Conda environment via custom user scripts.
+Manages the Conda environment via custom user scripts.
 
 """
 

benchmarks/benchmarks/sperf/equality.py

@@ -12,7 +12,7 @@ class CubeEquality(FileMixin):
     r"""Benchmark time and memory costs.
 
     Benchmark time and memory costs of comparing :class:`~iris.cube.Cube`\\ s
-     with attached :class:`~iris.experimental.ugrid.mesh.Mesh`\\ es.
+    with attached :class:`~iris.experimental.ugrid.mesh.Mesh`\\ es.
 
     Uses :class:`FileMixin` as the realistic case will be comparing
     :class:`~iris.cube.Cube`\\ s that have been loaded from file.

lib/iris/_concatenate.py

@@ -733,7 +733,7 @@ class _ProtoCube:
     """Framework for concatenating multiple source-cubes over one common dimension."""
 
     def __init__(self, cube):
-        """Create a new _ProtoCube  and record the cube as a source-cube.
+        """Create a new _ProtoCube and record the cube as a source-cube.
 
         Create a new _ProtoCube from the given cube and record the cube
         as a source-cube.

lib/iris/_merge.py

@@ -859,6 +859,10 @@ def _build_separable_group(
 ):
     """Update the space with the first separable consistent group.
 
+    Update the space with the first separable consistent group that
+    satisfies a valid functional relationship with all other candidate
+    dimensions in the group.
+
     For example, the group ABCD and separable consistent group CD,
     if A = f(C, D) and B = f(C, D) then update the space with
     "A: (C, D), B: (C, D), C: None, D: None". Where "A: (C, D)" means

lib/iris/coords.py

@@ -1191,7 +1191,7 @@ def _get_2d_coord_bound_grid(bounds):
 
 
 class Cell(namedtuple("Cell", ["point", "bound"])):
-    """An coordinate cell containing a single point, or point and bounds.
+    """A coordinate cell containing a single point, or point and bounds.
 
     An immutable representation of a single cell of a coordinate, including the
     sample point and/or boundary position.

lib/iris/fileformats/_structured_array_identification.py

@@ -4,15 +4,9 @@
 # See LICENSE in the root of the repository for full licensing details.
 r"""Identification of multi-dimensional structure in a flat sequence of homogeneous objects.
 
-One application of this is to efficiently identify a higher dimensional
-structure from a sorted sequence of PPField instances; for an example, given
-a list of 12 PPFields, identification that there are 3 unique "time" values
-and 4 unique "height" values where time and height are linearly independent
-means that we could construct a resulting cube with a shape of
-``(3, 4) + <shape of a single field>``.
-
 The purpose of this module is to provide utilities for the identification
 of multi-dimensional structure in a flat sequence of homogeneous objects.
+
 One application of this is to efficiently identify a higher dimensional
 structure from a sorted sequence of PPField instances; for an example, given
 a list of 12 PPFields, identification that there are 3 unique "time" values

lib/iris/fileformats/pp.py

@@ -1716,8 +1716,9 @@ def _create_field_data(field, data_shape, land_mask_field=None):
     """Modify a field's ``_data`` attribute.
 
     Modify a field's ``_data`` attribute either by:
-     * converting a 'deferred array bytes' tuple into a lazy array,
-     * converting LoadedArrayBytes into an actual numpy array.
+
+    * converting a 'deferred array bytes' tuple into a lazy array,
+    * converting LoadedArrayBytes into an actual numpy array.
 
     If 'land_mask_field' is passed (not None), then it contains the associated
     landmask, which is also a field :  Its data array is used as a template for

lib/iris/util.py

@@ -1736,10 +1736,6 @@ def _meshgrid(*xi, **kwargs):
     associated input 1D coordinate. This is not the case prior to numpy v1.13,
     where the output dtype is cast up to its highest resolution, regardlessly.
 
-    @numpy v1.13, the dtype of each output n-D coordinate is the same as its
-    associated input 1D coordinate. This is not the case prior to numpy v1.13,
-    where the output dtype is cast up to its highest resolution, regardlessly.
-
     Reference: https://github.com/numpy/numpy/pull/5302
 
     """