fix syntax of lots of API references

Author: TomNicholas

docs/source/data-structures.rst

@@ -23,8 +23,8 @@ Data Structures
 DataTree
 --------
 
-:py:class:``DataTree`` is xarray's highest-level data structure, able to organise heterogeneous data which
-could not be stored inside a single ``Dataset`` object. This includes representing the recursive structure of multiple
+:py:class:`DataTree` is xarray's highest-level data structure, able to organise heterogeneous data which
+could not be stored inside a single :py:class:`Dataset` object. This includes representing the recursive structure of multiple
 `groups`_ within a netCDF file or `Zarr Store`_.
 
 .. _groups: https://www.unidata.ucar.edu/software/netcdf/workshops/2011/groups-types/GroupsIntro.html
@@ -128,8 +128,8 @@ It is at tree construction time that consistency checks are enforced. For instan
 Alternatively you can also create a ``DataTree`` object from
 
 - An ``xarray.Dataset`` using ``Dataset.to_node()`` (not yet implemented),
-- A dictionary mapping directory-like paths to either ``DataTree`` nodes or data, using ``DataTree.from_dict()``,
-- A netCDF or Zarr file on disk with ``open_datatree()``. See :ref:`reading and writing files <io>`.
+- A dictionary mapping directory-like paths to either ``DataTree`` nodes or data, using :py:meth:`DataTree.from_dict()`,
+- A netCDF or Zarr file on disk with :py:func:`open_datatree()`. See :ref:`reading and writing files <io>`.
 
 
 DataTree Contents
@@ -152,7 +152,7 @@ We can also access all the data in a single node through a dataset-like view
 
 This demonstrates the fact that the data in any one node is equivalent to the contents of a single ``xarray.Dataset`` object.
 The ``DataTree.ds`` property returns an immutable view, but we can instead extract the node's data contents as a new (and mutable)
-``xarray.Dataset`` object via ``.to_dataset()``:
+``xarray.Dataset`` object via :py:meth:`DataTree.to_dataset()`:
 
 .. ipython:: python
 
@@ -184,10 +184,10 @@ For example, to create this example datatree from scratch, we could have written
 
 To change the variables in a node of a ``DataTree``, you can use all the standard dictionary
 methods, including ``values``, ``items``, ``__delitem__``, ``get`` and
-:py:meth:`~xarray.DataTree.update`.
+:py:meth:`DataTree.update`.
 Note that assigning a ``DataArray`` object to a ``DataTree`` variable using ``__setitem__`` or ``update`` will
-:ref:`automatically align<update>` the array(s) to the original node's indexes.
+:ref:`automatically align <update>` the array(s) to the original node's indexes.
 
-If you copy a ``DataTree`` using the ``:py:func::copy`` function or the :py:meth:`~xarray.DataTree.copy` it will copy the subtree,
+If you copy a ``DataTree`` using the :py:func:`copy` function or the :py:meth:`DataTree.copy` method it will copy the subtree,
 meaning that node and children below it, but no parents above it.
 Like for ``Dataset``, this copy is shallow by default, but you can copy all the underlying data arrays by calling ``dt.copy(deep=True)``.

docs/source/hierarchical-data.rst

@@ -14,6 +14,7 @@ Working With Hierarchical Data
     np.random.seed(123456)
     np.set_printoptions(threshold=10)
 
+
 Why Hierarchical Data?
 ----------------------
 
@@ -33,7 +34,7 @@ Often datasets like this cannot easily fit into a single ``xarray.Dataset`` obje
 or are more usefully thought of as groups of related ``xarray.Dataset`` objects.
 For this purpose we provide the :py:class:`DataTree` class.
 
-This page explains in detail how to understand and use the different features of the :py:class:`DataTree` class for your own heirarchical data needs.
+This page explains in detail how to understand and use the different features of the :py:class:`DataTree` class for your own hierarchical data needs.
 
 .. _node relationships:
 

docs/source/io.rst

@@ -17,9 +17,9 @@ Groups
 ~~~~~~
 
 Whilst netCDF groups can only be loaded individually as Dataset objects, a whole file of many nested groups can be loaded
-as a single ``:py:class::DataTree`` object.
-To open a whole netCDF file as a tree of groups use the ``:py:func::open_datatree()`` function.
-To save a DataTree object as a netCDF file containing many groups, use the ``:py:meth::DataTree.to_netcdf()`` method.
+as a single :py:class:`DataTree` object.
+To open a whole netCDF file as a tree of groups use the :py:func:`open_datatree` function.
+To save a DataTree object as a netCDF file containing many groups, use the :py:meth:`DataTree.to_netcdf` method.
 
 
 .. _netcdf.group.warning:
@@ -30,7 +30,7 @@ To save a DataTree object as a netCDF file containing many groups, use the ``:py
 
     In particular in the netCDF data model dimensions are entities that can exist regardless of whether any variable possesses them.
     This is in contrast to `xarray's data model <https://docs.xarray.dev/en/stable/user-guide/data-structures.html>`_
-    (and hence :ref:`datatree's data model<data structures>`) in which the dimensions of a (Dataset/Tree)
+    (and hence :ref:`datatree's data model <data structures>`) in which the dimensions of a (Dataset/Tree)
     object are simply the set of dimensions present across all variables in that dataset.
 
     This means that if a netCDF file contains dimensions but no variables which possess those dimensions,
@@ -43,10 +43,10 @@ Zarr
 Groups
 ~~~~~~
 
-Nested groups in zarr stores can be represented by loading the store as a ``:py:class::DataTree`` object, similarly to netCDF.
-To open a whole zarr store as a tree of groups use the ``:py:func::open_datatree()`` function.
-To save a DataTree object as a zarr store containing many groups, use the ``:py:meth::DataTree.to_zarr()`` method.
+Nested groups in zarr stores can be represented by loading the store as a :py:class:`DataTree` object, similarly to netCDF.
+To open a whole zarr store as a tree of groups use the :py:func:`open_datatree` function.
+To save a DataTree object as a zarr store containing many groups, use the :py:meth:`DataTree.to_zarr()` method.
 
 .. note::
-    Note that perfect round-tripping should always be possible with a zarr store (:ref:`unlike for netCDF files<netcdf.group.warning>`),
+    Note that perfect round-tripping should always be possible with a zarr store (:ref:`unlike for netCDF files <netcdf.group.warning>`),
     as zarr does not support "unused" dimensions.