Multiple docs improvements: fix warnings + increase cross-referencing.

docs/changelog_fragments/68.feat.rst

@@ -3,4 +3,4 @@ The :class:`ncdata.NcData` objects can be indexed with the ``[]`` operation, or
 specifed dimensions with the :meth:`~ncdata.NcData.slicer` method.
 This is based on the new :meth:`~ncdata.utils.index_by_dimensions()` utility method
 and :class:`~ncdata.utils.Slicer` class.
-See: :ref:`indexing_overview`
+See: :ref:`utils_indexing`

docs/conf.py

@@ -93,7 +93,13 @@
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
 # This pattern also affects html_static_path and html_extra_path.
-exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
+exclude_patterns = [
+    "_build",
+    "Thumbs.db",
+    ".DS_Store",
+    "changelog_fragments",
+    "details/api/modules.rst",
+]
 
 
 # -- Options for HTML output -------------------------------------------------

docs/details/developer_notes.rst

@@ -11,12 +11,21 @@ A new change-note fragment file should be included in each PR, but is normally c
 with a ``towncrier`` command-line command:
 
 * shortly, with ``towncrier create --content "mynotes..." <ISSUE-num>.<category>.rst``
-* ... or for longer forms, use ``towncrier create --edit``.
-* Here, "<category>" is one of feat/doc/bug/dev/misc.  Which are: user features;
-  bug fixes; documentation changes; general developer-relevant changes;
-  or "miscellaneous".
+
+  ... or, for longer content, use ``towncrier create --edit``.
+
+* Here, "<category>" is one of:
+
+    * "feat": user features
+    * "doc": documentation changes
+    * "bug": bug fixes
+    * "def": general developer-relevant changes
+    * "misc": miscellaneous
+
   (For reference, these categories are configured in ``pyproject.toml``).
+
 * the fragment files are stored in ``docs/changelog_fragments``.
+
 * N.B. for this to work well, every change should be identified with a matching github issue.
   If there are multiple associated PRs, they should all be linked to the issue.
 
@@ -26,33 +35,36 @@ Documentation build
 
 For a full docs-build:
 
-* a simple ``$ make html`` will do for now
+* The most useful way is simply ``$ cd docs`` and ``$ make html-keeplog``.
+   * Note: the plainer ``$ make html`` is the same, but "-keeplog", in addition, preserves the
+     changelog fragments **and** reverts the change_log.rst after the html build:
+     This stops you accidentally including a "built" changelog when making further commits.
 * The ``docs/Makefile`` wipes the API docs and invokes sphinx-apidoc for a full rebuild
 * It also calls towncrier to clear out the changelog fragments + update ``docs/change_log.rst``.
-  This should be reverted before pushing your PR -- i.e. leave changenotes in the fragments.
-* the results is then available at ``docs/_build/html/index.html``.
+* ( *assuming "-keeplog"*: fragments and change_notes.rst are then reverted, undoing the towncrier build ).
+* the result is then available at ``docs/_build/html/index.html``.
 
 .. note::
 
-    * the above is just for *local testing*, if required.
+    * the above is just for **local testing**, when required.
     * For PRs (and releases), we also provide *automatic* builds on GitHub,
-      via `ReadTheDocs <https://readthedocs.org/projects/ncdata/>`_
+      via ReadTheDocs_.
 
 
 Release actions
 ---------------
 
 #. Update the :ref:`change-log page <change_log>` in the details section
 
-    #. ensure all major changes + PRs are referenced in the :ref:`change_notes` section.
+    #. start with ``$ towncrier build``
 
-       * The starting point for this is now just : ``$ towncrier build``.
+    #. ensure all major changes + PRs are referenced in the :ref:`change_notes` section.
 
     #. update the "latest version" stated in the :ref:`development_status` section
 
 #. Cut a release on GitHub
 
-    * this triggers a new docs version on `ReadTheDocs <https://readthedocs.org/projects/ncdata>`_.
+    * this triggers a new docs version on ReadTheDocs_.
 
 #. Build the distribution
 
@@ -109,3 +121,6 @@ Release actions
 
        * wait a few hours..
        * check that the new version appears in the output of ``$ conda search ncdata``
+
+
+.. _ReadTheDocs: https://readthedocs.org/projects/ncdata

docs/userdocs/user_guide/common_operations.rst

@@ -55,6 +55,19 @@ Example :
 
     >>> dataset.variables["x"].avals["units"] = "m s-1"
 
+
+There is also an :meth:`~ncdata.NameMap.addall` method, which adds multiple content
+objects in one operation.
+
+.. doctest:: python
+
+    >>> vars = [NcVariable(name) for name in ("a", "b", "c")]
+    >>> dataset.variables.addall(vars)
+    >>> list(dataset.variables)
+    ['x', 'a', 'b', 'c']
+
+.. _operations_rename:
+
 Rename
 ------
 A component can be renamed with the :meth:`~ncdata.NameMap.rename` method.  This changes
@@ -67,6 +80,18 @@ Example :
 
     >>> dataset.variables.rename("x", "y")
 
+result:
+
+.. doctest:: python
+
+    >>> print(dataset.variables.get("x"))
+    None
+    >>> print(dataset.variables.get("y"))
+    <NcVariable(<no-dtype>): y()
+        y:units = 'm s-1'
+    >
+
+
 .. warning::
     Renaming a dimension will not rename references to it (i.e. in variables), which
     obviously may cause problems.
@@ -123,14 +148,29 @@ Equality Testing
 ----------------
 We implement equality operations ``==`` / ``!=`` for all the core data objects.
 
-However, simple equality testing on :class:`@ncdata.NcData` and :class:`@ncdata.NcVariable`
-objects can be very costly if it requires comparing large data arrays.
+.. doctest::
+
+    >>> vA = dataset.variables["a"]
+    >>> vB = dataset.variables["b"]
+    >>> vA == vB
+    False
+
+.. doctest::
+
+    >>> dataset == dataset.copy()
+    True
+
+.. warning::
+    Equality testing for :class:`~ncdata.NcData` and :class:`~ncdata.NcVariable` actually
+    calls the :func:`ncdata.utils.dataset_differences` and
+    :func:`ncdata.utils.variable_differences` utilities.
+
+    This can be very costly if it needs to compare large data arrays.
 
 If you need to avoid comparing large (and possibly lazy) arrays then you can use the
 :func:`ncdata.utils.dataset_differences` and
-:func:`ncdata.utils.variable_differences` utility functions.
-These functions also provide multiple options to enable more tolerant comparison,
-such as allowing variables to have a different ordering.
+:func:`ncdata.utils.variable_differences` utility functions directly instead.
+These provide a ``check_var_data=False`` option, to ignore differences in data content.
 
 See: :ref:`utils_equality`
 

docs/userdocs/user_guide/data_objects.rst

@@ -186,7 +186,9 @@ However, for most operations on attributes, it is much easier to use the ``.aval
 property instead.  This accesses *the same attributes*, but in the form of a simple
 "name: value" dictionary.
 
-Thus for example, to fetch an attribute you would usually write just :
+Get attribute value
+^^^^^^^^^^^^^^^^^^^
+For example, to fetch an attribute you would usually write just :
 
 .. testsetup::
 
@@ -205,23 +207,15 @@ and **not** :
 
 .. doctest:: python
 
-    >>> # WRONG: this reads an NcAttribute, not its value
+    >>> # WRONG: this get the NcAttribute object, not its value
     >>> unit = dataset.variables["x"].attributes["units"]
 
-or:
-
-.. doctest:: python
-
-    >>> # WRONG: this gets NcAttribute.value as a character array, not a string
+    >>> # WRONG: this returns a character array, not a string
     >>> unit = dataset.variables["x"].attributes["units"].value
 
-or even (which is at least correct):
-
-.. doctest:: python
-
-    >>> unit = dataset.variables["x"].attributes["units"].as_python_value()
-
 
+Set attribute value
+^^^^^^^^^^^^^^^^^^^
 Likewise, to **set** a value, you would normally just
 
 .. doctest:: python
@@ -236,9 +230,11 @@ and **not**
     >>> dataset.variables["x"].attributes["units"].value = "K"
 
 
-Note also, that as the ``.avals`` is a dictionary, you can use standard dictionary
-methods such as ``update`` and ``get`` to perform other operations in a relatively
-natural, Pythonic way.
+``.avals`` as a dictionary
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+Note also, that as ``.avals`` is a dictionary, you can use standard dictionary
+methods such as ``pop``, ``update`` and ``get`` to perform other operations in a
+relatively natural, Pythonic way.
 
 .. doctest:: python
 
@@ -247,6 +243,12 @@ natural, Pythonic way.
 
     >>> dataset.attributes.update({"experiment": "A407", "expt_run": 704})
 
+.. note::
+    The new ``.avals`` property effectively replaces the old
+    :meth:`~ncdata.NcData.get_attrval` and :meth:`~ncdata.NcData.set_attrval` methods,
+    which are now deprecated and will eventually be removed.
+
+
 .. _data-constructors:
 
 Core Object Constructors

docs/userdocs/user_guide/howtos.rst

@@ -377,8 +377,8 @@ See: :ref:`copy_notes`
 
 Extract a subsection by indexing
 --------------------------------
-The nicest way is usually just to use the :meth:`~ncdata.Ncdata.slicer` method to specify
-dimensions to index, and then index the result.
+The nicest way is usually to use the NcData :meth:`~ncdata.Ncdata.slicer` method to
+specify dimensions to index, and then index the result.
 
 .. testsetup::
 
@@ -388,22 +388,22 @@ dimensions to index, and then index the result.
     >>> for nn, dim in full_data.dimensions.items():
     ...    full_data.variables.add(NcVariable(nn, dimensions=[nn], data=np.arange(dim.size)))
 
-.. doctest::
-
-    >>> for dimname in full_data.dimensions:
-    ...     print(dimname, ':', full_data.variables[dimname].data)
-    x : [0 1 2 3 4 5 6]
-    y : [0 1 2 3 4 5]
-
 .. doctest::
 
     >>> data_region = full_data.slicer("y", "x")[3, 1::2]
 
+effect:
+
 .. doctest::
 
+    >>> for dimname in full_data.dimensions:
+    ...     print("(original)", dimname, ':', full_data.variables[dimname].data)
+    (original) x : [0 1 2 3 4 5 6]
+    (original) y : [0 1 2 3 4 5]
+
     >>> for dimname in data_region.dimensions:
-    ...     print(dimname, ':', data_region.variables[dimname].data)
-    x : [1 3 5]
+    ...     print("(new)", dimname, ':', data_region.variables[dimname].data)
+    (new) x : [1 3 5]
 
 You can also slice data directly, which simply acts on the dimensions in order:
 
@@ -413,7 +413,7 @@ You can also slice data directly, which simply acts on the dimensions in order:
     >>> data_region_2 == data_region
     True
 
-See: :ref:`indexing_overview`
+See: :ref:`utils_indexing`
 
 
 Read data from a NetCDF file
@@ -454,8 +454,8 @@ Use the ``dim_chunks`` argument in the :func:`ncdata.netcdf4.from_nc4` function
 
     >>> from ncdata.netcdf4 import from_nc4
     >>> ds = from_nc4(filepath, dim_chunks={"time": 3})
-    >>> print(ds.variables["time"].data.chunksize)
-    (3,)
+    >>> print(ds.variables["time"].data.chunks)
+    ((3, 3, 3, 1),)
 
 
 Save data to a new file
@@ -531,8 +531,28 @@ Use :func:`ncdata.xarray.to_xarray` and :func:`ncdata.xarray.from_xarray`.
     >>> from ncdata.xarray import from_xarray, to_xarray
     >>> dataset = xarray.open_dataset(filepath)
     >>> ncdata = from_xarray(dataset)
-    >>>
+
+    >>> print(ncdata)
+    <NcData: <'no-name'>
+        variables:
+            <NcVariable(float64): vx()
+                vx:units = 'm.s-1'
+                vx:q = 4.2
+                vx:_FillValue = nan
+            >
+    <BLANKLINE>
+        global attributes:
+            :experiment = 'A301.7'
+    >
+
     >>> ds2 = to_xarray(ncdata)
+    >>> print(ds2)
+    <xarray.Dataset> Size: 8B
+    Dimensions:  ()
+    Data variables:
+        vx       float64 8B nan
+    Attributes:
+        experiment:  A301.7
 
 Note that:
 
@@ -573,7 +593,7 @@ passed using specific dictionary keywords, e.g.
     ...   iris_load_kwargs={'constraints': 'air_temperature'},
     ...   xr_save_kwargs={'unlimited_dims': ('time',)},
     ... )
-    ...
+
 
 Combine data from different input files into one output
 -------------------------------------------------------