DOC: Fix some target not found Sphinx warnings. (#20140)

4 files changed, 29 insertions, 22 deletions

diff --git a/doc/source/reference/c-api/types-and-structures.rst b/doc/source/reference/c-api/types-and-structures.rst
index 605a4ae71..1ea47b498 100644
--- a/doc/source/reference/c-api/types-and-structures.rst
+++ b/doc/source/reference/c-api/types-and-structures.rst
@@ -286,6 +286,11 @@ PyArrayDescr_Type and PyArray_Descr
        array like behavior. Each bit in this member is a flag which are named
        as:
 
+   .. c:member:: int alignment
+
+       Non-NULL if this type is an array (C-contiguous) of some other type
+
+
 ..
   dedented to allow internal linking, pending a refactoring
 
diff --git a/doc/source/user/basics.io.genfromtxt.rst b/doc/source/user/basics.io.genfromtxt.rst
index 8fe7565aa..6a1ba75dd 100644
--- a/doc/source/user/basics.io.genfromtxt.rst
+++ b/doc/source/user/basics.io.genfromtxt.rst
@@ -231,9 +231,7 @@ When ``dtype=None``, the type of each column is determined iteratively from
 its data.  We start by checking whether a string can be converted to a
 boolean (that is, if the string matches ``true`` or ``false`` in lower
 cases); then whether it can be converted to an integer, then to a float,
-then to a complex and eventually to a string.  This behavior may be changed
-by modifying the default mapper of the
-:class:`~numpy.lib._iotools.StringConverter` class.
+then to a complex and eventually to a string.
 
 The option ``dtype=None`` is provided for convenience.  However, it is
 significantly slower than setting the dtype explicitly.
@@ -514,15 +512,15 @@ output array will then be a :class:`~numpy.ma.MaskedArray`.
 Shortcut functions
 ==================
 
-In addition to :func:`~numpy.genfromtxt`, the :mod:`numpy.lib.npyio` module
+In addition to :func:`~numpy.genfromtxt`, the ``numpy.lib.npyio`` module
 provides several convenience functions derived from
 :func:`~numpy.genfromtxt`.  These functions work the same way as the
 original, but they have different default values.
 
-:func:`~numpy.npyio.recfromtxt`
+``numpy.lib.npyio.recfromtxt``
    Returns a standard :class:`numpy.recarray` (if ``usemask=False``) or a
-   :class:`~numpy.ma.mrecords.MaskedRecords` array (if ``usemaske=True``).  The
+   ``numpy.ma.mrecords.MaskedRecords`` array (if ``usemaske=True``).  The
    default dtype is ``dtype=None``, meaning that the types of each column
    will be automatically determined.
-:func:`~numpy.npyio.recfromcsv`
-   Like :func:`~numpy.npyio.recfromtxt`, but with a default ``delimiter=","``.
+``numpy.lib.npyio.recfromcsv``
+   Like ``numpy.lib.npyio.recfromtxt``, but with a default ``delimiter=","``.
diff --git a/doc/source/user/basics.rec.rst b/doc/source/user/basics.rec.rst
index 1e6f30506..7f487f39b 100644
--- a/doc/source/user/basics.rec.rst
+++ b/doc/source/user/basics.rec.rst
@@ -579,12 +579,13 @@ As an optional convenience numpy provides an ndarray subclass,
 attribute instead of only by index.
 Record arrays use a special datatype, :class:`numpy.record`, that allows
 field access by attribute on the structured scalars obtained from the array.
-The :mod:`numpy.rec` module provides functions for creating recarrays from
+The ``numpy.rec`` module provides functions for creating recarrays from
 various objects.
 Additional helper functions for creating and manipulating structured arrays
 can be found in :mod:`numpy.lib.recfunctions`.
 
-The simplest way to create a record array is with ``numpy.rec.array``::
+The simplest way to create a record array is with
+:func:`numpy.rec.array <numpy.core.records.array>`::
 
  >>> recordarr = np.rec.array([(1, 2., 'Hello'), (2, 3., "World")],
  ...                    dtype=[('foo', 'i4'),('bar', 'f4'), ('baz', 'S10')])
@@ -600,14 +601,14 @@ The simplest way to create a record array is with ``numpy.rec.array``::
  >>> recordarr[1].baz
  b'World'
 
-:func:`numpy.rec.array` can convert a wide variety of arguments into record
-arrays, including structured arrays::
+:func:`numpy.rec.array <numpy.core.records.array>` can convert a wide variety
+of arguments into record arrays, including structured arrays::
 
  >>> arr = np.array([(1, 2., 'Hello'), (2, 3., "World")],
  ...             dtype=[('foo', 'i4'), ('bar', 'f4'), ('baz', 'S10')])
  >>> recordarr = np.rec.array(arr)
 
-The :mod:`numpy.rec` module provides a number of other convenience functions for
+The ``numpy.rec`` module provides a number of other convenience functions for
 creating record arrays, see :ref:`record array creation routines
 <routines.array-creation.rec>`.
 
diff --git a/numpy/lib/recfunctions.py b/numpy/lib/recfunctions.py
index a491f612e..ee4fbcd74 100644
--- a/numpy/lib/recfunctions.py
+++ b/numpy/lib/recfunctions.py
@@ -784,7 +784,8 @@ def repack_fields(a, align=False, recurse=False):
 
     This method removes any overlaps and reorders the fields in memory so they
     have increasing byte offsets, and adds or removes padding bytes depending
-    on the `align` option, which behaves like the `align` option to `np.dtype`.
+    on the `align` option, which behaves like the `align` option to
+    `numpy.dtype`.
 
     If `align=False`, this method produces a "packed" memory layout in which
     each field starts at the byte the previous field ended, and any padding
@@ -917,11 +918,12 @@ def structured_to_unstructured(arr, dtype=None, copy=False, casting='unsafe'):
     dtype : dtype, optional
        The dtype of the output unstructured array.
     copy : bool, optional
-        See copy argument to `ndarray.astype`. If true, always return a copy.
-        If false, and `dtype` requirements are satisfied, a view is returned.
+        See copy argument to `numpy.ndarray.astype`. If true, always return a
+        copy. If false, and `dtype` requirements are satisfied, a view is
+        returned.
     casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
-        See casting argument of `ndarray.astype`. Controls what kind of data
-        casting may occur.
+        See casting argument of `numpy.ndarray.astype`. Controls what kind of
+        data casting may occur.
 
     Returns
     -------
@@ -1020,11 +1022,12 @@ def unstructured_to_structured(arr, dtype=None, names=None, align=False,
     align : boolean, optional
        Whether to create an aligned memory layout.
     copy : bool, optional
-        See copy argument to `ndarray.astype`. If true, always return a copy.
-        If false, and `dtype` requirements are satisfied, a view is returned.
+        See copy argument to `numpy.ndarray.astype`. If true, always return a
+        copy. If false, and `dtype` requirements are satisfied, a view is
+        returned.
     casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
-        See casting argument of `ndarray.astype`. Controls what kind of data
-        casting may occur.
+        See casting argument of `numpy.ndarray.astype`. Controls what kind of
+        data casting may occur.
 
     Returns
     -------