DOC: BLD: fix lots of Sphinx warnings/errors.

3 files changed, 40 insertions, 39 deletions

diff --git a/numpy/doc/basics.py b/numpy/doc/basics.py
index dac236644..083d55a84 100644
--- a/numpy/doc/basics.py
+++ b/numpy/doc/basics.py
@@ -9,36 +9,36 @@ Array types and conversions between types
 NumPy supports a much greater variety of numerical types than Python does.
 This section shows which are available, and how to modify an array's data-type.
 
-==========  ==========================================================
-Data type   Description
-==========  ==========================================================
-bool_       Boolean (True or False) stored as a byte
-int_        Default integer type (same as C ``long``; normally either
-            ``int64`` or ``int32``)
-intc        Identical to C ``int`` (normally ``int32`` or ``int64``)
-intp        Integer used for indexing (same as C ``ssize_t``; normally
-            either ``int32`` or ``int64``)
-int8        Byte (-128 to 127)
-int16       Integer (-32768 to 32767)
-int32       Integer (-2147483648 to 2147483647)
-int64       Integer (-9223372036854775808 to 9223372036854775807)
-uint8       Unsigned integer (0 to 255)
-uint16      Unsigned integer (0 to 65535)
-uint32      Unsigned integer (0 to 4294967295)
-uint64      Unsigned integer (0 to 18446744073709551615)
-float_      Shorthand for ``float64``.
-float16     Half precision float: sign bit, 5 bits exponent,
-            10 bits mantissa
-float32     Single precision float: sign bit, 8 bits exponent,
-            23 bits mantissa
-float64     Double precision float: sign bit, 11 bits exponent,
-            52 bits mantissa
-complex_    Shorthand for ``complex128``.
-complex64   Complex number, represented by two 32-bit floats (real
-            and imaginary components)
-complex128  Complex number, represented by two 64-bit floats (real
-            and imaginary components)
-==========  ==========================================================
+============ ==========================================================
+Data type    Description
+============ ==========================================================
+``bool_``    Boolean (True or False) stored as a byte
+``int_``     Default integer type (same as C ``long``; normally either
+             ``int64`` or ``int32``)
+intc         Identical to C ``int`` (normally ``int32`` or ``int64``)
+intp         Integer used for indexing (same as C ``ssize_t``; normally
+             either ``int32`` or ``int64``)
+int8         Byte (-128 to 127)
+int16        Integer (-32768 to 32767)
+int32        Integer (-2147483648 to 2147483647)
+int64        Integer (-9223372036854775808 to 9223372036854775807)
+uint8        Unsigned integer (0 to 255)
+uint16       Unsigned integer (0 to 65535)
+uint32       Unsigned integer (0 to 4294967295)
+uint64       Unsigned integer (0 to 18446744073709551615)
+``float_``   Shorthand for ``float64``.
+float16      Half precision float: sign bit, 5 bits exponent,
+             10 bits mantissa
+float32      Single precision float: sign bit, 8 bits exponent,
+             23 bits mantissa
+float64      Double precision float: sign bit, 11 bits exponent,
+             52 bits mantissa
+``complex_`` Shorthand for ``complex128``.
+complex64    Complex number, represented by two 32-bit floats (real
+             and imaginary components)
+complex128   Complex number, represented by two 64-bit floats (real
+             and imaginary components)
+============ ==========================================================
 
 Additionally to ``intc`` the platform dependent C integer types ``short``,
 ``long``, ``longlong`` and their unsigned versions are defined.
diff --git a/numpy/doc/glossary.py b/numpy/doc/glossary.py
index 97b7b3362..794c393f6 100644
--- a/numpy/doc/glossary.py
+++ b/numpy/doc/glossary.py
@@ -48,7 +48,7 @@ Glossary
          array([(1, 2.0), (3, 4.0)],
                dtype=[('x', '<i4'), ('y', '<f8')])
 
-       Fast element-wise operations, called `ufuncs`_, operate on arrays.
+       Fast element-wise operations, called :term:`ufuncs`, operate on arrays.
 
    array_like
        Any sequence that can be interpreted as an ndarray.  This includes
@@ -82,7 +82,7 @@ Glossary
          array([[4, 5],
                 [5, 6]])
 
-       See `doc.broadcasting`_ for more information.
+       See `numpy.doc.broadcasting` for more information.
 
    C order
        See `row-major`
@@ -155,7 +155,8 @@ Glossary
        See `column-major`
 
    flattened
-       Collapsed to a one-dimensional array. See `ndarray.flatten`_ for details.
+       Collapsed to a one-dimensional array. See `numpy.ndarray.flatten`
+       for details.
 
    immutable
        An object that cannot be modified after execution is called
@@ -284,9 +285,9 @@ Glossary
        See *array*.
 
    record array
-       An `ndarray`_ with `structured data type`_ which has been subclassed as
-       np.recarray and whose dtype is of type np.record, making the
-       fields of its data type to be accessible by attribute.
+       An :term:`ndarray` with :term:`structured data type`_ which has been
+       subclassed as ``np.recarray`` and whose dtype is of type ``np.record``,
+       making the fields of its data type to be accessible by attribute.
 
    reference
        If ``a`` is a reference to ``b``, then ``(a is b) == True``.  Therefore,
@@ -348,10 +349,10 @@ Glossary
 
          >>> x[:, 1]
          array([2, 4])
-   
+
    structured data type
        A data type composed of other datatypes
-   
+
    tuple
        A sequence that may contain a variable number of types of any
        kind.  A tuple is immutable, i.e., once constructed it cannot be
diff --git a/numpy/doc/subclassing.py b/numpy/doc/subclassing.py
index 51d9dc120..c34278868 100644
--- a/numpy/doc/subclassing.py
+++ b/numpy/doc/subclassing.py
@@ -543,7 +543,7 @@ will be called, but now it sees an ``ndarray`` as the other argument. Likely,
 it will know how to handle this, and return a new instance of the ``B`` class
 to us. Our example class is not set up to handle this, but it might well be
 the best approach if, e.g., one were to re-implement ``MaskedArray`` using
- ``__array_ufunc__``.
+``__array_ufunc__``.
 
 As a final note: if the ``super`` route is suited to a given class, an
 advantage of using it is that it helps in constructing class hierarchies.