Most of these fixes involve putting blank lines around

.. versionadded:: x.x.x

and

.. deprecated:: x.x.x

Some of the examples were also fixed.

Update docs for boolean array indexing and nonzero order.
Add links to row-major and column-major terms where they appear.

Closes #3177

Viewing an ndarray as a np.recarray now automatically converts
the dtype to np.record.

This commit also fixes assignment to MaskedArray's dtype attribute,
fixes the repr of recarrays with non-structured dtype, and removes
recarray.view so that viewing a recarray as a non-structured dtype
no longer converts to ndarray type.

Fixes #3581

In https://github.com/numpy/numpy/pull/5483, I solved the problem that a
"recarray" and a "record array" (nomenclature defined in
https://github.com/numpy/numpy/pull/5482) looked identical by making
sure that a type's subclass was listed in the repr. However, recarrays
are still represented using the function 'rec.array' even though this
function technically creates record arrays, not recarrays.

So I have updated recarray.__repr__.

Setup:

    >>> a = np.array([(1,'ABC'), (2, "DEF")], dtype=[('foo', int), ('bar', 'S4')])
    >>> recordarr = np.rec.array(a)
    >>> recarr = a.view(np.recarray)

Behavior after https://github.com/numpy/numpy/pull/5483:

    >>> recordarr
    rec.array([(1, 'ABC'), (2, 'DEF')],
          dtype=(numpy.record, [('foo', '<i8'), ('bar', 'S4')]))
    >>> recarr
    rec.array([(1, 'ABC'), (2, 'DEF')],
          dtype=[('foo', '<i8'), ('bar', 'S4')])

New Behavior:

    >>> recordarr
    rec.array([(1, 'ABC'), (2, 'DEF')],
          dtype=[('foo', '<i8'), ('bar', '|S4')])
    >>> recarr
    array([(1, 'ABC'), (2, 'DEF')],
          dtype=[('foo', '<i8'), ('bar', 'S4')]).view(numpy.recarray)

This commit makes changes to `__getitem__` and `__getattr__` of recarrays:

 1. recarrays no longer convert string ndarrays to chararrays, and
    instead simply return ndarrays of string type.
 2. attribute access and index access of fields now behaves identically
 3. dtype.type is now inherited when fields of structured type are accessed

Demonstration:

    >>> rec = np.rec.array([('abc ', (1,1), 1), ('abc', (2,3), 1)],
    ...       dtype=[('foo', 'S4'), ('bar', [('A', int), ('B', int)]), ('baz', int)])

Old Behavior:

    >>> type(rec.foo), type(rec['foo'])
    (numpy.core.defchararray.chararray, numpy.recarray)
    >>> type(rec.bar), type(rec['bar']), rec.bar.dtype.type
    (numpy.recarray, numpy.recarray, numpy.void)
    >>> type(rec.baz), type(rec['baz'])
    (numpy.ndarray, numpy.ndarray)

New behavior:

    >>> type(rec.foo), type(rec['foo'])
    (numpy.ndarray, numpy.ndarray)
    >>> type(rec.bar), type(rec['bar']), rec.bar.dtype.type
    (numpy.recarray, numpy.recarray, numpy.record)
    >>> type(rec.baz), type(rec['baz'])
    (numpy.ndarray, numpy.ndarray)

This update adds a section better describing record arrays in the user
guide (numpy/doc/structured_arrays.py).

It also corrects nomenclature, such that "structured array" refers to
ndarrays with structured dtype, "record array" refers to modified
ndarrays as created by np.rec.array, and "recarray" refers to ndarrays
viewed as np.recarray. See the note at the end of the structured
array user guide.

Add a link to f2py docs, which was missing.

[ci skip]