From 2f41bb26b061821c77aff6982630de937ad9007a Mon Sep 17 00:00:00 2001
From: mattip <matti.picus@gmail.com>
Date: Sun, 24 Feb 2019 10:10:47 +0200
Subject: DOC: fixes from review

---
 numpy/doc/glossary.py | 62 +++++++++++++++++++++++++++++----------------------
 1 file changed, 35 insertions(+), 27 deletions(-)

(limited to 'numpy/doc/glossary.py')

diff --git a/numpy/doc/glossary.py b/numpy/doc/glossary.py
index 162288b14..292f293b7 100644
--- a/numpy/doc/glossary.py
+++ b/numpy/doc/glossary.py
@@ -348,31 +348,31 @@ Glossary
          Painting the city red!
 
    slice
-       Used to select only certain elements from a sequence::
+       Used to select only certain elements from a sequence:
 
-         >>> x = range(5)
-         >>> x
-         [0, 1, 2, 3, 4]
+       >>> x = range(5)
+       >>> x
+       [0, 1, 2, 3, 4]
 
-         >>> x[1:3] # slice from 1 to 3 (excluding 3 itself)
-         [1, 2]
+       >>> x[1:3] # slice from 1 to 3 (excluding 3 itself)
+       [1, 2]
 
-         >>> x[1:5:2] # slice from 1 to 5, but skipping every second element
-         [1, 3]
+       >>> x[1:5:2] # slice from 1 to 5, but skipping every second element
+       [1, 3]
 
-         >>> x[::-1] # slice a sequence in reverse
-         [4, 3, 2, 1, 0]
+       >>> x[::-1] # slice a sequence in reverse
+       [4, 3, 2, 1, 0]
 
        Arrays may have more than one dimension, each which can be sliced
-       individually::
+       individually:
 
-         >>> x = np.array([[1, 2], [3, 4]])
-         >>> x
-         array([[1, 2],
-                [3, 4]])
+       >>> x = np.array([[1, 2], [3, 4]])
+       >>> x
+       array([[1, 2],
+              [3, 4]])
 
-         >>> x[:, 1]
-         array([2, 4])
+       >>> x[:, 1]
+       array([2, 4])
 
    structure
        See :term:`structured data type`
@@ -380,9 +380,14 @@ Glossary
    structured data type
        A data type composed of other datatypes
 
-   subarray
+   subarray data type
        A :term:`structured data type` may contain a :term:`ndarray` with its
-       own dtype and shape.
+       own dtype and shape:
+
+       >>> dt = np.dtype([('a', np.int32), ('b', np.float32, (3,))])
+       >>> np.zeros(3, dtype=dt)
+       array([(0, [0., 0., 0.]), (0, [0., 0., 0.]), (0, [0., 0., 0.])],
+             dtype=[('a', '<i4'), ('b', '<f4', (3,))])
 
    title
        In addition to field names, structured array fields may have an
@@ -425,14 +430,17 @@ Glossary
          'alpha'
 
    ufunc
-       Universal function.  A fast element-wise array operation.  Examples include
-       ``add``, ``sin`` and ``logical_or``.
-
-   vectorized
-       A loop-based function that operates on data with fixed strides.
-       Compilers know how to take advantage of well-constructed loops and
-       match the data to specialized hardware that can operate on a number
-       of operands in parallel.
+       Universal function.  A fast element-wise, :term:`vectorized
+       <vectorization>` array operation.  Examples include ``add``, ``sin`` and
+       ``logical_or``.
+
+   vectorization
+       Optimizing a looping block by specialized code. In a traditional send,
+       vectorization operates on data with fixed strides via specialized
+       hardware. Compilers know how to take advantage of well-constructed loops
+       and match the data to specialized hardware that can operate on a number
+       of operands in parallel. NumPy uses :ref:`vectorization
+       <whatis-vectorization>` to mean any optimization via specialized code. 
 
    view
        An array that does not own its data, but refers to another array's
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