Added links to chapters

Author: rougier

06-custom-vectorization.rst

@@ -10,21 +10,43 @@ Custom vectorization
 Introduction
 ------------
 
-.. One of the strength of Numpy is that it allows you to write your own array class. The mechanism is a bit tedious but it is worth the effort.
+One of the strength of Numpy is that it allows you to `subclass the ndarray
+<https://docs.scipy.org/doc/numpy/user/basics.subclassing.html>`_ object.  The
+process is a bit tedious but it is worth the effort because it allows you to
+create new objects that suit perfectly your problem. We'll examine in the
+following section two real-word cases (typed list and memory aware) that are
+extensively used in the `glumpy <http://glumpy.github.io>`_ project while the
+last one (double precision array) is a more academic case.
 
 
 Typed list
 ----------
 
+Typed list (also known as ragged array) is a list of items that all have the
+same data type (in the sense of numpy). They offer both the list and ndarray
+API (with some restriction of course). Since respective API may be not
+compatible in some cases, we have to make some choices:
+
+.. code:: python
+
+   >>> l = TypedList(int)
+   >>> l.append([1,2])
+   >>> l.append([3])
+   >>> print(l)
+   [1, 2], [3]
+   >>> print(l+1)
+   [2, 3], [4]
+
+For the `+` operator, we'll choose to use Numpy API where the value is added to
+each individual item instead of expanding the list by appending a new item
+(`1`).
+   
+
 ..
    We would like to define a typed list object such that if offers both the Python
    list API and the Numpy array API (with some restriction of course). We would
    like for example to be able to write:
 
-   .. code:: python
-
-      >>> l = TypedList(int)
-      >>> l.append(1)
 
    We first need to subclass the ndarray object and define a new init method.
 

README.md

@@ -4,4 +4,13 @@ This work is licensed under a
 [Creative Commons Attribution 4.0 International License]
 (http://creativecommons.org/licenses/by/4.0/).
 
-**Work in progress. No due date yet.**
+* [Preface](preface.rst)
+* [Introduction](introduction.rst)
+* [Anatomy of an array](anatomy.rst)
+* [Code vectorization](code-vectorization.rst)
+* [Problem vectorization](problem-vectorization.rst)
+* [Custom vectorization](custom-vectorization.rst)
+* [Beyond Numpy](beyond-numpy.rst)
+* [Conclusion](conclusion.rst)
+* [Quick references](quick-reference.rst)
+* [Bibliography](bibliography.rst)