path: root/numpy/doc/subclassing.py

"""
=============================
Subclassing ndarray in python
=============================

Credits
-------

This page is based with thanks on the wiki page on subclassing by Pierre
Gerard-Marchant - http://www.scipy.org/Subclasses.

Introduction
------------

Subclassing ndarray is relatively simple, but it has some complications
compared to other Python objects.  On this page we explain the machinery
that allows you to subclass ndarray, and the implications for
implementing a subclass.

ndarrays and object creation
============================

Subclassing ndarray is complicated by the fact that new instances of
ndarray classes can come about in three different ways.  These are:

#. Explicit constructor call - as in ``MySubClass(params)``.  This is
   the usual route to Python instance creation.
#. View casting - casting an existing ndarray as a given subclass
#. Slicing an ndarray instance

The last two are particular features of ndarray, and the complications
of subclassing ndarray are due to the need to support these latter two
routes of instance creation.

.. _view-casting:

View casting
------------

*View casting* is the standard ndarray mechanism by which you take an
ndarray of any subclass, and return a view of the array as another
(specified) subclass:

>>> import numpy as np
>>> # create a completely useless ndarray subclass
>>> class C(np.ndarray): pass
>>> # create a standard ndarray
>>> arr = np.zeros((3,))
>>> # take a view of it, as our useless subclass
>>> c_arr = arr.view(C)
>>> type(c_arr)
<class 'C'>

.. _instance-slicing:

Array slicing
-------------

New instances of an ndarray subclass can also come about by taking
slices of subclassed arrays.  For example:

>>> v = c_arr[1:]
>>> type(v) # the view is of type 'C'
<class 'C'>
>>> v is c_arr # but it's a new instance
False

So, when we take a view from the ndarray, we return a new ndarray, that
points to the data in the original.  

Implications for subclassing
----------------------------

If we subclass ndarray, we need to make sure that :ref:`view-casting` or
:ref:`instance-slicing` of our subclassed instance returns another
instance of our own class.  Numpy has the machinery to do this, but it
is this view-creating machinery that makes subclassing slightly
non-standard.

There are two aspects to the machinery that ndarray uses to support
views and slices in subclasses.

The first is the use of the ``ndarray.__new__`` method for the main work
of object initialization, rather then the more usual ``__init__``
method.  The second is the use of the ``__array_finalize__`` method to
allow subclasses to clean up after the creation of views and slices.

A brief Python primer on ``__new__`` and ``__init__``
=====================================================

``__new__`` is a standard Python method, and, if present, is called
before ``__init__`` when we create a class instance. See the `python
__new__ documentation
<http://docs.python.org/reference/datamodel.html#object.__new__>`_ for more detail.  

For example, consider the following Python code:

.. testcode::

  class C(object):
      def __new__(cls, *args):
          print 'Cls in __new__:', cls
          print 'Args in __new__:', args
          return object.__new__(cls, *args)

      def __init__(self, *args):
          print 'type(self) in __init__:', type(self)
          print 'Args in __init__:', args

meaning that we get:

>>> c = C('hello')
Cls in __new__: <class 'C'>
Args in __new__: ('hello',)
type(self) in __init__: <class 'C'>
Args in __init__: ('hello',)

When we call ``C('hello')``, the ``__new__`` method gets its own class
as first argument, and the passed argument, which is the string
``'hello'``.  After python calls ``__new__``, it usually (see below)
calls our ``__init__`` method, with the output of ``__new__`` as the
first argument (now a class instance), and the passed arguments
following.

As you can see, the object can be initialized in the ``__new__``
method or the ``__init__`` method, or both, and in fact ndarray does
not have an ``__init__`` method, because all the initialization is
done in the ``__new__`` method.

Why use ``__new__`` rather than just the usual ``__init__``?  Because
in some cases, as for ndarray, we want to be able to return an object
of some other class.  Consider the following:

.. testcode::

  class D(C):
      def __new__(cls, *args):
          print 'D cls is:', cls
          print 'D args in __new__:', args
          return C.__new__(C, *args)

      def __init__(self, *args):
          # we never get here
          print 'In D __init__'

meaning that:

>>> obj = D('hello')
D cls is: <class 'D'>
D args in __new__: ('hello',)
Cls in __new__: <class 'C'>
Args in __new__: ('hello',)
>>> type(obj)
<class 'C'>

The definition of ``C`` is the same as before, but for ``D``, the
``__new__`` method returns an instance of class ``C`` rather than
``D``.  Note that the ``__init__`` method of ``D`` does not get
called.  In general, when the ``__new__`` method returns an object of
class other than the class in which it is defined, the ``__init__``
method of that class is not called.

This is how subclasses of the ndarray class are able to return views
that preserve the class type.  When taking a view, the standard
ndarray machinery creates the new ndarray object with something
like::

  obj = ndarray.__new__(subtype, shape, ...

where ``subdtype`` is the subclass.  Thus the returned view is of the
same class as the subclass, rather than being of class ``ndarray``.

That solves the problem of returning views of the same type, but now
we have a new problem.  The machinery of ndarray can set the class
this way, in its standard methods for taking views, but the ndarray
``__new__`` method knows nothing of what we have done in our own
``__new__`` method in order to set attributes, and so on.  (Aside -
why not call ``obj = subdtype.__new__(...`` then?  Because we may not
have a ``__new__`` method with the same call signature).

The role of ``__array_finalize__``
==================================

``__array_finalize__`` is the mechanism that numpy provides to allow
subclasses to handle the various ways that new instances get created.

Remember that subclass instances can come about in these three ways:

#. explicit constructor call (``obj = MySubClass(params)``).  This will
   call the usual sequence of ``MySubClass.__new__`` then (if it exists)
   ``MySubClass.__init__``.
#. :ref:`view-casting`
#. :ref:`instance-slicing`

Our ``MySubClass.__new__`` method only gets called in the case of the
explicit constructor call, so we can't rely on ``MySubClass.__new__`` or
``MySubClass.__init__`` to deal with the view casting or slicing.  It
turns out that ``MySubClass.__array_finalize__`` *does* get called for
all three methods of object creation, so this is where our object
creation housekeeping usually goes.

In fact ``MySubClass.__array_finalize__`` is called from
``ndarray.__new__``, when ``MySubClass`` is the first (class) argument
to ``ndarray.__new__``.  The reason ``ndarray.__new__(MySubClass,...)``
gets called is different for the three cases above.

* For the explicit constructor call, our subclass will need to create a
  new ndarray instance of its own class.  In practice this means that
  we, the authors of the code, will need to make a call to
  ``ndarray.__new__(MySubClass,...)``, or do view casting of an existing
  array (see below)
* For view casting, ``ndarray.view``, when casting, does an explicit
  call to ``ndarray.__new__(MySubClass,...)``
* For slicing, ``ndarray.__new__(MySubClass,...)`` is called from the
  numpy C ``array_slice`` function

The following code allows us to look at the call sequences:

.. testcode::

   import numpy as np

   class C(np.ndarray):
       def __new__(cls, *args, **kwargs):
           print 'In __new__ with class %s' % cls
           return np.ndarray.__new__(cls, *args, **kwargs)

       def __init__(self, *args, **kwargs):
           # in practice you probably will not need or want an __init__
           # method for your subclass
           print 'In __init__ with class %s' % self.__class__

       def __array_finalize__(self, obj):
           print 'In array_finalize:'
           print '   self type is %s' % type(self)
           print '   obj type is %s' % type(obj)


Now:

>>> # Explicit constructor
>>> c = C((10,))
In __new__ with class <class 'C'>
In array_finalize:
   self type is <class 'C'>
   obj type is <type 'NoneType'>
In __init__ with class <class 'C'>
>>> # View casting
>>> a = np.arange(10)
>>> cast_a = a.view(C)
In array_finalize:
   self type is <class 'C'>
   obj type is <type 'numpy.ndarray'>
>>> # Slicing
>>> cv = c[:1]
In array_finalize:
   self type is <class 'C'>
   obj type is <class 'C'>

The signature of ``__array_finalize__`` is::

    def __array_finalize__(self, obj):

``ndarray.__new__`` passes ``__array_finalize__`` the new object, of our
own class (``self``) as well as the object from which the view has been
taken (``obj``).  As you can see from the output above, ``obj`` is
``None`` when calling from the subclass explicit constructor.  

We can use ``__array_finalize__`` to take attributes from the old object
``obj``, and put them into the new view object, or do any other related
processing.  Because ``__array_finalize__`` is the only method that
always sees new instances being created, it is the sensible place to
fill in instance defaults.

This may be clearer with an example.

Simple example - adding an extra attribute to ndarray
-----------------------------------------------------

.. testcode::

  import numpy as np

  class InfoArray(np.ndarray):

      def __new__(subtype, shape, dtype=float, buffer=None, offset=0,
            strides=None, order=None, info=None):
          # Create the ndarray instance of our type, given the usual
          # input arguments.  This will call the standard ndarray
          # constructor, but return an object of our type
          obj = np.ndarray.__new__(subtype, shape, dtype, buffer, offset, strides,
                           order)
          # add the new attribute to the created instance
          obj.info = info
          # Finally, we must return the newly created object:
          return obj

      def __array_finalize__(self,obj):
          # We could have reached here in 3 ways:
          # From explicit constructor - e.g. InfoArray():
          #    self.info set, obj is None)
          # From view casting - e.g arr.view(InfoArray):
          #    self.info not set, obj is arr
          #    (self.info can be set if type(arr) is InfoArray)
          # From slicing - e.g infoarr[:3]
          #    self.info not set, obj.info set
          self.info = getattr(obj, 'info', None)
          # We do not need to return anything


Using the object looks like this:

  >>> obj = InfoArray(shape=(3,)) # explicit constructor
  >>> type(obj)
  <class 'InfoArray'>
  >>> obj.info is None
  True  
  >>> obj = InfoArray(shape=(3,), info='information')
  >>> obj.info
  'information'
  >>> v = obj[1:] # slicing
  >>> type(v)
  <class 'InfoArray'>
  >>> v.info
  'information'
  >>> arr = np.arange(10) 
  >>> cast_arr = arr.view(InfoArray) # view casting 
  >>> type(cast_arr)
  <class 'InfoArray'>
  >>> cast_arr.info is None
  True

This class isn't very useful, because it has the same constructor as the
bare ndarray object, including passing in buffers and shapes and so on.
We would probably prefer the constructor to be able to take an already
formed ndarray from the usual numpy calls to ``np.array`` and return an
object.

Slightly more realistic example - attribute added to existing array
-------------------------------------------------------------------

Here is a class that takes a standard ndarray that already exists, casts
as our type, and adds an extra attribute.  The ``__array_finalize__``
method is the same as InfoArray above, but the ``__new__`` method is
different.

.. testcode::

  import numpy as np

  class RealisticInfoArray(np.ndarray):

      def __new__(cls, input_array, info=None):
          # Input array is an already formed ndarray instance
          # We first cast to be our class type
          obj = np.asarray(input_array).view(cls)
          # add the new attribute to the created instance
          obj.info = info
          # Finally, we must return the newly created object:
          return obj

      def __array_finalize__(self,obj):
          # We could have reached here in 3 ways:
          # From explicit constructor - e.g. RealisticInfoArray():
          #    self.info set, obj is None)
          # From view casting - e.g arr.view(RealisticInfoArray):
          #    self.info not set, obj is arr
          #    (self.info can be set if type(arr) is RealisticInfoArray)
          # From slicing - e.g infoarr[:3]
          #    self.info not set, obj.info set
          self.info = getattr(obj, 'info', None)
          # We do not need to return anything


So:

  >>> arr = np.arange(5)
  >>> obj = RealisticInfoArray(arr, info='information')
  >>> type(obj)
  <class 'RealisticInfoArray'>
  >>> obj.info
  'information'
  >>> v = obj[1:]
  >>> type(v)
  <class 'RealisticInfoArray'>
  >>> v.info
  'information'


``__array_wrap__`` for ufuncs
-----------------------------

Let's say you have an instance ``obj`` of your new subclass,
``RealisticInfoArray``, and you pass it into a ufunc with another
array:

.. testcode::

  arr = np.arange(5)
  ret = np.multiply.outer(arr, obj)

When a numpy ufunc is called on a subclass of ndarray, the
``__array_wrap__`` method is called to transform the result into a new
instance of the subclass. By default, ``__array_wrap__`` will call
``__array_finalize__``, and the attributes will be inherited.

By defining a specific ``__array_wrap__`` method for our subclass, we
can tweak the output. The ``__array_wrap__`` method requires one
argument, the object on which the ufunc is applied, and an optional
parameter *context*. This parameter is returned by some ufuncs as a
3-element tuple: (name of the ufunc, argument of the ufunc, domain of
the ufunc). See the masked array subclass for an implementation.

Extra gotchas - custom ``__del__`` methods and ndarray.base
-----------------------------------------------------------

One of the problems that ndarray solves is keeping track of memory
ownership of ndarrays and their views.  Consider the case where we have
created an ndarray, ``arr`` and have taken a slice with ``v = arr[1:]``.
The two objects are looking at the same memory.  Numpy keeps track of
where the data came from for a particular array or view, with the
``base`` attribute:

>>> # A normal ndarray, that owns its own data
>>> arr = np.zeros((4,))
>>> # In this case, base is None
>>> arr.base is None
True
>>> # We take a view
>>> v1 = arr[1:]
>>> # base now points to the array that it derived from
>>> v1.base is arr
True
>>> # Take a view of a view
>>> v2 = v1[1:]
>>> # base points to the view it derived from
>>> v2.base is v1
True

In general, if the array owns its own memory, as for ``arr`` in this
case, then ``arr.base`` will be None - there are some exceptions to this
- see the numpy book for more details.

The ``base`` attribute is useful in being able to tell whether we have
a view or the original array.  This in turn can be useful if we need
to know whether or not to do some specific cleanup when the subclassed
array is deleted.  For example, we may only want to do the cleanup if
the original array is deleted, but not the views.  For an example of
how this can work, have a look at the ``memmap`` class in
``numpy.core``.

.. _Python __new__:

"""