path: root/doc/source/reference/arrays.classes.rst

.. _arrays.classes:

#########################
Standard array subclasses
#########################

.. currentmodule:: numpy

The :class:`ndarray` in NumPy is a "new-style" Python
built-in-type. Therefore, it can be inherited from (in Python or in C)
if desired. Therefore, it can form a foundation for many useful
classes. Often whether to sub-class the array object or to simply use
the core array component as an internal part of a new class is a
difficult decision, and can be simply a matter of choice. NumPy has
several tools for simplifying how your new object interacts with other
array objects, and so the choice may not be significant in the
end. One way to simplify the question is by asking yourself if the
object you are interested in can be replaced as a single array or does
it really require two or more arrays at its core.

Note that :func:`asarray` always returns the base-class ndarray. If
you are confident that your use of the array object can handle any
subclass of an ndarray, then :func:`asanyarray` can be used to allow
subclasses to propagate more cleanly through your subroutine. In
principal a subclass could redefine any aspect of the array and
therefore, under strict guidelines, :func:`asanyarray` would rarely be
useful. However, most subclasses of the array object will not
redefine certain aspects of the array object such as the buffer
interface, or the attributes of the array. One important example,
however, of why your subroutine may not be able to handle an arbitrary
subclass of an array is that matrices redefine the "*" operator to be
matrix-multiplication, rather than element-by-element multiplication.


Special attributes and methods
==============================

.. seealso:: :ref:`Subclassing ndarray <basics.subclassing>`

Numpy provides several hooks that classes can customize:

.. method:: class.__numpy_ufunc__(ufunc, method, i, inputs, **kwargs)

   .. versionadded:: 1.11

   Any class (ndarray subclass or not) can define this method to
   override behavior of Numpy's ufuncs. This works quite similarly to
   Python's ``__mul__`` and other binary operation routines.

   - *ufunc* is the ufunc object that was called.
   - *method* is a string indicating which Ufunc method was called
     (one of ``"__call__"``, ``"reduce"``, ``"reduceat"``,
     ``"accumulate"``, ``"outer"``, ``"inner"``).
   - *i* is the index of *self* in *inputs*.
   - *inputs* is a tuple of the input arguments to the ``ufunc``
   - *kwargs* is a dictionary containing the optional input arguments
     of the ufunc. The ``out`` argument is always contained in
     *kwargs*, if given. See the discussion in :ref:`ufuncs` for
     details.

   The method should return either the result of the operation, or
   :obj:`NotImplemented` if the operation requested is not
   implemented.

   If one of the arguments has a :func:`__numpy_ufunc__` method, it is
   executed *instead* of the ufunc.  If more than one of the input
   arguments implements :func:`__numpy_ufunc__`, they are tried in the
   order: subclasses before superclasses, otherwise left to right. The
   first routine returning something else than :obj:`NotImplemented`
   determines the result. If all of the :func:`__numpy_ufunc__`
   operations return :obj:`NotImplemented`, a :exc:`TypeError` is
   raised.

   If an :class:`ndarray` subclass defines the :func:`__numpy_ufunc__`
   method, this disables the :func:`__array_wrap__`,
   :func:`__array_prepare__`, :data:`__array_priority__` mechanism
   described below.

   .. note:: In addition to ufuncs, :func:`__numpy_ufunc__` also
      overrides the behavior of :func:`numpy.dot` even though it is
      not an Ufunc.

   .. note:: If you also define right-hand binary operator override
      methods (such as ``__rmul__``) or comparison operations (such as
      ``__gt__``) in your class, they take precedence over the
      :func:`__numpy_ufunc__` mechanism when resolving results of
      binary operations (such as ``ndarray_obj * your_obj``).

      The technical special case is: ``ndarray.__mul__`` returns
      ``NotImplemented`` if the other object is *not* a subclass of
      :class:`ndarray`, and defines both ``__numpy_ufunc__`` and
      ``__rmul__``. Similar exception applies for the other operations
      than multiplication.

      In such a case, when computing a binary operation such as
      ``ndarray_obj * your_obj``, your ``__numpy_ufunc__`` method
      *will not* be called.  Instead, the execution passes on to your
      right-hand ``__rmul__`` operation, as per standard Python
      operator override rules.

      Similar special case applies to *in-place operations*: If you
      define ``__rmul__``, then ``ndarray_obj *= your_obj`` *will not*
      call your ``__numpy_ufunc__`` implementation. Instead, the
      default Python behavior ``ndarray_obj = ndarray_obj * your_obj``
      occurs.

      Note that the above discussion applies only to Python's builtin
      binary operation mechanism. ``np.multiply(ndarray_obj,
      your_obj)`` always calls only your ``__numpy_ufunc__``, as
      expected.

.. method:: class.__array_finalize__(obj)

   This method is called whenever the system internally allocates a
   new array from *obj*, where *obj* is a subclass (subtype) of the
   :class:`ndarray`. It can be used to change attributes of *self*
   after construction (so as to ensure a 2-d matrix for example), or
   to update meta-information from the "parent." Subclasses inherit
   a default implementation of this method that does nothing.

.. method:: class.__array_prepare__(array, context=None)

   At the beginning of every :ref:`ufunc <ufuncs.output-type>`, this
   method is called on the input object with the highest array
   priority, or the output object if one was specified. The output
   array is passed in and whatever is returned is passed to the ufunc.
   Subclasses inherit a default implementation of this method which
   simply returns the output array unmodified. Subclasses may opt to
   use this method to transform the output array into an instance of
   the subclass and update metadata before returning the array to the
   ufunc for computation.

.. method:: class.__array_wrap__(array, context=None)

   At the end of every :ref:`ufunc <ufuncs.output-type>`, this method
   is called on the input object with the highest array priority, or
   the output object if one was specified. The ufunc-computed array
   is passed in and whatever is returned is passed to the user.
   Subclasses inherit a default implementation of this method, which
   transforms the array into a new instance of the object's class.
   Subclasses may opt to use this method to transform the output array
   into an instance of the subclass and update metadata before
   returning the array to the user.

.. data:: class.__array_priority__

   The value of this attribute is used to determine what type of
   object to return in situations where there is more than one
   possibility for the Python type of the returned object. Subclasses
   inherit a default value of 0.0 for this attribute.

.. method:: class.__array__([dtype])

   If a class (ndarray subclass or not) having the :func:`__array__`
   method is used as the output object of an :ref:`ufunc
   <ufuncs.output-type>`, results will be written to the object
   returned by :func:`__array__`. Similar conversion is done on
   input arrays.


Matrix objects
==============

.. index::
   single: matrix

:class:`matrix` objects inherit from the ndarray and therefore, they
have the same attributes and methods of ndarrays. There are six
important differences of matrix objects, however, that may lead to
unexpected results when you use matrices but expect them to act like
arrays:

1. Matrix objects can be created using a string notation to allow
   Matlab-style syntax where spaces separate columns and semicolons
   (';') separate rows.

2. Matrix objects are always two-dimensional. This has far-reaching
   implications, in that m.ravel() is still two-dimensional (with a 1
   in the first dimension) and item selection returns two-dimensional
   objects so that sequence behavior is fundamentally different than
   arrays.

3. Matrix objects over-ride multiplication to be
   matrix-multiplication. **Make sure you understand this for
   functions that you may want to receive matrices. Especially in
   light of the fact that asanyarray(m) returns a matrix when m is
   a matrix.**

4. Matrix objects over-ride power to be matrix raised to a power. The
   same warning about using power inside a function that uses
   asanyarray(...) to get an array object holds for this fact.

5. The default __array_priority\__ of matrix objects is 10.0, and
   therefore mixed operations with ndarrays always produce matrices.

6. Matrices have special attributes which make calculations easier.
   These are

   .. autosummary::
      :toctree: generated/

      matrix.T
      matrix.H
      matrix.I
      matrix.A

.. warning::

    Matrix objects over-ride multiplication, '*', and power, '**', to
    be matrix-multiplication and matrix power, respectively. If your
    subroutine can accept sub-classes and you do not convert to base-
    class arrays, then you must use the ufuncs multiply and power to
    be sure that you are performing the correct operation for all
    inputs.

The matrix class is a Python subclass of the ndarray and can be used
as a reference for how to construct your own subclass of the ndarray.
Matrices can be created from other matrices, strings, and anything
else that can be converted to an ``ndarray`` . The name "mat "is an
alias for "matrix "in NumPy.

.. autosummary::
   :toctree: generated/

   matrix
   asmatrix
   bmat

Example 1: Matrix creation from a string

>>> a=mat('1 2 3; 4 5 3')
>>> print (a*a.T).I
[[ 0.2924 -0.1345]
 [-0.1345  0.0819]]

Example 2: Matrix creation from nested sequence

>>> mat([[1,5,10],[1.0,3,4j]])
matrix([[  1.+0.j,   5.+0.j,  10.+0.j],
        [  1.+0.j,   3.+0.j,   0.+4.j]])

Example 3: Matrix creation from an array

>>> mat(random.rand(3,3)).T
matrix([[ 0.7699,  0.7922,  0.3294],
        [ 0.2792,  0.0101,  0.9219],
        [ 0.3398,  0.7571,  0.8197]])

Memory-mapped file arrays
=========================

.. index::
   single: memory maps

.. currentmodule:: numpy

Memory-mapped files are useful for reading and/or modifying small
segments of a large file with regular layout, without reading the
entire file into memory. A simple subclass of the ndarray uses a
memory-mapped file for the data buffer of the array. For small files,
the over-head of reading the entire file into memory is typically not
significant, however for large files using memory mapping can save
considerable resources.

Memory-mapped-file arrays have one additional method (besides those
they inherit from the ndarray): :meth:`.flush() <memmap.flush>` which
must be called manually by the user to ensure that any changes to the
array actually get written to disk.

.. autosummary::
   :toctree: generated/

   memmap
   memmap.flush

Example:

>>> a = memmap('newfile.dat', dtype=float, mode='w+', shape=1000)
>>> a[10] = 10.0
>>> a[30] = 30.0
>>> del a
>>> b = fromfile('newfile.dat', dtype=float)
>>> print b[10], b[30]
10.0 30.0
>>> a = memmap('newfile.dat', dtype=float)
>>> print a[10], a[30]
10.0 30.0


Character arrays (:mod:`numpy.char`)
====================================

.. seealso:: :ref:`routines.array-creation.char`

.. index::
   single: character arrays

.. note::
   The `chararray` class exists for backwards compatibility with
   Numarray, it is not recommended for new development. Starting from numpy
   1.4, if one needs arrays of strings, it is recommended to use arrays of
   `dtype` `object_`, `string_` or `unicode_`, and use the free functions
   in the `numpy.char` module for fast vectorized string operations.

These are enhanced arrays of either :class:`string_` type or
:class:`unicode_` type.  These arrays inherit from the
:class:`ndarray`, but specially-define the operations ``+``, ``*``,
and ``%`` on a (broadcasting) element-by-element basis.  These
operations are not available on the standard :class:`ndarray` of
character type. In addition, the :class:`chararray` has all of the
standard :class:`string <str>` (and :class:`unicode`) methods,
executing them on an element-by-element basis. Perhaps the easiest
way to create a chararray is to use :meth:`self.view(chararray)
<ndarray.view>` where *self* is an ndarray of str or unicode
data-type. However, a chararray can also be created using the
:meth:`numpy.chararray` constructor, or via the
:func:`numpy.char.array <core.defchararray.array>` function:

.. autosummary::
   :toctree: generated/

   chararray
   core.defchararray.array

Another difference with the standard ndarray of str data-type is
that the chararray inherits the feature introduced by Numarray that
white-space at the end of any element in the array will be ignored
on item retrieval and comparison operations.


.. _arrays.classes.rec:

Record arrays (:mod:`numpy.rec`)
================================

.. seealso:: :ref:`routines.array-creation.rec`, :ref:`routines.dtype`,
             :ref:`arrays.dtypes`.

Numpy provides the :class:`recarray` class which allows accessing the
fields of a structured array as attributes, and a corresponding
scalar data type object :class:`record`.

.. currentmodule:: numpy

.. autosummary::
   :toctree: generated/

   recarray
   record

Masked arrays (:mod:`numpy.ma`)
===============================

.. seealso:: :ref:`maskedarray`

Standard container class
========================

.. currentmodule:: numpy

For backward compatibility and as a standard "container "class, the
UserArray from Numeric has been brought over to NumPy and named
:class:`numpy.lib.user_array.container` The container class is a
Python class whose self.array attribute is an ndarray. Multiple
inheritance is probably easier with numpy.lib.user_array.container
than with the ndarray itself and so it is included by default. It is
not documented here beyond mentioning its existence because you are
encouraged to use the ndarray class directly if you can.

.. autosummary::
   :toctree: generated/

   numpy.lib.user_array.container

.. index::
   single: user_array
   single: container class


Array Iterators
===============

.. currentmodule:: numpy

.. index::
   single: array iterator

Iterators are a powerful concept for array processing. Essentially,
iterators implement a generalized for-loop. If *myiter* is an iterator
object, then the Python code::

    for val in myiter:
        ...
        some code involving val
        ...

calls ``val = myiter.next()`` repeatedly until :exc:`StopIteration` is
raised by the iterator. There are several ways to iterate over an
array that may be useful: default iteration, flat iteration, and
:math:`N`-dimensional enumeration.


Default iteration
-----------------

The default iterator of an ndarray object is the default Python
iterator of a sequence type. Thus, when the array object itself is
used as an iterator. The default behavior is equivalent to::

    for i in range(arr.shape[0]):
        val = arr[i]

This default iterator selects a sub-array of dimension :math:`N-1`
from the array. This can be a useful construct for defining recursive
algorithms. To loop over the entire array requires :math:`N` for-loops.

>>> a = arange(24).reshape(3,2,4)+10
>>> for val in a:
...     print 'item:', val
item: [[10 11 12 13]
 [14 15 16 17]]
item: [[18 19 20 21]
 [22 23 24 25]]
item: [[26 27 28 29]
 [30 31 32 33]]


Flat iteration
--------------

.. autosummary::
   :toctree: generated/

   ndarray.flat

As mentioned previously, the flat attribute of ndarray objects returns
an iterator that will cycle over the entire array in C-style
contiguous order.

>>> for i, val in enumerate(a.flat):
...     if i%5 == 0: print i, val
0 10
5 15
10 20
15 25
20 30

Here, I've used the built-in enumerate iterator to return the iterator
index as well as the value.


N-dimensional enumeration
-------------------------

.. autosummary::
   :toctree: generated/

   ndenumerate

Sometimes it may be useful to get the N-dimensional index while
iterating. The ndenumerate iterator can achieve this.

>>> for i, val in ndenumerate(a):
...     if sum(i)%5 == 0: print i, val
(0, 0, 0) 10
(1, 1, 3) 25
(2, 0, 3) 29
(2, 1, 2) 32


Iterator for broadcasting
-------------------------

.. autosummary::
   :toctree: generated/

   broadcast

The general concept of broadcasting is also available from Python
using the :class:`broadcast` iterator. This object takes :math:`N`
objects as inputs and returns an iterator that returns tuples
providing each of the input sequence elements in the broadcasted
result.

>>> for val in broadcast([[1,0],[2,3]],[0,1]):
...     print val
(1, 0)
(0, 1)
(2, 0)
(3, 1)