.. _array-array:

==========================
Working with Python arrays
==========================

.. include::
    ../two-syntax-variants-used

.. caution::

    The developers of CPython are keen to emphasize that the implementation of
    the ``array.array`` type is an *internal* structure and can change at any time,
    potentially even between patch releases of Python. If you use it
    through the ``cpython.array`` cimport then you are accepting the risk
    of that happening and will not complain to either them or us (the
    developers of Cython). It is exposed mostly for historical reasons
    from a time when there was no other way to provide fast element access.

    The compatible ways to use this type are to use it through the Python
    interface without specifying a type to Cython at all, and to use
    :ref:`typed memoryviews<memoryviews>` for fast element access.

Python has a builtin array module supporting dynamic 1-dimensional arrays of
primitive types. It is possible to access the underlying C array of a Python
array from within Cython. At the same time they are ordinary Python objects
which can be stored in lists and serialized between processes when using
:obj:`multiprocessing`.

Compared to the manual approach with :c:func:`malloc` and :c:func:`free`, this
gives the safe and automatic memory management of Python, and compared to a
Numpy array there is no need to install a dependency, as the :obj:`array`
module is built into both Python and Cython.

Safe usage with memory views
----------------------------


.. tabs::
    .. group-tab:: Pure Python

        .. literalinclude:: ../../examples/tutorial/array/safe_usage.py

    .. group-tab:: Cython

        .. literalinclude:: ../../examples/tutorial/array/safe_usage.pyx


NB: the import brings the regular Python array object into the namespace
while the cimport adds functions accessible from Cython.

A Python array is constructed with a type signature and sequence of
initial values. For the possible type signatures, refer to the Python
documentation for the `array module <https://docs.python.org/library/array.html>`_.

Notice that when a Python array is assigned to a variable typed as
memory view, there will be a slight overhead to construct the memory
view. However, from that point on the variable can be passed to other
functions without overhead, so long as it is typed:


.. tabs::
    .. group-tab:: Pure Python

        .. literalinclude:: ../../examples/tutorial/array/overhead.py

    .. group-tab:: Cython

        .. literalinclude:: ../../examples/tutorial/array/overhead.pyx


Zero-overhead, unsafe access to raw C pointer
---------------------------------------------
To avoid any overhead and to be able to pass a C pointer to other
functions, it is possible to access the underlying contiguous array as a
pointer. There is no type or bounds checking, so be careful to use the
right type and signedness.


.. tabs::
    .. group-tab:: Pure Python

        .. literalinclude:: ../../examples/tutorial/array/unsafe_usage.py

    .. group-tab:: Cython

        .. literalinclude:: ../../examples/tutorial/array/unsafe_usage.pyx


Note that any length-changing operation on the array object may invalidate the
pointer.


Cloning, extending arrays
-------------------------
To avoid having to use the array constructor from the Python module,
it is possible to create a new array with the same type as a template,
and preallocate a given number of elements. The array is initialized to
zero when requested.


.. tabs::
    .. group-tab:: Pure Python

        .. literalinclude:: ../../examples/tutorial/array/clone.py

    .. group-tab:: Cython

        .. literalinclude:: ../../examples/tutorial/array/clone.pyx


An array can also be extended and resized; this avoids repeated memory
reallocation which would occur if elements would be appended or removed
one by one.


.. tabs::
    .. group-tab:: Pure Python

        .. literalinclude:: ../../examples/tutorial/array/resize.py

    .. group-tab:: Cython

        .. literalinclude:: ../../examples/tutorial/array/resize.pyx


API reference
-------------

Data fields
~~~~~~~~~~~

::

    data.as_voidptr
    data.as_chars
    data.as_schars
    data.as_uchars
    data.as_shorts
    data.as_ushorts
    data.as_ints
    data.as_uints
    data.as_longs
    data.as_ulongs
    data.as_longlongs  # requires Python >=3
    data.as_ulonglongs  # requires Python >=3
    data.as_floats
    data.as_doubles
    data.as_pyunicodes

Direct access to the underlying contiguous C array, with given type;
e.g., ``myarray.data.as_ints``.


Functions
~~~~~~~~~
The following functions are available to Cython from the array module

.. tabs::
    .. group-tab:: Pure Python

        .. code-block:: python

            @cython.cfunc
            @cython.exceptval(-1)
            def resize(self: array.array, n: cython.Py_ssize_t) -> cython.int

    .. group-tab:: Cython

        .. code-block:: cython

            cdef int resize(array.array self, Py_ssize_t n) except -1

Fast resize / realloc. Not suitable for repeated, small increments; resizes
underlying array to exactly the requested amount.

----

.. tabs::
    .. group-tab:: Pure Python

        .. code-block:: python

            @cython.cfunc
            @cython.exceptval(-1)
            def resize_smart(self: array.array, n: cython.Py_ssize_t) -> cython.int

    .. group-tab:: Cython

        .. code-block:: cython

            cdef int resize_smart(array.array self, Py_ssize_t n) except -1

Efficient for small increments; uses growth pattern that delivers
amortized linear-time appends.

----

.. tabs::
    .. group-tab:: Pure Python

        .. code-block:: python

            @cython.cfunc
            @cython.inline
            def clone(template: array.array, length: cython.Py_ssize_t, zero: cython.bint) -> array.array

    .. group-tab:: Cython

        .. code-block:: cython

            cdef inline array.array clone(array.array template, Py_ssize_t length, bint zero)


Fast creation of a new array, given a template array. Type will be same as
``template``. If zero is ``True``, new array will be initialized with zeroes.

----

.. tabs::
    .. group-tab:: Pure Python

        .. code-block:: python

            @cython.cfunc
            @cython.inline
            def copy(self: array.array) -> array.array

    .. group-tab:: Cython

        .. code-block:: cython

            cdef inline array.array copy(array.array self)

Make a copy of an array.

----

.. tabs::
    .. group-tab:: Pure Python

        .. code-block:: python

            @cython.cfunc
            @cython.inline
            @cython.exceptval(-1)
            def extend_buffer(self: array.array, stuff: cython.p_char, n: cython.Py_ssize_t) -> cython.int

    .. group-tab:: Cython

        .. code-block:: cython

            cdef inline int extend_buffer(array.array self, char* stuff, Py_ssize_t n) except -1

Efficient appending of new data of same type (e.g. of same array type)
``n``: number of elements (not number of bytes!)

----

.. tabs::
    .. group-tab:: Pure Python

        .. code-block:: python

            @cython.cfunc
            @cython.inline
            @cython.exceptval(-1)
            def extend(self: array.array, other: array.array) -> cython.int

    .. group-tab:: Cython

        .. code-block:: cython

            cdef inline int extend(array.array self, array.array other) except -1

Extend array with data from another array; types must match.

----

.. tabs::
    .. group-tab:: Pure Python

        .. code-block:: python

            @cython.cfunc
            @cython.inline
            def zero(self: array.array) -> cython.void

    .. group-tab:: Cython

        .. code-block:: cython

            cdef inline void zero(array.array self)

Set all elements of array to zero.
