Glossary
========

.. glossary::

   Extension type
      "Extension type" can refer to either a Cython class defined with ``cdef class`` or more generally to any Python type that is ultimately implemented as a native C struct (including the built-in types like `int` or `dict`).
      
   Dynamic allocation or Heap allocation
      A C variable allocated with ``malloc`` (in C) or ``new`` (in C++) is
      `allocated dynamically/heap allocated <https://en.wikipedia.org/wiki/C_dynamic_memory_allocation>`_.
      Its lifetime is until the user deletes it explicitly (with ``free`` in C or ``del`` in C++).
      This can happen in a different function than the allocation.

   pointer
      A **pointer** is a variable that stores the address of another variable
      (i.e. direct address of the memory location). They allow for
      dynamic memory allocation and deallocation. They can be used to build
      dynamic data structures. `Read more <https://en.wikipedia.org/wiki/Pointer_(computer_programming)#C_pointers>`__.
      
   Python object
      When using Python, the contents of every variable is a Python object
      (including Cython extension types). Key features of Python objects are that
      they are passed *by reference* and that their lifetime is *managed* automatically
      so that they are destroyed when no more references exist to them.
      In Cython, they are distinct from C types, which are passed *by value* and whose
      lifetime is managed depending on whether they are allocated on the stack or heap.
      To explicitly declare a Python object variable in Cython use ``cdef object abc``.
      Internally in C, they are referred to as ``PyObject*``.
      
   Stack allocation
      A C variable declared within a function as ``cdef SomeType a``
      is said to be allocated on the stack.
      It exists for the duration of the function only.
      
   Typed memoryview
      A useful Cython type for getting quick access to blocks of memory.
      A memoryview alone does not actually own any memory.
      However, it can be initialized with a Python object that supports the
      `buffer protocol`_ (typically "array" types, for example a Numpy array).
      The memoryview keeps a reference to that Python object alive
      and provides quick access to the memory without needing to go
      through the Python API of the object and its ``__getitem__``/``__setitem__`` methods.
      For more information, see :ref:`memoryviews`.

.. _buffer protocol: https://docs.python.org/3/c-api/buffer.html
