.. highlight:: c

.. _new-types-topics:

*****************************************
Defining Extension Types: Assorted Topics
*****************************************

.. _dnt-type-methods:

This section aims to give a quick fly-by on the various type methods you can
implement and what they do.

Here is the definition of :c:type:`PyTypeObject`, with some fields only used in
:ref:`debug builds <debug-build>` omitted:

.. literalinclude:: ../includes/typestruct.h


Now that's a *lot* of methods.  Don't worry too much though -- if you have
a type you want to define, the chances are very good that you will only
implement a handful of these.

As you probably expect by now, we're going to go over this and give more
information about the various handlers.  We won't go in the order they are
defined in the structure, because there is a lot of historical baggage that
impacts the ordering of the fields.  It's often easiest to find an example
that includes the fields you need and then change the values to suit your new
type. ::

   const char *tp_name; /* For printing */

The name of the type -- as mentioned in the previous chapter, this will appear in
various places, almost entirely for diagnostic purposes. Try to choose something
that will be helpful in such a situation! ::

   Py_ssize_t tp_basicsize, tp_itemsize; /* For allocation */

These fields tell the runtime how much memory to allocate when new objects of
this type are created.  Python has some built-in support for variable length
structures (think: strings, tuples) which is where the :c:member:`~PyTypeObject.tp_itemsize` field
comes in.  This will be dealt with later. ::

   const char *tp_doc;

Here you can put a string (or its address) that you want returned when the
Python script references ``obj.__doc__`` to retrieve the doc string.

Now we come to the basic type methods -- the ones most extension types will
implement.


Finalization and De-allocation
------------------------------

.. index::
   single: object; deallocation
   single: deallocation, object
   single: object; finalization
   single: finalization, of objects

::

   destructor tp_dealloc;

This function is called when the reference count of the instance of your type is
reduced to zero and the Python interpreter wants to reclaim it.  If your type
has memory to free or other clean-up to perform, you can put it here.  The
object itself needs to be freed here as well.  Here is an example of this
function::

   static void
   newdatatype_dealloc(PyObject *op)
   {
       newdatatypeobject *self = (newdatatypeobject *) op;
       free(self->obj_UnderlyingDatatypePtr);
       Py_TYPE(self)->tp_free(self);
   }

If your type supports garbage collection, the destructor should call
:c:func:`PyObject_GC_UnTrack` before clearing any member fields::

   static void
   newdatatype_dealloc(PyObject *op)
   {
       newdatatypeobject *self = (newdatatypeobject *) op;
       PyObject_GC_UnTrack(op);
       Py_CLEAR(self->other_obj);
       ...
       Py_TYPE(self)->tp_free(self);
   }

.. index::
   single: PyErr_Fetch (C function)
   single: PyErr_Restore (C function)

One important requirement of the deallocator function is that it leaves any
pending exceptions alone.  This is important since deallocators are frequently
called as the interpreter unwinds the Python stack; when the stack is unwound
due to an exception (rather than normal returns), nothing is done to protect the
deallocators from seeing that an exception has already been set.  Any actions
which a deallocator performs which may cause additional Python code to be
executed may detect that an exception has been set.  This can lead to misleading
errors from the interpreter.  The proper way to protect against this is to save
a pending exception before performing the unsafe action, and restoring it when
done.  This can be done using the :c:func:`PyErr_Fetch` and
:c:func:`PyErr_Restore` functions::

   static void
   my_dealloc(PyObject *obj)
   {
       MyObject *self = (MyObject *) obj;
       PyObject *cbresult;

       if (self->my_callback != NULL) {
           PyObject *err_type, *err_value, *err_traceback;

           /* This saves the current exception state */
           PyErr_Fetch(&err_type, &err_value, &err_traceback);

           cbresult = PyObject_CallNoArgs(self->my_callback);
           if (cbresult == NULL) {
              PyErr_WriteUnraisable(self->my_callback);
           }
           else {
               Py_DECREF(cbresult);
           }

           /* This restores the saved exception state */
           PyErr_Restore(err_type, err_value, err_traceback);

           Py_DECREF(self->my_callback);
       }
       Py_TYPE(self)->tp_free(self);
   }

.. note::
   There are limitations to what you can safely do in a deallocator function.
   First, if your type supports garbage collection (using :c:member:`~PyTypeObject.tp_traverse`
   and/or :c:member:`~PyTypeObject.tp_clear`), some of the object's members can have been
   cleared or finalized by the time :c:member:`~PyTypeObject.tp_dealloc` is called.  Second, in
   :c:member:`~PyTypeObject.tp_dealloc`, your object is in an unstable state: its reference
   count is equal to zero.  Any call to a non-trivial object or API (as in the
   example above) might end up calling :c:member:`~PyTypeObject.tp_dealloc` again, causing a
   double free and a crash.

   Starting with Python 3.4, it is recommended not to put any complex
   finalization code in :c:member:`~PyTypeObject.tp_dealloc`, and instead use the new
   :c:member:`~PyTypeObject.tp_finalize` type method.

   .. seealso::
      :pep:`442` explains the new finalization scheme.

.. index::
   single: string; object representation
   pair: built-in function; repr

Object Presentation
-------------------

In Python, there are two ways to generate a textual representation of an object:
the :func:`repr` function, and the :func:`str` function.  (The :func:`print`
function just calls :func:`str`.)  These handlers are both optional.

::

   reprfunc tp_repr;
   reprfunc tp_str;

The :c:member:`~PyTypeObject.tp_repr` handler should return a string object containing a
representation of the instance for which it is called.  Here is a simple
example::

   static PyObject *
   newdatatype_repr(PyObject *op)
   {
       newdatatypeobject *self = (newdatatypeobject *) op;
       return PyUnicode_FromFormat("Repr-ified_newdatatype{{size:%d}}",
                                   self->obj_UnderlyingDatatypePtr->size);
   }

If no :c:member:`~PyTypeObject.tp_repr` handler is specified, the interpreter will supply a
representation that uses the type's :c:member:`~PyTypeObject.tp_name` and a uniquely identifying
value for the object.

The :c:member:`~PyTypeObject.tp_str` handler is to :func:`str` what the :c:member:`~PyTypeObject.tp_repr` handler
described above is to :func:`repr`; that is, it is called when Python code calls
:func:`str` on an instance of your object.  Its implementation is very similar
to the :c:member:`~PyTypeObject.tp_repr` function, but the resulting string is intended for human
consumption.  If :c:member:`~PyTypeObject.tp_str` is not specified, the :c:member:`~PyTypeObject.tp_repr` handler is
used instead.

Here is a simple example::

   static PyObject *
   newdatatype_str(PyObject *op)
   {
       newdatatypeobject *self = (newdatatypeobject *) op;
       return PyUnicode_FromFormat("Stringified_newdatatype{{size:%d}}",
                                   self->obj_UnderlyingDatatypePtr->size);
   }



Attribute Management
--------------------

For every object which can support attributes, the corresponding type must
provide the functions that control how the attributes are resolved.  There needs
to be a function which can retrieve attributes (if any are defined), and another
to set attributes (if setting attributes is allowed).  Removing an attribute is
a special case, for which the new value passed to the handler is ``NULL``.

Python supports two pairs of attribute handlers; a type that supports attributes
only needs to implement the functions for one pair.  The difference is that one
pair takes the name of the attribute as a :c:expr:`char\*`, while the other
accepts a :c:expr:`PyObject*`.  Each type can use whichever pair makes more
sense for the implementation's convenience. ::

   getattrfunc  tp_getattr;        /* char * version */
   setattrfunc  tp_setattr;
   /* ... */
   getattrofunc tp_getattro;       /* PyObject * version */
   setattrofunc tp_setattro;

If accessing attributes of an object is always a simple operation (this will be
explained shortly), there are generic implementations which can be used to
provide the :c:expr:`PyObject*` version of the attribute management functions.
The actual need for type-specific attribute handlers almost completely
disappeared starting with Python 2.2, though there are many examples which have
not been updated to use some of the new generic mechanism that is available.


.. _generic-attribute-management:

Generic Attribute Management
^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Most extension types only use *simple* attributes.  So, what makes the
attributes simple?  There are only a couple of conditions that must be met:

#. The name of the attributes must be known when :c:func:`PyType_Ready` is
   called.

#. No special processing is needed to record that an attribute was looked up or
   set, nor do actions need to be taken based on the value.

Note that this list does not place any restrictions on the values of the
attributes, when the values are computed, or how relevant data is stored.

When :c:func:`PyType_Ready` is called, it uses three tables referenced by the
type object to create :term:`descriptor`\s which are placed in the dictionary of the
type object.  Each descriptor controls access to one attribute of the instance
object.  Each of the tables is optional; if all three are ``NULL``, instances of
the type will only have attributes that are inherited from their base type, and
should leave the :c:member:`~PyTypeObject.tp_getattro` and :c:member:`~PyTypeObject.tp_setattro` fields ``NULL`` as
well, allowing the base type to handle attributes.

The tables are declared as three fields of the type object::

   struct PyMethodDef *tp_methods;
   struct PyMemberDef *tp_members;
   struct PyGetSetDef *tp_getset;

If :c:member:`~PyTypeObject.tp_methods` is not ``NULL``, it must refer to an array of
:c:type:`PyMethodDef` structures.  Each entry in the table is an instance of this
structure::

   typedef struct PyMethodDef {
       const char  *ml_name;       /* method name */
       PyCFunction  ml_meth;       /* implementation function */
       int          ml_flags;      /* flags */
       const char  *ml_doc;        /* docstring */
   } PyMethodDef;

One entry should be defined for each method provided by the type; no entries are
needed for methods inherited from a base type.  One additional entry is needed
at the end; it is a sentinel that marks the end of the array.  The
:c:member:`~PyMethodDef.ml_name` field of the sentinel must be ``NULL``.

The second table is used to define attributes which map directly to data stored
in the instance.  A variety of primitive C types are supported, and access may
be read-only or read-write.  The structures in the table are defined as::

   typedef struct PyMemberDef {
       const char *name;
       int         type;
       int         offset;
       int         flags;
       const char *doc;
   } PyMemberDef;

For each entry in the table, a :term:`descriptor` will be constructed and added to the
type which will be able to extract a value from the instance structure.  The
:c:member:`~PyMemberDef.type` field should contain a type code like :c:macro:`Py_T_INT` or
:c:macro:`Py_T_DOUBLE`; the value will be used to determine how to
convert Python values to and from C values.  The :c:member:`~PyMemberDef.flags` field is used to
store flags which control how the attribute can be accessed: you can set it to
:c:macro:`Py_READONLY` to prevent Python code from setting it.

An interesting advantage of using the :c:member:`~PyTypeObject.tp_members` table to build
descriptors that are used at runtime is that any attribute defined this way can
have an associated doc string simply by providing the text in the table.  An
application can use the introspection API to retrieve the descriptor from the
class object, and get the doc string using its :attr:`~type.__doc__` attribute.

As with the :c:member:`~PyTypeObject.tp_methods` table, a sentinel entry with a :c:member:`~PyMethodDef.ml_name` value
of ``NULL`` is required.

.. XXX Descriptors need to be explained in more detail somewhere, but not here.

   Descriptor objects have two handler functions which correspond to the
   \member{tp_getattro} and \member{tp_setattro} handlers.  The
   \method{__get__()} handler is a function which is passed the descriptor,
   instance, and type objects, and returns the value of the attribute, or it
   returns \NULL{} and sets an exception.  The \method{__set__()} handler is
   passed the descriptor, instance, type, and new value;


Type-specific Attribute Management
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

For simplicity, only the :c:expr:`char\*` version will be demonstrated here; the
type of the name parameter is the only difference between the :c:expr:`char\*`
and :c:expr:`PyObject*` flavors of the interface. This example effectively does
the same thing as the generic example above, but does not use the generic
support added in Python 2.2.  It explains how the handler functions are
called, so that if you do need to extend their functionality, you'll understand
what needs to be done.

The :c:member:`~PyTypeObject.tp_getattr` handler is called when the object requires an attribute
look-up.  It is called in the same situations where the :meth:`~object.__getattr__`
method of a class would be called.

Here is an example::

   static PyObject *
   newdatatype_getattr(PyObject *op, char *name)
   {
       newdatatypeobject *self = (newdatatypeobject *) op;
       if (strcmp(name, "data") == 0) {
           return PyLong_FromLong(self->data);
       }

       PyErr_Format(PyExc_AttributeError,
                    "'%.100s' object has no attribute '%.400s'",
                    Py_TYPE(self)->tp_name, name);
       return NULL;
   }

The :c:member:`~PyTypeObject.tp_setattr` handler is called when the :meth:`~object.__setattr__` or
:meth:`~object.__delattr__` method of a class instance would be called.  When an
attribute should be deleted, the third parameter will be ``NULL``.  Here is an
example that simply raises an exception; if this were really all you wanted, the
:c:member:`~PyTypeObject.tp_setattr` handler should be set to ``NULL``. ::

   static int
   newdatatype_setattr(PyObject *op, char *name, PyObject *v)
   {
       PyErr_Format(PyExc_RuntimeError, "Read-only attribute: %s", name);
       return -1;
   }

Object Comparison
-----------------

::

   richcmpfunc tp_richcompare;

The :c:member:`~PyTypeObject.tp_richcompare` handler is called when comparisons are needed.  It is
analogous to the :ref:`rich comparison methods <richcmpfuncs>`, like
:meth:`!__lt__`, and also called by :c:func:`PyObject_RichCompare` and
:c:func:`PyObject_RichCompareBool`.

This function is called with two Python objects and the operator as arguments,
where the operator is one of ``Py_EQ``, ``Py_NE``, ``Py_LE``, ``Py_GE``,
``Py_LT`` or ``Py_GT``.  It should compare the two objects with respect to the
specified operator and return ``Py_True`` or ``Py_False`` if the comparison is
successful, ``Py_NotImplemented`` to indicate that comparison is not
implemented and the other object's comparison method should be tried, or ``NULL``
if an exception was set.

Here is a sample implementation, for a datatype that is considered equal if the
size of an internal pointer is equal::

   static PyObject *
   newdatatype_richcmp(PyObject *lhs, PyObject *rhs, int op)
   {
       newdatatypeobject *obj1 = (newdatatypeobject *) lhs;
       newdatatypeobject *obj2 = (newdatatypeobject *) rhs;
       PyObject *result;
       int c, size1, size2;

       /* code to make sure that both arguments are of type
          newdatatype omitted */

       size1 = obj1->obj_UnderlyingDatatypePtr->size;
       size2 = obj2->obj_UnderlyingDatatypePtr->size;

       switch (op) {
       case Py_LT: c = size1 <  size2; break;
       case Py_LE: c = size1 <= size2; break;
       case Py_EQ: c = size1 == size2; break;
       case Py_NE: c = size1 != size2; break;
       case Py_GT: c = size1 >  size2; break;
       case Py_GE: c = size1 >= size2; break;
       }
       result = c ? Py_True : Py_False;
       return Py_NewRef(result);
    }


Abstract Protocol Support
-------------------------

Python supports a variety of *abstract* 'protocols;' the specific interfaces
provided to use these interfaces are documented in :ref:`abstract`.


A number of these abstract interfaces were defined early in the development of
the Python implementation.  In particular, the number, mapping, and sequence
protocols have been part of Python since the beginning.  Other protocols have
been added over time.  For protocols which depend on several handler routines
from the type implementation, the older protocols have been defined as optional
blocks of handlers referenced by the type object.  For newer protocols there are
additional slots in the main type object, with a flag bit being set to indicate
that the slots are present and should be checked by the interpreter.  (The flag
bit does not indicate that the slot values are non-``NULL``. The flag may be set
to indicate the presence of a slot, but a slot may still be unfilled.) ::

   PyNumberMethods   *tp_as_number;
   PySequenceMethods *tp_as_sequence;
   PyMappingMethods  *tp_as_mapping;

If you wish your object to be able to act like a number, a sequence, or a
mapping object, then you place the address of a structure that implements the C
type :c:type:`PyNumberMethods`, :c:type:`PySequenceMethods`, or
:c:type:`PyMappingMethods`, respectively. It is up to you to fill in this
structure with appropriate values. You can find examples of the use of each of
these in the :file:`Objects` directory of the Python source distribution. ::

   hashfunc tp_hash;

This function, if you choose to provide it, should return a hash number for an
instance of your data type. Here is a simple example::

   static Py_hash_t
   newdatatype_hash(PyObject *op)
   {
       newdatatypeobject *self = (newdatatypeobject *) op;
       Py_hash_t result;
       result = self->some_size + 32767 * self->some_number;
       if (result == -1) {
           result = -2;
       }
       return result;
   }

:c:type:`Py_hash_t` is a signed integer type with a platform-varying width.
Returning ``-1`` from :c:member:`~PyTypeObject.tp_hash` indicates an error,
which is why you should be careful to avoid returning it when hash computation
is successful, as seen above.

::

   ternaryfunc tp_call;

This function is called when an instance of your data type is "called", for
example, if ``obj1`` is an instance of your data type and the Python script
contains ``obj1('hello')``, the :c:member:`~PyTypeObject.tp_call` handler is invoked.

This function takes three arguments:

#. *self* is the instance of the data type which is the subject of the call.
   If the call is ``obj1('hello')``, then *self* is ``obj1``.

#. *args* is a tuple containing the arguments to the call.  You can use
   :c:func:`PyArg_ParseTuple` to extract the arguments.

#. *kwds* is a dictionary of keyword arguments that were passed. If this is
   non-``NULL`` and you support keyword arguments, use
   :c:func:`PyArg_ParseTupleAndKeywords` to extract the arguments.  If you
   do not want to support keyword arguments and this is non-``NULL``, raise a
   :exc:`TypeError` with a message saying that keyword arguments are not supported.

Here is a toy ``tp_call`` implementation::

   static PyObject *
   newdatatype_call(PyObject *op, PyObject *args, PyObject *kwds)
   {
       newdatatypeobject *self = (newdatatypeobject *) op;
       PyObject *result;
       const char *arg1;
       const char *arg2;
       const char *arg3;

       if (!PyArg_ParseTuple(args, "sss:call", &arg1, &arg2, &arg3)) {
           return NULL;
       }
       result = PyUnicode_FromFormat(
           "Returning -- value: [%d] arg1: [%s] arg2: [%s] arg3: [%s]\n",
           self->obj_UnderlyingDatatypePtr->size,
           arg1, arg2, arg3);
       return result;
   }

::

   /* Iterators */
   getiterfunc tp_iter;
   iternextfunc tp_iternext;

These functions provide support for the iterator protocol.  Both handlers
take exactly one parameter, the instance for which they are being called,
and return a new reference.  In the case of an error, they should set an
exception and return ``NULL``.  :c:member:`~PyTypeObject.tp_iter` corresponds
to the Python :meth:`~object.__iter__` method, while :c:member:`~PyTypeObject.tp_iternext`
corresponds to the Python :meth:`~iterator.__next__` method.

Any :term:`iterable` object must implement the :c:member:`~PyTypeObject.tp_iter`
handler, which must return an :term:`iterator` object.  Here the same guidelines
apply as for Python classes:

* For collections (such as lists and tuples) which can support multiple
  independent iterators, a new iterator should be created and returned by
  each call to :c:member:`~PyTypeObject.tp_iter`.
* Objects which can only be iterated over once (usually due to side effects of
  iteration, such as file objects) can implement :c:member:`~PyTypeObject.tp_iter`
  by returning a new reference to themselves -- and should also therefore
  implement the :c:member:`~PyTypeObject.tp_iternext`  handler.

Any :term:`iterator` object should implement both :c:member:`~PyTypeObject.tp_iter`
and :c:member:`~PyTypeObject.tp_iternext`.  An iterator's
:c:member:`~PyTypeObject.tp_iter` handler should return a new reference
to the iterator.  Its :c:member:`~PyTypeObject.tp_iternext` handler should
return a new reference to the next object in the iteration, if there is one.
If the iteration has reached the end, :c:member:`~PyTypeObject.tp_iternext`
may return ``NULL`` without setting an exception, or it may set
:exc:`StopIteration` *in addition* to returning ``NULL``; avoiding
the exception can yield slightly better performance.  If an actual error
occurs, :c:member:`~PyTypeObject.tp_iternext` should always set an exception
and return ``NULL``.


.. _weakref-support:

Weak Reference Support
----------------------

One of the goals of Python's weak reference implementation is to allow any type
to participate in the weak reference mechanism without incurring the overhead on
performance-critical objects (such as numbers).

.. seealso::
   Documentation for the :mod:`weakref` module.

For an object to be weakly referenceable, the extension type must set the
``Py_TPFLAGS_MANAGED_WEAKREF`` bit of the :c:member:`~PyTypeObject.tp_flags`
field. The legacy :c:member:`~PyTypeObject.tp_weaklistoffset` field should
be left as zero.

If this flag is set, :c:macro:`Py_TPFLAGS_HAVE_GC` should also be set.

Concretely, here is how the statically declared type object would look::

   static PyTypeObject TrivialType = {
       PyVarObject_HEAD_INIT(NULL, 0)
       /* ... other members omitted for brevity ... */
       .tp_flags = Py_TPFLAGS_MANAGED_WEAKREF | ...,
   };


The only further addition is that ``tp_dealloc`` needs to clear any weak
references (by calling :c:func:`PyObject_ClearWeakRefs`)::

   static void
   Trivial_dealloc(PyObject *op)
   {
       /* Clear weakrefs first before calling any destructors */
       PyObject_ClearWeakRefs(op);
       /* ... remainder of destruction code omitted for brevity ... */
       Py_TYPE(op)->tp_free(op);
   }


More Suggestions
----------------

In order to learn how to implement any specific method for your new data type,
get the :term:`CPython` source code.  Go to the :file:`Objects` directory,
then search the C source files for ``tp_`` plus the function you want
(for example, ``tp_richcompare``).  You will find examples of the function
you want to implement.

When you need to verify that an object is a concrete instance of the type you
are implementing, use the :c:func:`PyObject_TypeCheck` function.  A sample of
its use might be something like the following::

   if (!PyObject_TypeCheck(some_object, &MyType)) {
       PyErr_SetString(PyExc_TypeError, "arg #1 not a mything");
       return NULL;
   }

.. seealso::
   Download CPython source releases.
      https://www.python.org/downloads/source/

   The CPython project on GitHub, where the CPython source code is developed.
      https://github.com/python/cpython
