2. Defining New Types
*********************

As mentioned in the last chapter, Python allows the writer of an
extension module to define new types that can be manipulated from
Python code, much like strings and lists in core Python.

This is not hard; the code for all extension types follows a pattern,
but there are some details that you need to understand before you can
get started.


2.1. The Basics
===============

The Python runtime sees all Python objects as variables of type
"PyObject*", which serves as a “base type” for all Python objects.
"PyObject" itself only contains the refcount and a pointer to the
object’s “type object”.  This is where the action is; the type object
determines which (C) functions get called when, for instance, an
attribute gets looked up on an object or it is multiplied by another
object.  These C functions are called “type methods”.

So, if you want to define a new object type, you need to create a new
type object.

This sort of thing can only be explained by example, so here’s a
minimal, but complete, module that defines a new type:

   #include <Python.h>

   typedef struct {
       PyObject_HEAD
       /* Type-specific fields go here. */
   } noddy_NoddyObject;

   static PyTypeObject noddy_NoddyType = {
       PyVarObject_HEAD_INIT(NULL, 0)
       "noddy.Noddy",             /* tp_name */
       sizeof(noddy_NoddyObject), /* tp_basicsize */
       0,                         /* tp_itemsize */
       0,                         /* tp_dealloc */
       0,                         /* tp_print */
       0,                         /* tp_getattr */
       0,                         /* tp_setattr */
       0,                         /* tp_reserved */
       0,                         /* tp_repr */
       0,                         /* tp_as_number */
       0,                         /* tp_as_sequence */
       0,                         /* tp_as_mapping */
       0,                         /* tp_hash  */
       0,                         /* tp_call */
       0,                         /* tp_str */
       0,                         /* tp_getattro */
       0,                         /* tp_setattro */
       0,                         /* tp_as_buffer */
       Py_TPFLAGS_DEFAULT,        /* tp_flags */
       "Noddy objects",           /* tp_doc */
   };

   static PyModuleDef noddymodule = {
       PyModuleDef_HEAD_INIT,
       "noddy",
       "Example module that creates an extension type.",
       -1,
       NULL, NULL, NULL, NULL, NULL
   };

   PyMODINIT_FUNC
   PyInit_noddy(void)
   {
       PyObject* m;

       noddy_NoddyType.tp_new = PyType_GenericNew;
       if (PyType_Ready(&noddy_NoddyType) < 0)
           return NULL;

       m = PyModule_Create(&noddymodule);
       if (m == NULL)
           return NULL;

       Py_INCREF(&noddy_NoddyType);
       PyModule_AddObject(m, "Noddy", (PyObject *)&noddy_NoddyType);
       return m;
   }

Now that’s quite a bit to take in at once, but hopefully bits will
seem familiar from the last chapter.

The first bit that will be new is:

   typedef struct {
       PyObject_HEAD
   } noddy_NoddyObject;

This is what a Noddy object will contain—in this case, nothing more
than what every Python object contains—a field called "ob_base" of
type "PyObject".  "PyObject" in turn, contains an "ob_refcnt" field
and a pointer to a type object.  These can be accessed using the
macros "Py_REFCNT" and "Py_TYPE" respectively.  These are the fields
the "PyObject_HEAD" macro brings in.  The reason for the macro is to
standardize the layout and to enable special debugging fields in debug
builds.

Note that there is no semicolon after the "PyObject_HEAD" macro; one
is included in the macro definition.  Be wary of adding one by
accident; it’s easy to do from habit, and your compiler might not
complain, but someone else’s probably will!  (On Windows, MSVC is
known to call this an error and refuse to compile the code.)

For contrast, let’s take a look at the corresponding definition for
standard Python floats:

   typedef struct {
       PyObject_HEAD
       double ob_fval;
   } PyFloatObject;

Moving on, we come to the crunch — the type object.

   static PyTypeObject noddy_NoddyType = {
       PyVarObject_HEAD_INIT(NULL, 0)
       "noddy.Noddy",             /* tp_name */
       sizeof(noddy_NoddyObject), /* tp_basicsize */
       0,                         /* tp_itemsize */
       0,                         /* tp_dealloc */
       0,                         /* tp_print */
       0,                         /* tp_getattr */
       0,                         /* tp_setattr */
       0,                         /* tp_as_async */
       0,                         /* tp_repr */
       0,                         /* tp_as_number */
       0,                         /* tp_as_sequence */
       0,                         /* tp_as_mapping */
       0,                         /* tp_hash  */
       0,                         /* tp_call */
       0,                         /* tp_str */
       0,                         /* tp_getattro */
       0,                         /* tp_setattro */
       0,                         /* tp_as_buffer */
       Py_TPFLAGS_DEFAULT,        /* tp_flags */
       "Noddy objects",           /* tp_doc */
   };

Now if you go and look up the definition of "PyTypeObject" in
"object.h" you’ll see that it has many more fields that the definition
above.  The remaining fields will be filled with zeros by the C
compiler, and it’s common practice to not specify them explicitly
unless you need them.

This is so important that we’re going to pick the top of it apart
still further:

   PyVarObject_HEAD_INIT(NULL, 0)

This line is a bit of a wart; what we’d like to write is:

   PyVarObject_HEAD_INIT(&PyType_Type, 0)

as the type of a type object is “type”, but this isn’t strictly
conforming C and some compilers complain.  Fortunately, this member
will be filled in for us by "PyType_Ready()".

   "noddy.Noddy",              /* tp_name */

The name of our type.  This will appear in the default textual
representation of our objects and in some error messages, for example:

   >>> "" + noddy.new_noddy()
   Traceback (most recent call last):
     File "<stdin>", line 1, in <module>
   TypeError: cannot add type "noddy.Noddy" to string

Note that the name is a dotted name that includes both the module name
and the name of the type within the module. The module in this case is
"noddy" and the type is "Noddy", so we set the type name to
"noddy.Noddy". One side effect of using an undotted name is that the
pydoc documentation tool will not list the new type in the module
documentation.

   sizeof(noddy_NoddyObject),  /* tp_basicsize */

This is so that Python knows how much memory to allocate when you call
"PyObject_New()".

Note: If you want your type to be subclassable from Python, and your
  type has the same "tp_basicsize" as its base type, you may have
  problems with multiple inheritance.  A Python subclass of your type
  will have to list your type first in its "__bases__", or else it
  will not be able to call your type’s "__new__()" method without
  getting an error.  You can avoid this problem by ensuring that your
  type has a larger value for "tp_basicsize" than its base type does.
  Most of the time, this will be true anyway, because either your base
  type will be "object", or else you will be adding data members to
  your base type, and therefore increasing its size.

   0,                          /* tp_itemsize */

This has to do with variable length objects like lists and strings.
Ignore this for now.

Skipping a number of type methods that we don’t provide, we set the
class flags to "Py_TPFLAGS_DEFAULT".

   Py_TPFLAGS_DEFAULT,        /* tp_flags */

All types should include this constant in their flags.  It enables all
of the members defined until at least Python 3.3.  If you need further
members, you will need to OR the corresponding flags.

We provide a doc string for the type in "tp_doc".

   "Noddy objects",           /* tp_doc */

Now we get into the type methods, the things that make your objects
different from the others.  We aren’t going to implement any of these
in this version of the module.  We’ll expand this example later to
have more interesting behavior.

For now, all we want to be able to do is to create new "Noddy"
objects. To enable object creation, we have to provide a "tp_new"
implementation. In this case, we can just use the default
implementation provided by the API function "PyType_GenericNew()".
We’d like to just assign this to the "tp_new" slot, but we can’t, for
portability sake, On some platforms or compilers, we can’t statically
initialize a structure member with a function defined in another C
module, so, instead, we’ll assign the "tp_new" slot in the module
initialization function just before calling "PyType_Ready()":

   noddy_NoddyType.tp_new = PyType_GenericNew;
   if (PyType_Ready(&noddy_NoddyType) < 0)
       return;

All the other type methods are *NULL*, so we’ll go over them later —
that’s for a later section!

Everything else in the file should be familiar, except for some code
in "PyInit_noddy()":

   if (PyType_Ready(&noddy_NoddyType) < 0)
       return;

This initializes the "Noddy" type, filing in a number of members,
including "ob_type" that we initially set to *NULL*.

   PyModule_AddObject(m, "Noddy", (PyObject *)&noddy_NoddyType);

This adds the type to the module dictionary.  This allows us to create
"Noddy" instances by calling the "Noddy" class:

   >>> import noddy
   >>> mynoddy = noddy.Noddy()

That’s it!  All that remains is to build it; put the above code in a
file called "noddy.c" and

   from distutils.core import setup, Extension
   setup(name="noddy", version="1.0",
         ext_modules=[Extension("noddy", ["noddy.c"])])

in a file called "setup.py"; then typing

   $ python setup.py build

at a shell should produce a file "noddy.so" in a subdirectory; move to
that directory and fire up Python — you should be able to "import
noddy" and play around with Noddy objects.

That wasn’t so hard, was it?

Of course, the current Noddy type is pretty uninteresting. It has no
data and doesn’t do anything. It can’t even be subclassed.


2.1.1. Adding data and methods to the Basic example
---------------------------------------------------

Let’s extend the basic example to add some data and methods.  Let’s
also make the type usable as a base class. We’ll create a new module,
"noddy2" that adds these capabilities:

   #include <Python.h>
   #include "structmember.h"

   typedef struct {
       PyObject_HEAD
       PyObject *first; /* first name */
       PyObject *last;  /* last name */
       int number;
   } Noddy;

   static void
   Noddy_dealloc(Noddy* self)
   {
       Py_XDECREF(self->first);
       Py_XDECREF(self->last);
       Py_TYPE(self)->tp_free((PyObject*)self);
   }

   static PyObject *
   Noddy_new(PyTypeObject *type, PyObject *args, PyObject *kwds)
   {
       Noddy *self;

       self = (Noddy *)type->tp_alloc(type, 0);
       if (self != NULL) {
           self->first = PyUnicode_FromString("");
           if (self->first == NULL) {
               Py_DECREF(self);
               return NULL;
           }

           self->last = PyUnicode_FromString("");
           if (self->last == NULL) {
               Py_DECREF(self);
               return NULL;
           }

           self->number = 0;
       }

       return (PyObject *)self;
   }

   static int
   Noddy_init(Noddy *self, PyObject *args, PyObject *kwds)
   {
       PyObject *first=NULL, *last=NULL, *tmp;

       static char *kwlist[] = {"first", "last", "number", NULL};

       if (! PyArg_ParseTupleAndKeywords(args, kwds, "|OOi", kwlist,
                                         &first, &last,
                                         &self->number))
           return -1;

       if (first) {
           tmp = self->first;
           Py_INCREF(first);
           self->first = first;
           Py_XDECREF(tmp);
       }

       if (last) {
           tmp = self->last;
           Py_INCREF(last);
           self->last = last;
           Py_XDECREF(tmp);
       }

       return 0;
   }


   static PyMemberDef Noddy_members[] = {
       {"first", T_OBJECT_EX, offsetof(Noddy, first), 0,
        "first name"},
       {"last", T_OBJECT_EX, offsetof(Noddy, last), 0,
        "last name"},
       {"number", T_INT, offsetof(Noddy, number), 0,
        "noddy number"},
       {NULL}  /* Sentinel */
   };

   static PyObject *
   Noddy_name(Noddy* self)
   {
       if (self->first == NULL) {
           PyErr_SetString(PyExc_AttributeError, "first");
           return NULL;
       }

       if (self->last == NULL) {
           PyErr_SetString(PyExc_AttributeError, "last");
           return NULL;
       }

       return PyUnicode_FromFormat("%S %S", self->first, self->last);
   }

   static PyMethodDef Noddy_methods[] = {
       {"name", (PyCFunction)Noddy_name, METH_NOARGS,
        "Return the name, combining the first and last name"
       },
       {NULL}  /* Sentinel */
   };

   static PyTypeObject NoddyType = {
       PyVarObject_HEAD_INIT(NULL, 0)
       "noddy.Noddy",             /* tp_name */
       sizeof(Noddy),             /* tp_basicsize */
       0,                         /* tp_itemsize */
       (destructor)Noddy_dealloc, /* tp_dealloc */
       0,                         /* tp_print */
       0,                         /* tp_getattr */
       0,                         /* tp_setattr */
       0,                         /* tp_reserved */
       0,                         /* tp_repr */
       0,                         /* tp_as_number */
       0,                         /* tp_as_sequence */
       0,                         /* tp_as_mapping */
       0,                         /* tp_hash  */
       0,                         /* tp_call */
       0,                         /* tp_str */
       0,                         /* tp_getattro */
       0,                         /* tp_setattro */
       0,                         /* tp_as_buffer */
       Py_TPFLAGS_DEFAULT |
           Py_TPFLAGS_BASETYPE,   /* tp_flags */
       "Noddy objects",           /* tp_doc */
       0,                         /* tp_traverse */
       0,                         /* tp_clear */
       0,                         /* tp_richcompare */
       0,                         /* tp_weaklistoffset */
       0,                         /* tp_iter */
       0,                         /* tp_iternext */
       Noddy_methods,             /* tp_methods */
       Noddy_members,             /* tp_members */
       0,                         /* tp_getset */
       0,                         /* tp_base */
       0,                         /* tp_dict */
       0,                         /* tp_descr_get */
       0,                         /* tp_descr_set */
       0,                         /* tp_dictoffset */
       (initproc)Noddy_init,      /* tp_init */
       0,                         /* tp_alloc */
       Noddy_new,                 /* tp_new */
   };

   static PyModuleDef noddy2module = {
       PyModuleDef_HEAD_INIT,
       "noddy2",
       "Example module that creates an extension type.",
       -1,
       NULL, NULL, NULL, NULL, NULL
   };

   PyMODINIT_FUNC
   PyInit_noddy2(void)
   {
       PyObject* m;

       if (PyType_Ready(&NoddyType) < 0)
           return NULL;

       m = PyModule_Create(&noddy2module);
       if (m == NULL)
           return NULL;

       Py_INCREF(&NoddyType);
       PyModule_AddObject(m, "Noddy", (PyObject *)&NoddyType);
       return m;
   }

This version of the module has a number of changes.

We’ve added an extra include:

   #include <structmember.h>

This include provides declarations that we use to handle attributes,
as described a bit later.

The name of the "Noddy" object structure has been shortened to
"Noddy".  The type object name has been shortened to "NoddyType".

The  "Noddy" type now has three data attributes, *first*, *last*, and
*number*.  The *first* and *last* variables are Python strings
containing first and last names. The *number* attribute is an integer.

The object structure is updated accordingly:

   typedef struct {
       PyObject_HEAD
       PyObject *first;
       PyObject *last;
       int number;
   } Noddy;

Because we now have data to manage, we have to be more careful about
object allocation and deallocation.  At a minimum, we need a
deallocation method:

   static void
   Noddy_dealloc(Noddy* self)
   {
       Py_XDECREF(self->first);
       Py_XDECREF(self->last);
       Py_TYPE(self)->tp_free((PyObject*)self);
   }

which is assigned to the "tp_dealloc" member:

   (destructor)Noddy_dealloc, /*tp_dealloc*/

This method decrements the reference counts of the two Python
attributes. We use "Py_XDECREF()" here because the "first" and "last"
members could be *NULL*.  It then calls the "tp_free" member of the
object’s type to free the object’s memory.  Note that the object’s
type might not be "NoddyType", because the object may be an instance
of a subclass.

We want to make sure that the first and last names are initialized to
empty strings, so we provide a new method:

   static PyObject *
   Noddy_new(PyTypeObject *type, PyObject *args, PyObject *kwds)
   {
       Noddy *self;

       self = (Noddy *)type->tp_alloc(type, 0);
       if (self != NULL) {
           self->first = PyUnicode_FromString("");
           if (self->first == NULL) {
               Py_DECREF(self);
               return NULL;
           }

           self->last = PyUnicode_FromString("");
           if (self->last == NULL) {
               Py_DECREF(self);
               return NULL;
           }

           self->number = 0;
       }

       return (PyObject *)self;
   }

and install it in the "tp_new" member:

   Noddy_new,                 /* tp_new */

The new member is responsible for creating (as opposed to
initializing) objects of the type.  It is exposed in Python as the
"__new__()" method.  See the paper titled “Unifying types and classes
in Python” for a detailed discussion of the "__new__()" method.  One
reason to implement a new method is to assure the initial values of
instance variables.  In this case, we use the new method to make sure
that the initial values of the members "first" and "last" are not
*NULL*. If we didn’t care whether the initial values were *NULL*, we
could have used "PyType_GenericNew()" as our new method, as we did
before.  "PyType_GenericNew()" initializes all of the instance
variable members to *NULL*.

The new method is a static method that is passed the type being
instantiated and any arguments passed when the type was called, and
that returns the new object created. New methods always accept
positional and keyword arguments, but they often ignore the arguments,
leaving the argument handling to initializer methods. Note that if the
type supports subclassing, the type passed may not be the type being
defined.  The new method calls the "tp_alloc" slot to allocate memory.
We don’t fill the "tp_alloc" slot ourselves. Rather "PyType_Ready()"
fills it for us by inheriting it from our base class, which is
"object" by default.  Most types use the default allocation.

Note: If you are creating a co-operative "tp_new" (one that calls a
  base type’s "tp_new" or "__new__()"), you must *not* try to
  determine what method to call using method resolution order at
  runtime. Always statically determine what type you are going to
  call, and call its "tp_new" directly, or via
  "type->tp_base->tp_new".  If you do not do this, Python subclasses
  of your type that also inherit from other Python-defined classes may
  not work correctly. (Specifically, you may not be able to create
  instances of such subclasses without getting a "TypeError".)

We provide an initialization function:

   static int
   Noddy_init(Noddy *self, PyObject *args, PyObject *kwds)
   {
       PyObject *first=NULL, *last=NULL, *tmp;

       static char *kwlist[] = {"first", "last", "number", NULL};

       if (! PyArg_ParseTupleAndKeywords(args, kwds, "|OOi", kwlist,
                                         &first, &last,
                                         &self->number))
           return -1;

       if (first) {
           tmp = self->first;
           Py_INCREF(first);
           self->first = first;
           Py_XDECREF(tmp);
       }

       if (last) {
           tmp = self->last;
           Py_INCREF(last);
           self->last = last;
           Py_XDECREF(tmp);
       }

       return 0;
   }

by filling the "tp_init" slot.

   (initproc)Noddy_init,         /* tp_init */

The "tp_init" slot is exposed in Python as the "__init__()" method. It
is used to initialize an object after it’s created. Unlike the new
method, we can’t guarantee that the initializer is called.  The
initializer isn’t called when unpickling objects and it can be
overridden.  Our initializer accepts arguments to provide initial
values for our instance. Initializers always accept positional and
keyword arguments. Initializers should return either 0 on success or
-1 on error.

Initializers can be called multiple times.  Anyone can call the
"__init__()" method on our objects.  For this reason, we have to be
extra careful when assigning the new values.  We might be tempted, for
example to assign the "first" member like this:

   if (first) {
       Py_XDECREF(self->first);
       Py_INCREF(first);
       self->first = first;
   }

But this would be risky.  Our type doesn’t restrict the type of the
"first" member, so it could be any kind of object.  It could have a
destructor that causes code to be executed that tries to access the
"first" member.  To be paranoid and protect ourselves against this
possibility, we almost always reassign members before decrementing
their reference counts.  When don’t we have to do this?

* when we absolutely know that the reference count is greater than 1

* when we know that deallocation of the object [1] will not cause
  any calls back into our type’s code

* when decrementing a reference count in a "tp_dealloc" handler when
  garbage-collections is not supported [2]

We want to expose our instance variables as attributes. There are a
number of ways to do that. The simplest way is to define member
definitions:

   static PyMemberDef Noddy_members[] = {
       {"first", T_OBJECT_EX, offsetof(Noddy, first), 0,
        "first name"},
       {"last", T_OBJECT_EX, offsetof(Noddy, last), 0,
        "last name"},
       {"number", T_INT, offsetof(Noddy, number), 0,
        "noddy number"},
       {NULL}  /* Sentinel */
   };

and put the definitions in the "tp_members" slot:

   Noddy_members,             /* tp_members */

Each member definition has a member name, type, offset, access flags
and documentation string. See the Generic Attribute Management section
below for details.

A disadvantage of this approach is that it doesn’t provide a way to
restrict the types of objects that can be assigned to the Python
attributes.  We expect the first and last names to be strings, but any
Python objects can be assigned. Further, the attributes can be
deleted, setting the C pointers to *NULL*.  Even though we can make
sure the members are initialized to non-*NULL* values, the members can
be set to *NULL* if the attributes are deleted.

We define a single method, "name()", that outputs the objects name as
the concatenation of the first and last names.

   static PyObject *
   Noddy_name(Noddy* self)
   {
       if (self->first == NULL) {
           PyErr_SetString(PyExc_AttributeError, "first");
           return NULL;
       }

       if (self->last == NULL) {
           PyErr_SetString(PyExc_AttributeError, "last");
           return NULL;
       }

       return PyUnicode_FromFormat("%S %S", self->first, self->last);
   }

The method is implemented as a C function that takes a "Noddy" (or
"Noddy" subclass) instance as the first argument.  Methods always take
an instance as the first argument. Methods often take positional and
keyword arguments as well, but in this case we don’t take any and
don’t need to accept a positional argument tuple or keyword argument
dictionary. This method is equivalent to the Python method:

   def name(self):
      return "%s %s" % (self.first, self.last)

Note that we have to check for the possibility that our "first" and
"last" members are *NULL*.  This is because they can be deleted, in
which case they are set to *NULL*.  It would be better to prevent
deletion of these attributes and to restrict the attribute values to
be strings.  We’ll see how to do that in the next section.

Now that we’ve defined the method, we need to create an array of
method definitions:

   static PyMethodDef Noddy_methods[] = {
       {"name", (PyCFunction)Noddy_name, METH_NOARGS,
        "Return the name, combining the first and last name"
       },
       {NULL}  /* Sentinel */
   };

and assign them to the "tp_methods" slot:

   Noddy_methods,             /* tp_methods */

Note that we used the "METH_NOARGS" flag to indicate that the method
is passed no arguments.

Finally, we’ll make our type usable as a base class.  We’ve written
our methods carefully so far so that they don’t make any assumptions
about the type of the object being created or used, so all we need to
do is to add the "Py_TPFLAGS_BASETYPE" to our class flag definition:

   Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE, /*tp_flags*/

We rename "PyInit_noddy()" to "PyInit_noddy2()" and update the module
name in the "PyModuleDef" struct.

Finally, we update our "setup.py" file to build the new module:

   from distutils.core import setup, Extension
   setup(name="noddy", version="1.0",
         ext_modules=[
            Extension("noddy", ["noddy.c"]),
            Extension("noddy2", ["noddy2.c"]),
            ])


2.1.2. Providing finer control over data attributes
---------------------------------------------------

In this section, we’ll provide finer control over how the "first" and
"last" attributes are set in the "Noddy" example. In the previous
version of our module, the instance variables "first" and "last" could
be set to non-string values or even deleted. We want to make sure that
these attributes always contain strings.

   #include <Python.h>
   #include "structmember.h"

   typedef struct {
       PyObject_HEAD
       PyObject *first;
       PyObject *last;
       int number;
   } Noddy;

   static void
   Noddy_dealloc(Noddy* self)
   {
       Py_XDECREF(self->first);
       Py_XDECREF(self->last);
       Py_TYPE(self)->tp_free((PyObject*)self);
   }

   static PyObject *
   Noddy_new(PyTypeObject *type, PyObject *args, PyObject *kwds)
   {
       Noddy *self;

       self = (Noddy *)type->tp_alloc(type, 0);
       if (self != NULL) {
           self->first = PyUnicode_FromString("");
           if (self->first == NULL) {
               Py_DECREF(self);
               return NULL;
           }

           self->last = PyUnicode_FromString("");
           if (self->last == NULL) {
               Py_DECREF(self);
               return NULL;
           }

           self->number = 0;
       }

       return (PyObject *)self;
   }

   static int
   Noddy_init(Noddy *self, PyObject *args, PyObject *kwds)
   {
       PyObject *first=NULL, *last=NULL, *tmp;

       static char *kwlist[] = {"first", "last", "number", NULL};

       if (! PyArg_ParseTupleAndKeywords(args, kwds, "|SSi", kwlist,
                                         &first, &last,
                                         &self->number))
           return -1;

       if (first) {
           tmp = self->first;
           Py_INCREF(first);
           self->first = first;
           Py_DECREF(tmp);
       }

       if (last) {
           tmp = self->last;
           Py_INCREF(last);
           self->last = last;
           Py_DECREF(tmp);
       }

       return 0;
   }

   static PyMemberDef Noddy_members[] = {
       {"number", T_INT, offsetof(Noddy, number), 0,
        "noddy number"},
       {NULL}  /* Sentinel */
   };

   static PyObject *
   Noddy_getfirst(Noddy *self, void *closure)
   {
       Py_INCREF(self->first);
       return self->first;
   }

   static int
   Noddy_setfirst(Noddy *self, PyObject *value, void *closure)
   {
       if (value == NULL) {
           PyErr_SetString(PyExc_TypeError, "Cannot delete the first attribute");
           return -1;
       }

       if (! PyUnicode_Check(value)) {
           PyErr_SetString(PyExc_TypeError,
                           "The first attribute value must be a string");
           return -1;
       }

       Py_DECREF(self->first);
       Py_INCREF(value);
       self->first = value;

       return 0;
   }

   static PyObject *
   Noddy_getlast(Noddy *self, void *closure)
   {
       Py_INCREF(self->last);
       return self->last;
   }

   static int
   Noddy_setlast(Noddy *self, PyObject *value, void *closure)
   {
       if (value == NULL) {
           PyErr_SetString(PyExc_TypeError, "Cannot delete the last attribute");
           return -1;
       }

       if (! PyUnicode_Check(value)) {
           PyErr_SetString(PyExc_TypeError,
                           "The last attribute value must be a string");
           return -1;
       }

       Py_DECREF(self->last);
       Py_INCREF(value);
       self->last = value;

       return 0;
   }

   static PyGetSetDef Noddy_getseters[] = {
       {"first",
        (getter)Noddy_getfirst, (setter)Noddy_setfirst,
        "first name",
        NULL},
       {"last",
        (getter)Noddy_getlast, (setter)Noddy_setlast,
        "last name",
        NULL},
       {NULL}  /* Sentinel */
   };

   static PyObject *
   Noddy_name(Noddy* self)
   {
       return PyUnicode_FromFormat("%S %S", self->first, self->last);
   }

   static PyMethodDef Noddy_methods[] = {
       {"name", (PyCFunction)Noddy_name, METH_NOARGS,
        "Return the name, combining the first and last name"
       },
       {NULL}  /* Sentinel */
   };

   static PyTypeObject NoddyType = {
       PyVarObject_HEAD_INIT(NULL, 0)
       "noddy.Noddy",             /* tp_name */
       sizeof(Noddy),             /* tp_basicsize */
       0,                         /* tp_itemsize */
       (destructor)Noddy_dealloc, /* tp_dealloc */
       0,                         /* tp_print */
       0,                         /* tp_getattr */
       0,                         /* tp_setattr */
       0,                         /* tp_reserved */
       0,                         /* tp_repr */
       0,                         /* tp_as_number */
       0,                         /* tp_as_sequence */
       0,                         /* tp_as_mapping */
       0,                         /* tp_hash  */
       0,                         /* tp_call */
       0,                         /* tp_str */
       0,                         /* tp_getattro */
       0,                         /* tp_setattro */
       0,                         /* tp_as_buffer */
       Py_TPFLAGS_DEFAULT |
           Py_TPFLAGS_BASETYPE,   /* tp_flags */
       "Noddy objects",           /* tp_doc */
       0,                         /* tp_traverse */
       0,                         /* tp_clear */
       0,                         /* tp_richcompare */
       0,                         /* tp_weaklistoffset */
       0,                         /* tp_iter */
       0,                         /* tp_iternext */
       Noddy_methods,             /* tp_methods */
       Noddy_members,             /* tp_members */
       Noddy_getseters,           /* tp_getset */
       0,                         /* tp_base */
       0,                         /* tp_dict */
       0,                         /* tp_descr_get */
       0,                         /* tp_descr_set */
       0,                         /* tp_dictoffset */
       (initproc)Noddy_init,      /* tp_init */
       0,                         /* tp_alloc */
       Noddy_new,                 /* tp_new */
   };

   static PyModuleDef noddy3module = {
       PyModuleDef_HEAD_INIT,
       "noddy3",
       "Example module that creates an extension type.",
       -1,
       NULL, NULL, NULL, NULL, NULL
   };

   PyMODINIT_FUNC
   PyInit_noddy3(void)
   {
       PyObject* m;

       if (PyType_Ready(&NoddyType) < 0)
           return NULL;

       m = PyModule_Create(&noddy3module);
       if (m == NULL)
           return NULL;

       Py_INCREF(&NoddyType);
       PyModule_AddObject(m, "Noddy", (PyObject *)&NoddyType);
       return m;
   }

To provide greater control, over the "first" and "last" attributes,
we’ll use custom getter and setter functions.  Here are the functions
for getting and setting the "first" attribute:

   Noddy_getfirst(Noddy *self, void *closure)
   {
       Py_INCREF(self->first);
       return self->first;
   }

   static int
   Noddy_setfirst(Noddy *self, PyObject *value, void *closure)
   {
     if (value == NULL) {
       PyErr_SetString(PyExc_TypeError, "Cannot delete the first attribute");
       return -1;
     }

     if (! PyUnicode_Check(value)) {
       PyErr_SetString(PyExc_TypeError,
                       "The first attribute value must be a str");
       return -1;
     }

     Py_DECREF(self->first);
     Py_INCREF(value);
     self->first = value;

     return 0;
   }

The getter function is passed a "Noddy" object and a “closure”, which
is void pointer. In this case, the closure is ignored. (The closure
supports an advanced usage in which definition data is passed to the
getter and setter. This could, for example, be used to allow a single
set of getter and setter functions that decide the attribute to get or
set based on data in the closure.)

The setter function is passed the "Noddy" object, the new value, and
the closure. The new value may be *NULL*, in which case the attribute
is being deleted.  In our setter, we raise an error if the attribute
is deleted or if the attribute value is not a string.

We create an array of "PyGetSetDef" structures:

   static PyGetSetDef Noddy_getseters[] = {
       {"first",
        (getter)Noddy_getfirst, (setter)Noddy_setfirst,
        "first name",
        NULL},
       {"last",
        (getter)Noddy_getlast, (setter)Noddy_setlast,
        "last name",
        NULL},
       {NULL}  /* Sentinel */
   };

and register it in the "tp_getset" slot:

   Noddy_getseters,           /* tp_getset */

to register our attribute getters and setters.

The last item in a "PyGetSetDef" structure is the closure mentioned
above. In this case, we aren’t using the closure, so we just pass
*NULL*.

We also remove the member definitions for these attributes:

   static PyMemberDef Noddy_members[] = {
       {"number", T_INT, offsetof(Noddy, number), 0,
        "noddy number"},
       {NULL}  /* Sentinel */
   };

We also need to update the "tp_init" handler to only allow strings [3]
to be passed:

   static int
   Noddy_init(Noddy *self, PyObject *args, PyObject *kwds)
   {
       PyObject *first=NULL, *last=NULL, *tmp;

       static char *kwlist[] = {"first", "last", "number", NULL};

       if (! PyArg_ParseTupleAndKeywords(args, kwds, "|SSi", kwlist,
                                         &first, &last,
                                         &self->number))
           return -1;

       if (first) {
           tmp = self->first;
           Py_INCREF(first);
           self->first = first;
           Py_DECREF(tmp);
       }

       if (last) {
           tmp = self->last;
           Py_INCREF(last);
           self->last = last;
           Py_DECREF(tmp);
       }

       return 0;
   }

With these changes, we can assure that the "first" and "last" members
are never *NULL* so we can remove checks for *NULL* values in almost
all cases. This means that most of the "Py_XDECREF()" calls can be
converted to "Py_DECREF()" calls. The only place we can’t change these
calls is in the deallocator, where there is the possibility that the
initialization of these members failed in the constructor.

We also rename the module initialization function and module name in
the initialization function, as we did before, and we add an extra
definition to the "setup.py" file.


2.1.3. Supporting cyclic garbage collection
-------------------------------------------

Python has a cyclic-garbage collector that can identify unneeded
objects even when their reference counts are not zero. This can happen
when objects are involved in cycles.  For example, consider:

   >>> l = []
   >>> l.append(l)
   >>> del l

In this example, we create a list that contains itself. When we delete
it, it still has a reference from itself. Its reference count doesn’t
drop to zero. Fortunately, Python’s cyclic-garbage collector will
eventually figure out that the list is garbage and free it.

In the second version of the "Noddy" example, we allowed any kind of
object to be stored in the "first" or "last" attributes. [4] This
means that "Noddy" objects can participate in cycles:

   >>> import noddy2
   >>> n = noddy2.Noddy()
   >>> l = [n]
   >>> n.first = l

This is pretty silly, but it gives us an excuse to add support for the
cyclic-garbage collector to the "Noddy" example.  To support cyclic
garbage collection, types need to fill two slots and set a class flag
that enables these slots:

   #include <Python.h>
   #include "structmember.h"

   typedef struct {
       PyObject_HEAD
       PyObject *first;
       PyObject *last;
       int number;
   } Noddy;

   static int
   Noddy_traverse(Noddy *self, visitproc visit, void *arg)
   {
       int vret;

       if (self->first) {
           vret = visit(self->first, arg);
           if (vret != 0)
               return vret;
       }
       if (self->last) {
           vret = visit(self->last, arg);
           if (vret != 0)
               return vret;
       }

       return 0;
   }

   static int
   Noddy_clear(Noddy *self)
   {
       PyObject *tmp;

       tmp = self->first;
       self->first = NULL;
       Py_XDECREF(tmp);

       tmp = self->last;
       self->last = NULL;
       Py_XDECREF(tmp);

       return 0;
   }

   static void
   Noddy_dealloc(Noddy* self)
   {
       PyObject_GC_UnTrack(self);
       Noddy_clear(self);
       Py_TYPE(self)->tp_free((PyObject*)self);
   }

   static PyObject *
   Noddy_new(PyTypeObject *type, PyObject *args, PyObject *kwds)
   {
       Noddy *self;

       self = (Noddy *)type->tp_alloc(type, 0);
       if (self != NULL) {
           self->first = PyUnicode_FromString("");
           if (self->first == NULL) {
               Py_DECREF(self);
               return NULL;
           }

           self->last = PyUnicode_FromString("");
           if (self->last == NULL) {
               Py_DECREF(self);
               return NULL;
           }

           self->number = 0;
       }

       return (PyObject *)self;
   }

   static int
   Noddy_init(Noddy *self, PyObject *args, PyObject *kwds)
   {
       PyObject *first=NULL, *last=NULL, *tmp;

       static char *kwlist[] = {"first", "last", "number", NULL};

       if (! PyArg_ParseTupleAndKeywords(args, kwds, "|OOi", kwlist,
                                         &first, &last,
                                         &self->number))
           return -1;

       if (first) {
           tmp = self->first;
           Py_INCREF(first);
           self->first = first;
           Py_XDECREF(tmp);
       }

       if (last) {
           tmp = self->last;
           Py_INCREF(last);
           self->last = last;
           Py_XDECREF(tmp);
       }

       return 0;
   }


   static PyMemberDef Noddy_members[] = {
       {"first", T_OBJECT_EX, offsetof(Noddy, first), 0,
        "first name"},
       {"last", T_OBJECT_EX, offsetof(Noddy, last), 0,
        "last name"},
       {"number", T_INT, offsetof(Noddy, number), 0,
        "noddy number"},
       {NULL}  /* Sentinel */
   };

   static PyObject *
   Noddy_name(Noddy* self)
   {
       if (self->first == NULL) {
           PyErr_SetString(PyExc_AttributeError, "first");
           return NULL;
       }

       if (self->last == NULL) {
           PyErr_SetString(PyExc_AttributeError, "last");
           return NULL;
       }

       return PyUnicode_FromFormat("%S %S", self->first, self->last);
   }

   static PyMethodDef Noddy_methods[] = {
       {"name", (PyCFunction)Noddy_name, METH_NOARGS,
        "Return the name, combining the first and last name"
       },
       {NULL}  /* Sentinel */
   };

   static PyTypeObject NoddyType = {
       PyVarObject_HEAD_INIT(NULL, 0)
       "noddy.Noddy",             /* tp_name */
       sizeof(Noddy),             /* tp_basicsize */
       0,                         /* tp_itemsize */
       (destructor)Noddy_dealloc, /* tp_dealloc */
       0,                         /* tp_print */
       0,                         /* tp_getattr */
       0,                         /* tp_setattr */
       0,                         /* tp_reserved */
       0,                         /* tp_repr */
       0,                         /* tp_as_number */
       0,                         /* tp_as_sequence */
       0,                         /* tp_as_mapping */
       0,                         /* tp_hash  */
       0,                         /* tp_call */
       0,                         /* tp_str */
       0,                         /* tp_getattro */
       0,                         /* tp_setattro */
       0,                         /* tp_as_buffer */
       Py_TPFLAGS_DEFAULT |
           Py_TPFLAGS_BASETYPE |
           Py_TPFLAGS_HAVE_GC,    /* tp_flags */
       "Noddy objects",           /* tp_doc */
       (traverseproc)Noddy_traverse,   /* tp_traverse */
       (inquiry)Noddy_clear,           /* tp_clear */
       0,                         /* tp_richcompare */
       0,                         /* tp_weaklistoffset */
       0,                         /* tp_iter */
       0,                         /* tp_iternext */
       Noddy_methods,             /* tp_methods */
       Noddy_members,             /* tp_members */
       0,                         /* tp_getset */
       0,                         /* tp_base */
       0,                         /* tp_dict */
       0,                         /* tp_descr_get */
       0,                         /* tp_descr_set */
       0,                         /* tp_dictoffset */
       (initproc)Noddy_init,      /* tp_init */
       0,                         /* tp_alloc */
       Noddy_new,                 /* tp_new */
   };

   static PyModuleDef noddy4module = {
       PyModuleDef_HEAD_INIT,
       "noddy4",
       "Example module that creates an extension type.",
       -1,
       NULL, NULL, NULL, NULL, NULL
   };

   PyMODINIT_FUNC
   PyInit_noddy4(void)
   {
       PyObject* m;

       if (PyType_Ready(&NoddyType) < 0)
           return NULL;

       m = PyModule_Create(&noddy4module);
       if (m == NULL)
           return NULL;

       Py_INCREF(&NoddyType);
       PyModule_AddObject(m, "Noddy", (PyObject *)&NoddyType);
       return m;
   }

The traversal method provides access to subobjects that could
participate in cycles:

   static int
   Noddy_traverse(Noddy *self, visitproc visit, void *arg)
   {
       int vret;

       if (self->first) {
           vret = visit(self->first, arg);
           if (vret != 0)
               return vret;
       }
       if (self->last) {
           vret = visit(self->last, arg);
           if (vret != 0)
               return vret;
       }

       return 0;
   }

For each subobject that can participate in cycles, we need to call the
"visit()" function, which is passed to the traversal method. The
"visit()" function takes as arguments the subobject and the extra
argument *arg* passed to the traversal method.  It returns an integer
value that must be returned if it is non-zero.

Python provides a "Py_VISIT()" macro that automates calling visit
functions.  With "Py_VISIT()", "Noddy_traverse()" can be simplified:

   static int
   Noddy_traverse(Noddy *self, visitproc visit, void *arg)
   {
       Py_VISIT(self->first);
       Py_VISIT(self->last);
       return 0;
   }

Note: Note that the "tp_traverse" implementation must name its
  arguments exactly *visit* and *arg* in order to use "Py_VISIT()".
  This is to encourage uniformity across these boring implementations.

We also need to provide a method for clearing any subobjects that can
participate in cycles.

   static int
   Noddy_clear(Noddy *self)
   {
       PyObject *tmp;

       tmp = self->first;
       self->first = NULL;
       Py_XDECREF(tmp);

       tmp = self->last;
       self->last = NULL;
       Py_XDECREF(tmp);

       return 0;
   }

Notice the use of a temporary variable in "Noddy_clear()". We use the
temporary variable so that we can set each member to *NULL* before
decrementing its reference count.  We do this because, as was
discussed earlier, if the reference count drops to zero, we might
cause code to run that calls back into the object.  In addition,
because we now support garbage collection, we also have to worry about
code being run that triggers garbage collection.  If garbage
collection is run, our "tp_traverse" handler could get called. We
can’t take a chance of having "Noddy_traverse()" called when a
member’s reference count has dropped to zero and its value hasn’t been
set to *NULL*.

Python provides a "Py_CLEAR()" that automates the careful decrementing
of reference counts.  With "Py_CLEAR()", the "Noddy_clear()" function
can be simplified:

   static int
   Noddy_clear(Noddy *self)
   {
       Py_CLEAR(self->first);
       Py_CLEAR(self->last);
       return 0;
   }

Note that "Noddy_dealloc()" may call arbitrary functions through
"__del__" method or weakref callback. It means circular GC can be
triggered inside the function.  Since GC assumes reference count is
not zero, we need to untrack the object from GC by calling
"PyObject_GC_UnTrack()" before clearing members. Here is reimplemented
deallocator which uses "PyObject_GC_UnTrack()" and "Noddy_clear()".

   static void
   Noddy_dealloc(Noddy* self)
   {
       PyObject_GC_UnTrack(self);
       Noddy_clear(self);
       Py_TYPE(self)->tp_free((PyObject*)self);
   }

Finally, we add the "Py_TPFLAGS_HAVE_GC" flag to the class flags:

   Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE | Py_TPFLAGS_HAVE_GC, /* tp_flags */

That’s pretty much it.  If we had written custom "tp_alloc" or
"tp_free" slots, we’d need to modify them for cyclic-garbage
collection. Most extensions will use the versions automatically
provided.


2.1.4. Subclassing other types
------------------------------

It is possible to create new extension types that are derived from
existing types. It is easiest to inherit from the built in types,
since an extension can easily use the "PyTypeObject" it needs. It can
be difficult to share these "PyTypeObject" structures between
extension modules.

In this example we will create a "Shoddy" type that inherits from the
built-in "list" type. The new type will be completely compatible with
regular lists, but will have an additional "increment()" method that
increases an internal counter.

   >>> import shoddy
   >>> s = shoddy.Shoddy(range(3))
   >>> s.extend(s)
   >>> print(len(s))
   6
   >>> print(s.increment())
   1
   >>> print(s.increment())
   2

   #include <Python.h>

   typedef struct {
       PyListObject list;
       int state;
   } Shoddy;


   static PyObject *
   Shoddy_increment(Shoddy *self, PyObject *unused)
   {
       self->state++;
       return PyLong_FromLong(self->state);
   }


   static PyMethodDef Shoddy_methods[] = {
       {"increment", (PyCFunction)Shoddy_increment, METH_NOARGS,
        PyDoc_STR("increment state counter")},
       {NULL,	NULL},
   };

   static int
   Shoddy_init(Shoddy *self, PyObject *args, PyObject *kwds)
   {
       if (PyList_Type.tp_init((PyObject *)self, args, kwds) < 0)
           return -1;
       self->state = 0;
       return 0;
   }


   static PyTypeObject ShoddyType = {
       PyVarObject_HEAD_INIT(NULL, 0)
       "shoddy.Shoddy",         /* tp_name */
       sizeof(Shoddy),          /* tp_basicsize */
       0,                       /* tp_itemsize */
       0,                       /* tp_dealloc */
       0,                       /* tp_print */
       0,                       /* tp_getattr */
       0,                       /* tp_setattr */
       0,                       /* tp_reserved */
       0,                       /* tp_repr */
       0,                       /* tp_as_number */
       0,                       /* tp_as_sequence */
       0,                       /* tp_as_mapping */
       0,                       /* tp_hash */
       0,                       /* tp_call */
       0,                       /* tp_str */
       0,                       /* tp_getattro */
       0,                       /* tp_setattro */
       0,                       /* tp_as_buffer */
       Py_TPFLAGS_DEFAULT |
           Py_TPFLAGS_BASETYPE, /* tp_flags */
       0,                       /* tp_doc */
       0,                       /* tp_traverse */
       0,                       /* tp_clear */
       0,                       /* tp_richcompare */
       0,                       /* tp_weaklistoffset */
       0,                       /* tp_iter */
       0,                       /* tp_iternext */
       Shoddy_methods,          /* tp_methods */
       0,                       /* tp_members */
       0,                       /* tp_getset */
       0,                       /* tp_base */
       0,                       /* tp_dict */
       0,                       /* tp_descr_get */
       0,                       /* tp_descr_set */
       0,                       /* tp_dictoffset */
       (initproc)Shoddy_init,   /* tp_init */
       0,                       /* tp_alloc */
       0,                       /* tp_new */
   };

   static PyModuleDef shoddymodule = {
       PyModuleDef_HEAD_INIT,
       "shoddy",
       "Shoddy module",
       -1,
       NULL, NULL, NULL, NULL, NULL
   };

   PyMODINIT_FUNC
   PyInit_shoddy(void)
   {
       PyObject *m;

       ShoddyType.tp_base = &PyList_Type;
       if (PyType_Ready(&ShoddyType) < 0)
           return NULL;

       m = PyModule_Create(&shoddymodule);
       if (m == NULL)
           return NULL;

       Py_INCREF(&ShoddyType);
       PyModule_AddObject(m, "Shoddy", (PyObject *) &ShoddyType);
       return m;
   }

As you can see, the source code closely resembles the "Noddy" examples
in previous sections. We will break down the main differences between
them.

   typedef struct {
       PyListObject list;
       int state;
   } Shoddy;

The primary difference for derived type objects is that the base
type’s object structure must be the first value. The base type will
already include the "PyObject_HEAD()" at the beginning of its
structure.

When a Python object is a "Shoddy" instance, its *PyObject** pointer
can be safely cast to both *PyListObject** and *Shoddy**.

   static int
   Shoddy_init(Shoddy *self, PyObject *args, PyObject *kwds)
   {
       if (PyList_Type.tp_init((PyObject *)self, args, kwds) < 0)
          return -1;
       self->state = 0;
       return 0;
   }

In the "__init__" method for our type, we can see how to call through
to the "__init__" method of the base type.

This pattern is important when writing a type with custom "new" and
"dealloc" methods. The "new" method should not actually create the
memory for the object with "tp_alloc", that will be handled by the
base class when calling its "tp_new".

When filling out the "PyTypeObject()" for the "Shoddy" type, you see a
slot for "tp_base()". Due to cross platform compiler issues, you can’t
fill that field directly with the "PyList_Type()"; it can be done
later in the module’s "init()" function.

   PyMODINIT_FUNC
   PyInit_shoddy(void)
   {
       PyObject *m;

       ShoddyType.tp_base = &PyList_Type;
       if (PyType_Ready(&ShoddyType) < 0)
           return NULL;

       m = PyModule_Create(&shoddymodule);
       if (m == NULL)
           return NULL;

       Py_INCREF(&ShoddyType);
       PyModule_AddObject(m, "Shoddy", (PyObject *) &ShoddyType);
       return m;
   }

Before calling "PyType_Ready()", the type structure must have the
"tp_base" slot filled in. When we are deriving a new type, it is not
necessary to fill out the "tp_alloc" slot with "PyType_GenericNew()" –
the allocate function from the base type will be inherited.

After that, calling "PyType_Ready()" and adding the type object to the
module is the same as with the basic "Noddy" examples.


2.2. 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 "PyTypeObject", with some fields only used
in debug builds omitted:

   typedef struct _typeobject {
       PyObject_VAR_HEAD
       const char *tp_name; /* For printing, in format "<module>.<name>" */
       Py_ssize_t tp_basicsize, tp_itemsize; /* For allocation */

       /* Methods to implement standard operations */

       destructor tp_dealloc;
       printfunc tp_print;
       getattrfunc tp_getattr;
       setattrfunc tp_setattr;
       PyAsyncMethods *tp_as_async; /* formerly known as tp_compare (Python 2)
                                       or tp_reserved (Python 3) */
       reprfunc tp_repr;

       /* Method suites for standard classes */

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

       /* More standard operations (here for binary compatibility) */

       hashfunc tp_hash;
       ternaryfunc tp_call;
       reprfunc tp_str;
       getattrofunc tp_getattro;
       setattrofunc tp_setattro;

       /* Functions to access object as input/output buffer */
       PyBufferProcs *tp_as_buffer;

       /* Flags to define presence of optional/expanded features */
       unsigned long tp_flags;

       const char *tp_doc; /* Documentation string */

       /* call function for all accessible objects */
       traverseproc tp_traverse;

       /* delete references to contained objects */
       inquiry tp_clear;

       /* rich comparisons */
       richcmpfunc tp_richcompare;

       /* weak reference enabler */
       Py_ssize_t tp_weaklistoffset;

       /* Iterators */
       getiterfunc tp_iter;
       iternextfunc tp_iternext;

       /* Attribute descriptor and subclassing stuff */
       struct PyMethodDef *tp_methods;
       struct PyMemberDef *tp_members;
       struct PyGetSetDef *tp_getset;
       struct _typeobject *tp_base;
       PyObject *tp_dict;
       descrgetfunc tp_descr_get;
       descrsetfunc tp_descr_set;
       Py_ssize_t tp_dictoffset;
       initproc tp_init;
       allocfunc tp_alloc;
       newfunc tp_new;
       freefunc tp_free; /* Low-level free-memory routine */
       inquiry tp_is_gc; /* For PyObject_IS_GC */
       PyObject *tp_bases;
       PyObject *tp_mro; /* method resolution order */
       PyObject *tp_cache;
       PyObject *tp_subclasses;
       PyObject *tp_weaklist;
       destructor tp_del;

       /* Type attribute cache version tag. Added in version 2.6 */
       unsigned int tp_version_tag;

       destructor tp_finalize;

   } PyTypeObject;

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; be sure
your type initialization keeps the fields in the right order!  It’s
often easiest to find an example that includes all the fields you need
(even if they’re initialized to "0") 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 last section, 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, lists) which is where
the "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.


2.2.1. Finalization and De-allocation
-------------------------------------

   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(newdatatypeobject * obj)
   {
       free(obj->obj_UnderlyingDatatypePtr);
       Py_TYPE(obj)->tp_free(obj);
   }

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
"PyErr_Fetch()" and "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_CallObject(self->my_callback, NULL);
           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(obj)->tp_free((PyObject*)self);
   }

Note: There are limitations to what you can safely do in a
  deallocator function. First, if your type supports garbage
  collection (using "tp_traverse" and/or "tp_clear"), some of the
  object’s members can have been cleared or finalized by the time
  "tp_dealloc" is called. Second, in "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 "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 "tp_dealloc", and instead use the new
  "tp_finalize" type method.

  See also: **PEP 442** explains the new finalization scheme.


2.2.2. Object Presentation
--------------------------

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

   reprfunc tp_repr;
   reprfunc tp_str;

The "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(newdatatypeobject * obj)
   {
       return PyUnicode_FromFormat("Repr-ified_newdatatype{{size:\%d}}",
                                   obj->obj_UnderlyingDatatypePtr->size);
   }

If no "tp_repr" handler is specified, the interpreter will supply a
representation that uses the type’s "tp_name" and a uniquely-
identifying value for the object.

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

Here is a simple example:

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


2.2.3. 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
"char*", while the other accepts a "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 "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.


2.2.3.1. 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:

1. The name of the attributes must be known when "PyType_Ready()"
   is called.

2. 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 "PyType_Ready()" is called, it uses three tables referenced by
the type object to create *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
"tp_getattro" and "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 "tp_methods" is not *NULL*, it must refer to an array of
"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 "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 {
       char *name;
       int   type;
       int   offset;
       int   flags;
       char *doc;
   } PyMemberDef;

For each entry in the table, a *descriptor* will be constructed and
added to the type which will be able to extract a value from the
instance structure.  The "type" field should contain one of the type
codes defined in the "structmember.h" header; the value will be used
to determine how to convert Python values to and from C values.  The
"flags" field is used to store flags which control how the attribute
can be accessed.

The following flag constants are defined in "structmember.h"; they may
be combined using bitwise-OR.

+-----------------------------+------------------------------------------------+
| Constant                    | Meaning                                        |
+=============================+================================================+
| "READONLY"                  | Never writable.                                |
+-----------------------------+------------------------------------------------+
| "READ_RESTRICTED"           | Not readable in restricted mode.               |
+-----------------------------+------------------------------------------------+
| "WRITE_RESTRICTED"          | Not writable in restricted mode.               |
+-----------------------------+------------------------------------------------+
| "RESTRICTED"                | Not readable or writable in restricted mode.   |
+-----------------------------+------------------------------------------------+

An interesting advantage of using the "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 "__doc__" attribute.

As with the "tp_methods" table, a sentinel entry with a "name" value
of *NULL* is required.


2.2.3.2. Type-specific Attribute Management
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

For simplicity, only the "char*" version will be demonstrated here;
the type of the name parameter is the only difference between the
"char*" and "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 "tp_getattr" handler is called when the object requires an
attribute look-up.  It is called in the same situations where the
"__getattr__()" method of a class would be called.

Here is an example:

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

       PyErr_Format(PyExc_AttributeError,
                    "'%.50s' object has no attribute '%.400s'",
                    tp->tp_name, name);
       return NULL;
   }

The "tp_setattr" handler is called when the "__setattr__()" or
"__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 "tp_setattr" handler should be set to *NULL*.

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


2.2.4. Object Comparison
------------------------

   richcmpfunc tp_richcompare;

The "tp_richcompare" handler is called when comparisons are needed.
It is analogous to the rich comparison methods, like "__lt__()", and
also called by "PyObject_RichCompare()" and
"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_GT", "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 *obj1, PyObject *obj2, int op)
   {
       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;
       Py_INCREF(result);
       return result;
    }


2.2.5. Abstract Protocol Support
--------------------------------

Python supports a variety of *abstract* ‘protocols;’ the specific
interfaces provided to use these interfaces are documented in Abstract
Objects Layer.

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 "PyNumberMethods", "PySequenceMethods", or
"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 "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 moderately
pointless example:

   static long
   newdatatype_hash(newdatatypeobject *obj)
   {
       long result;
       result = obj->obj_UnderlyingDatatypePtr->size;
       result = result * 3;
       return result;
   }

   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 "tp_call" handler is
invoked.

This function takes three arguments:

1. *arg1* is the instance of the data type which is the subject of
   the call. If the call is "obj1('hello')", then *arg1* is "obj1".

2. *arg2* is a tuple containing the arguments to the call.  You can
   use "PyArg_ParseTuple()" to extract the arguments.

3. *arg3* is a dictionary of keyword arguments that were passed. If
   this is non-*NULL* and you support keyword arguments, use
   "PyArg_ParseTupleAndKeywords()" to extract the arguments.  If you
   do not want to support keyword arguments and this is non-*NULL*,
   raise a "TypeError" with a message saying that keyword arguments
   are not supported.

Here is a desultory example of the implementation of the call
function.

   /* Implement the call function.
    *    obj1 is the instance receiving the call.
    *    obj2 is a tuple containing the arguments to the call, in this
    *         case 3 strings.
    */
   static PyObject *
   newdatatype_call(newdatatypeobject *obj, PyObject *args, PyObject *other)
   {
       PyObject *result;
       char *arg1;
       char *arg2;
       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",
           obj->obj_UnderlyingDatatypePtr->size,
           arg1, arg2, arg3);
       return result;
   }

   /* Iterators */
   getiterfunc tp_iter;
   iternextfunc tp_iternext;

These functions provide support for the iterator protocol.  Any object
which wishes to support iteration over its contents (which may be
generated during iteration) must implement the "tp_iter" handler.
Objects which are returned by a "tp_iter" handler must implement both
the "tp_iter" and "tp_iternext" handlers. 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*.

For an object which represents an iterable collection, the "tp_iter"
handler must return an iterator object.  The iterator object is
responsible for maintaining the state of the iteration.  For
collections which can support multiple iterators which do not
interfere with each other (as lists and tuples do), a new iterator
should be created and returned.  Objects which can only be iterated
over once (usually due to side effects of iteration) should implement
this handler by returning a new reference to themselves, and should
also implement the "tp_iternext" handler.  File objects are an example
of such an iterator.

Iterator objects should implement both handlers.  The "tp_iter"
handler should return a new reference to the iterator (this is the
same as the "tp_iter" handler for objects which can only be iterated
over destructively).  The "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, it may return *NULL* without setting an
exception or it may set "StopIteration"; avoiding the exception can
yield slightly better performance.  If an actual error occurs, it
should set an exception and return *NULL*.


2.2.6. 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 those objects which do not benefit by weak
referencing (such as numbers).

For an object to be weakly referencable, the extension must include a
"PyObject*" field in the instance structure for the use of the weak
reference mechanism; it must be initialized to *NULL* by the object’s
constructor.  It must also set the "tp_weaklistoffset" field of the
corresponding type object to the offset of the field. For example, the
instance type is defined with the following structure:

   typedef struct {
       PyObject_HEAD
       PyClassObject *in_class;       /* The class object */
       PyObject      *in_dict;        /* A dictionary */
       PyObject      *in_weakreflist; /* List of weak references */
   } PyInstanceObject;

The statically-declared type object for instances is defined this way:

   PyTypeObject PyInstance_Type = {
       PyVarObject_HEAD_INIT(&PyType_Type, 0)
       0,
       "module.instance",

       /* Lots of stuff omitted for brevity... */

       Py_TPFLAGS_DEFAULT,                         /* tp_flags */
       0,                                          /* tp_doc */
       0,                                          /* tp_traverse */
       0,                                          /* tp_clear */
       0,                                          /* tp_richcompare */
       offsetof(PyInstanceObject, in_weakreflist), /* tp_weaklistoffset */
   };

The type constructor is responsible for initializing the weak
reference list to *NULL*:

   static PyObject *
   instance_new() {
       /* Other initialization stuff omitted for brevity */

       self->in_weakreflist = NULL;

       return (PyObject *) self;
   }

The only further addition is that the destructor needs to call the
weak reference manager to clear any weak references.  This is only
required if the weak reference list is non-*NULL*:

   static void
   instance_dealloc(PyInstanceObject *inst)
   {
       /* Allocate temporaries if needed, but do not begin
          destruction just yet.
        */

       if (inst->in_weakreflist != NULL)
           PyObject_ClearWeakRefs((PyObject *) inst);

       /* Proceed with object destruction normally. */
   }


2.2.7. More Suggestions
-----------------------

Remember that you can omit most of these functions, in which case you
provide "0" as a value.  There are type definitions for each of the
functions you must provide.  They are in "object.h" in the Python
include directory that comes with the source distribution of Python.

In order to learn how to implement any specific method for your new
data type, do the following: Download and unpack the Python source
distribution.  Go to the "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 an instance of the type you
are implementing, use the "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;
   }

-[ Footnotes ]-

[1] This is true when we know that the object is a basic type,
    like a string or a float.

[2] We relied on this in the "tp_dealloc" handler in this example,
    because our type doesn’t support garbage collection. Even if a
    type supports garbage collection, there are calls that can be made
    to “untrack” the object from garbage collection, however, these
    calls are advanced and not covered here.

[3] We now know that the first and last members are strings, so
    perhaps we could be less careful about decrementing their
    reference counts, however, we accept instances of string
    subclasses. Even though deallocating normal strings won’t call
    back into our objects, we can’t guarantee that deallocating an
    instance of a string subclass won’t call back into our objects.

[4] Even in the third version, we aren’t guaranteed to avoid
    cycles. Instances of string subclasses are allowed and string
    subclasses could allow cycles even if normal strings don’t.
