3. Defining Extension Types: Assorted Topics
********************************************

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;
       Py_ssize_t tp_vectorcall_offset;
       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 */

       /* Assigned meaning in release 2.0 */
       /* call function for all accessible objects */
       traverseproc tp_traverse;

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

       /* Assigned meaning in release 2.1 */
       /* 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;
       // Strong reference on a heap type, borrowed reference on a static type
       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;
       vectorcallfunc tp_vectorcall;

       /* bitset of which type-watchers care about this type */
       unsigned char tp_watched;
   } 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.  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 "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.


3.1. 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((PyObject *)obj);
   }

If your type supports garbage collection, the destructor should call
"PyObject_GC_UnTrack()" before clearing any member fields:

   static void
   newdatatype_dealloc(newdatatypeobject *obj)
   {
       PyObject_GC_UnTrack(obj);
       Py_CLEAR(obj->other_obj);
       ...
       Py_TYPE(obj)->tp_free((PyObject *)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_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(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.


3.2. 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);
   }


3.3. 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.


3.3.1. 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 {
       const char *name;
       int         type;
       int         offset;
       int         flags;
       const 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 a type code like
"Py_T_INT" or "Py_T_DOUBLE"; 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:
you can set it to "Py_READONLY" to prevent Python code from setting
it.

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 "ml_name"
value of "NULL" is required.


3.3.2. 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,
                    "'%.100s' object has no attribute '%.400s'",
                    Py_TYPE(obj)->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)
   {
       PyErr_Format(PyExc_RuntimeError, "Read-only attribute: %s", name);
       return -1;
   }


3.4. 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_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(newdatatypeobject *obj1, newdatatypeobject *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;
    }


3.5. 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 simple example:

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

"Py_hash_t" is a signed integer type with a platform-varying width.
Returning "-1" from "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 "tp_call" handler is
invoked.

This function takes three arguments:

1. *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".

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

3. *kwds* 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 toy "tp_call" implementation:

   static PyObject *
   newdatatype_call(newdatatypeobject *obj, PyObject *args, PyObject *kwds)
   {
       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",
           obj->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".  "tp_iter" corresponds
to the Python "__iter__()" method, while "tp_iternext" corresponds to
the Python "__next__()" method.

Any *iterable* object must implement the "tp_iter" handler, which must
return an *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 "tp_iter".

* Objects which can only be iterated over once (usually due to side
  effects of iteration, such as file objects) can implement "tp_iter"
  by returning a new reference to themselves – and should also
  therefore implement the "tp_iternext"  handler.

Any *iterator* object should implement both "tp_iter" and
"tp_iternext".  An iterator’s "tp_iter" handler should return a new
reference to the iterator.  Its "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, "tp_iternext" may return "NULL"
without setting an exception, or it may set "StopIteration" *in
addition* to returning "NULL"; avoiding the exception can yield
slightly better performance.  If an actual error occurs, "tp_iternext"
should always set an exception and return "NULL".


3.6. 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 performance-critical objects (such as
numbers).

See also: Documentation for the "weakref" module.

For an object to be weakly referencable, the extension type must set
the "Py_TPFLAGS_MANAGED_WEAKREF" bit of the "tp_flags" field. The
legacy "tp_weaklistoffset" field should be left as zero.

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 "PyObject_ClearWeakRefs()"):

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


3.7. More Suggestions
=====================

In order to learn how to implement any specific method for your new
data type, get the *CPython* source code.  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 a concrete 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;
   }

See also:

  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
