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

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

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

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(self)->tp_free(self);
}

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

Attribute Management¶

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

Python supports two pairs of attribute handlers; a type that supports attributes only needs to implement the functions for one pair. The difference is that one pair takes the name of the attribute as a 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.

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

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

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

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

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

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

Object Comparison¶

richcmpfunc tp_richcompare;

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

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

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

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

Abstract Protocol Support¶

Python supports a variety of abstract ‘protocols;’ the specific interfaces provided to use these interfaces are documented in 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(PyObject *op)
{
    newdatatypeobject *self = (newdatatypeobject *) op;
    Py_hash_t result;
    result = self->some_size + 32767 * self->some_number;
    if (result == -1) {
        result = -2;
    }
    return result;
}

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

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

/* Iterators */
getiterfunc tp_iter;
iternextfunc tp_iternext;

These functions provide support for the iterator protocol. Both handlers take exactly one parameter, the instance for which they are being called, and return a new reference. In the case of an error, they should set an exception and return NULL. 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.

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).

For an object to be weakly referenceable, 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.

If this flag is set, Py_TPFLAGS_HAVE_GC should also be set.

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

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

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

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

More Suggestions¶

In order to learn how to implement any specific method for your new data type, get the 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;
}