Extension Types

Introduction

Note

This page uses two different syntax variants:

  • Cython specific cdef syntax, which was designed to make type declarations concise and easily readable from a C/C++ perspective.

  • Pure Python syntax which allows static Cython type declarations in pure Python code, following PEP-484 type hints and PEP 526 variable annotations.

    To make use of C data types in Python syntax, you need to import the special cython module in the Python module that you want to compile, e.g.

    import cython
    

As well as creating normal user-defined classes with the Python class statement, Cython also lets you create new built-in Python types, known as extension types. You define an extension type using the cdef class statement or decorating the class with the @cclass decorator. Here’s an example:

@cython.cclass
class Shrubbery:
    width: cython.int
    height: cython.int

    def __init__(self, w, h):
        self.width = w
        self.height = h

    def describe(self):
        print("This shrubbery is", self.width,
              "by", self.height, "cubits.")

As you can see, a Cython extension type definition looks a lot like a Python class definition. Within it, you use the def statement to define methods that can be called from Python code. You can even define many of the special methods such as __init__() as you would in Python.

The main difference is that you can define attributes using

  • the cdef statement,

  • the cython.declare() function or

  • the annotation of an attribute name.

@cython.cclass
class Shrubbery:
    width = declare(cython.int)
    height: cython.int

The attributes may be Python objects (either generic or of a particular extension type), or they may be of any C data type. So you can use extension types to wrap arbitrary C data structures and provide a Python-like interface to them.

Static Attributes

Attributes of an extension type are stored directly in the object’s C struct. The set of attributes is fixed at compile time; you can’t add attributes to an extension type instance at run time simply by assigning to them, as you could with a Python class instance. However, you can explicitly enable support for dynamically assigned attributes, or subclass the extension type with a normal Python class, which then supports arbitrary attribute assignments. See Dynamic Attributes.

There are two ways that attributes of an extension type can be accessed: by Python attribute lookup, or by direct access to the C struct from Cython code. Python code is only able to access attributes of an extension type by the first method, but Cython code can use either method.

By default, extension type attributes are only accessible by direct access, not Python access, which means that they are not accessible from Python code. To make them accessible from Python code, you need to declare them as public or readonly. For example:

import cython

@cython.cclass
class Shrubbery:
    width = cython.declare(cython.int, visibility='public')
    height = cython.declare(cython.int, visibility='public')
    depth = cython.declare(cython.float, visibility='readonly')

makes the width and height attributes readable and writable from Python code, and the depth attribute readable but not writable.

Note

You can only expose simple C types, such as ints, floats, and strings, for Python access. You can also expose Python-valued attributes.

Note

Also the public and readonly options apply only to Python access, not direct access. All the attributes of an extension type are always readable and writable by C-level access.

Dynamic Attributes

It is not possible to add attributes to an extension type at runtime by default. You have two ways of avoiding this limitation, both add an overhead when a method is called from Python code. Especially when calling hybrid methods declared with cpdef in .pyx files or with the @ccall decorator.

The first approach is to create a Python subclass:

@cython.cclass
class Animal:

    number_of_legs: cython.int

    def __cinit__(self, number_of_legs: cython.int):
        self.number_of_legs = number_of_legs


class ExtendableAnimal(Animal):  # Note that we use class, not cdef class
    pass


dog = ExtendableAnimal(4)
dog.has_tail = True

Declaring a __dict__ attribute is the second way of enabling dynamic attributes:

@cython.cclass
class Animal:

    number_of_legs: cython.int
    __dict__: dict

    def __cinit__(self, number_of_legs: cython.int):
        self.number_of_legs = number_of_legs


dog = Animal(4)
dog.has_tail = True

Type declarations

Before you can directly access the attributes of an extension type, the Cython compiler must know that you have an instance of that type, and not just a generic Python object. It knows this already in the case of the self parameter of the methods of that type, but in other cases you will have to use a type declaration.

For example, in the following function:

@cython.cfunc
def widen_shrubbery(sh, extra_width): # BAD
    sh.width = sh.width + extra_width

because the sh parameter hasn’t been given a type, the width attribute will be accessed by a Python attribute lookup. If the attribute has been declared public or readonly then this will work, but it will be very inefficient. If the attribute is private, it will not work at all – the code will compile, but an attribute error will be raised at run time.

The solution is to declare sh as being of type Shrubbery, as follows:

import cython
from cython.cimports.my_module import Shrubbery

@cython.cfunc
def widen_shrubbery(sh: Shrubbery, extra_width):
    sh.width = sh.width + extra_width

Now the Cython compiler knows that sh has a C attribute called width and will generate code to access it directly and efficiently. The same consideration applies to local variables, for example:

import cython
from cython.cimports.my_module import Shrubbery

@cython.cfunc
def another_shrubbery(sh1: Shrubbery) -> Shrubbery:
    sh2: Shrubbery
    sh2 = Shrubbery()
    sh2.width = sh1.width
    sh2.height = sh1.height
    return sh2

Note

Here, we cimport the class Shrubbery (using the cimport statement or importing from special cython.cimports package), and this is necessary to declare the type at compile time. To be able to cimport an extension type, we split the class definition into two parts, one in a definition file and the other in the corresponding implementation file. You should read Sharing Extension Types to learn to do that.

Type Testing and Casting

Suppose I have a method quest() which returns an object of type Shrubbery. To access its width I could write:

sh: Shrubbery = quest()
print(sh.width)

which requires the use of a local variable and performs a type test on assignment. If you know the return value of quest() will be of type Shrubbery you can use a cast to write:

print( cython.cast(Shrubbery, quest()).width )

This may be dangerous if quest() is not actually a Shrubbery, as it will try to access width as a C struct member which may not exist. At the C level, rather than raising an AttributeError, either an nonsensical result will be returned (interpreting whatever data is at that address as an int) or a segfault may result from trying to access invalid memory. Instead, one can write:

print( cython.cast(Shrubbery, quest(), typecheck=True).width )

which performs a type check (possibly raising a TypeError) before making the cast and allowing the code to proceed.

To explicitly test the type of an object, use the isinstance() builtin function. For known builtin or extension types, Cython translates these into a fast and safe type check that ignores changes to the object’s __class__ attribute etc., so that after a successful isinstance() test, code can rely on the expected C structure of the extension type and its C-level attributes (stored in the object’s C struct) and cdef/@cfunc methods.

Extension types and None

Cython handles None values differently in C-like type declarations and when Python annotations are used.

In cdef declarations and C-like function argument declarations (func(list x)), when you declare an argument or C variable as having an extension or Python builtin type, Cython will allow it to take on the value None as well as values of its declared type. This is analogous to the way a C pointer can take on the value NULL, and you need to exercise the same caution because of it. There is no problem as long as you are performing Python operations on it, because full dynamic type checking will be applied. However, when you access C attributes of an extension type (as in the widen_shrubbery function above), it’s up to you to make sure the reference you’re using is not None – in the interests of efficiency, Cython does not check this.

With the C-like declaration syntax, you need to be particularly careful when exposing Python functions which take extension types as arguments:

def widen_shrubbery(Shrubbery sh, extra_width): # This is
    sh.width = sh.width + extra_width           # dangerous!

The users of our module could crash it by passing None for the sh parameter.

As in Python, whenever it is unclear whether a variable can be None, but the code requires a non-None value, an explicit check can help:

def widen_shrubbery(Shrubbery sh, extra_width):
    if sh is None:
        raise TypeError
    sh.width = sh.width + extra_width

but since this is anticipated to be such a frequent requirement, Cython language provides a more convenient way. Parameters of a Python function declared as an extension type can have a not None clause:

def widen_shrubbery(Shrubbery sh not None, extra_width):
    sh.width = sh.width + extra_width

Now the function will automatically check that sh is not None along with checking that it has the right type.

When annotations are used, the behaviour follows the Python typing semantics of PEP-484 instead. The value None is not allowed when a variable is annotated only with its plain type:

def widen_shrubbery(sh: Shrubbery, extra_width):  # TypeError is raised
    sh.width = sh.width + extra_width             # when sh is None

To also allow None, typing.Optional[ ] must be used explicitly. For function arguments, this is also automatically allowed when they have a default argument of None`, e.g. func(x: list = None) does not require typing.Optional:

import typing
def widen_shrubbery(sh: typing.Optional[Shrubbery], extra_width):
    if sh is None:
        # We want to raise a custom exception in case of a None value.
        raise ValueError
    sh.width = sh.width + extra_width

The upside of using annotations here is that they are safe by default because you need to explicitly allow None values for them.

Note

The not None and typing.Optional can only be used in Python functions (defined with def and without @cython.cfunc decorator) and not C functions (defined with cdef or decorated using @cython.cfunc). If you need to check whether a parameter to a C function is None, you will need to do it yourself.

Note

Some more things:

  • The self parameter of a method of an extension type is guaranteed never to be None.

  • When comparing a value with None, keep in mind that, if x is a Python object, x is None and x is not None are very efficient because they translate directly to C pointer comparisons, whereas x == None and x != None, or simply using x as a boolean value (as in if x: ...) will invoke Python operations and therefore be much slower.

Special methods

Although the principles are similar, there are substantial differences between many of the __xxx__() special methods of extension types and their Python counterparts. There is a separate page devoted to this subject, and you should read it carefully before attempting to use any special methods in your extension types.

Properties

You can declare properties in an extension class using the same syntax as in ordinary Python code:

@cython.cclass
class Spam:
    @property
    def cheese(self):
        # This is called when the property is read.
        ...

    @cheese.setter
    def cheese(self, value):
        # This is called when the property is written.
        ...

    @cheese.deleter
    def cheese(self):
        # This is called when the property is deleted.

There is also a special (deprecated) legacy syntax for defining properties in an extension class:

cdef class Spam:

    property cheese:

        "A doc string can go here."

        def __get__(self):
            # This is called when the property is read.
            ...

        def __set__(self, value):
            # This is called when the property is written.
            ...

        def __del__(self):
            # This is called when the property is deleted.

The __get__(), __set__() and __del__() methods are all optional; if they are omitted, an exception will be raised when the corresponding operation is attempted.

Here’s a complete example. It defines a property which adds to a list each time it is written to, returns the list when it is read, and empties the list when it is deleted:

import cython

@cython.cclass
class CheeseShop:

    cheeses: object

    def __cinit__(self):
        self.cheeses = []

    @property
    def cheese(self):
        return "We don't have: %s" % self.cheeses

    @cheese.setter
    def cheese(self, value):
        self.cheeses.append(value)

    @cheese.deleter
    def cheese(self):
        del self.cheeses[:]

# Test input
from cheesy import CheeseShop

shop = CheeseShop()
print(shop.cheese)

shop.cheese = "camembert"
print(shop.cheese)

shop.cheese = "cheddar"
print(shop.cheese)

del shop.cheese
print(shop.cheese)
# Test output
We don't have: []
We don't have: ['camembert']
We don't have: ['camembert', 'cheddar']
We don't have: []

C methods

Extension types can have C methods as well as Python methods. Like C functions, C methods are declared using

  • cdef instead of def or @cfunc decorator for C methods, or

  • cpdef instead of def or @ccall decorator for hybrid methods.

C methods are “virtual”, and may be overridden in derived extension types. In addition, cpdef/@ccall methods can even be overridden by Python methods when called as C method. This adds a little to their calling overhead compared to a cdef/@cfunc method:

import cython

@cython.cclass
class Parrot:

    @cython.cfunc
    def describe(self) -> cython.void:
        print("This parrot is resting.")

@cython.cclass
class Norwegian(Parrot):

    @cython.cfunc
    def describe(self) -> cython.void:
        Parrot.describe(self)
        print("Lovely plumage!")

cython.declare(p1=Parrot, p2=Parrot)
p1 = Parrot()
p2 = Norwegian()
print("p2:")
p2.describe()
# Output
p1:
This parrot is resting.
p2:
This parrot is resting.
Lovely plumage!

The above example also illustrates that a C method can call an inherited C method using the usual Python technique, i.e.:

Parrot.describe(self)

cdef/@ccall methods can be declared static by using the @staticmethod decorator. This can be especially useful for constructing classes that take non-Python compatible types:

import cython
from cython.cimports.libc.stdlib import free

@cython.cclass
class OwnedPointer:
    ptr: cython.pointer(cython.void)

    def __dealloc__(self):
        if self.ptr is not cython.NULL:
            free(self.ptr)

    @staticmethod
    @cython.cfunc
    def create(ptr: cython.pointer(cython.void)):
        p = OwnedPointer()
        p.ptr = ptr
        return p

Note

Cython currently does not support decorating cdef/@ccall methods with the @classmethod decorator.

Subclassing

If an extension type inherits from other types, the first base class must be a built-in type or another extension type:

@cython.cclass
class Parrot:
    ...

@cython.cclass
class Norwegian(Parrot):
    ...

A complete definition of the base type must be available to Cython, so if the base type is a built-in type, it must have been previously declared as an extern extension type. If the base type is defined in another Cython module, it must either be declared as an extern extension type or imported using the cimport statement or importing from the special cython.cimports package.

Multiple inheritance is supported, however the second and subsequent base classes must be an ordinary Python class (not an extension type or a built-in type).

Cython extension types can also be subclassed in Python. A Python class can inherit from multiple extension types provided that the usual Python rules for multiple inheritance are followed (i.e. the C layouts of all the base classes must be compatible).

There is a way to prevent extension types from being subtyped in Python. This is done via the final directive, usually set on an extension type or C method using a decorator:

import cython

@cython.final
@cython.cclass
class Parrot:
   def describe(self): pass

@cython.cclass
class Lizard:

   @cython.final
   @cython.cfunc
   def done(self): pass

Trying to create a Python subclass from a final type or overriding a final method will raise a TypeError at runtime. Cython will also prevent subtyping a final type or overriding a final method inside of the same module, i.e. creating an extension type that uses a final type as its base type will fail at compile time. Note, however, that this restriction does not currently propagate to other extension modules, so Cython is unable to prevent final extension types from being subtyped at the C level by foreign code.

Forward-declaring extension types

Extension types can be forward-declared, like struct and union types. This is usually not necessary and violates the DRY principle (Don’t Repeat Yourself).

If you are forward-declaring an extension type that has a base class, you must specify the base class in both the forward declaration and its subsequent definition, for example,:

cdef class A(B)

...

cdef class A(B):
    # attributes and methods

Fast instantiation

Cython provides two ways to speed up the instantiation of extension types. The first one is a direct call to the __new__() special static method, as known from Python. For an extension type Penguin, you could use the following code:

import cython

@cython.cclass
class Penguin:
    food: object

    def __cinit__(self, food):
        self.food = food

    def __init__(self, food):
        print("eating!")

normal_penguin = Penguin('fish')
fast_penguin = Penguin.__new__(Penguin, 'wheat')  # note: not calling __init__() !

Note that the path through __new__() will not call the type’s __init__() method (again, as known from Python). Thus, in the example above, the first instantiation will print eating!, but the second will not. This is only one of the reasons why the __cinit__() method is safer than the normal __init__() method for initialising extension types and bringing them into a correct and safe state. See the Initialisation Methods Section about the differences.

The second performance improvement applies to types that are often created and deleted in a row, so that they can benefit from a freelist. Cython provides the decorator @cython.freelist(N) for this, which creates a statically sized freelist of N instances for a given type. Example:

import cython

@cython.freelist(8)
@cython.cclass
class Penguin:
    food: object
    def __cinit__(self, food):
        self.food = food

penguin = Penguin('fish 1')
penguin = None
penguin = Penguin('fish 2')  # does not need to allocate memory!

Instantiation from existing C/C++ pointers

It is quite common to want to instantiate an extension class from an existing (pointer to a) data structure, often as returned by external C/C++ functions.

As extension classes can only accept Python objects as arguments in their constructors, this necessitates the use of factory functions or factory methods. For example:

import cython
from cython.cimports.libc.stdlib import malloc, free

# Example C struct
my_c_struct = cython.struct(
    a = cython.int,
    b = cython.int,
)

@cython.cclass
class WrapperClass:
    """A wrapper class for a C/C++ data structure"""
    _ptr: cython.pointer(my_c_struct)
    ptr_owner: cython.bint

    def __cinit__(self):
        self.ptr_owner = False

    def __dealloc__(self):
        # De-allocate if not null and flag is set
        if self._ptr is not cython.NULL and self.ptr_owner is True:
            free(self._ptr)
            self._ptr = cython.NULL

    def __init__(self):
        # Prevent accidental instantiation from normal Python code
        # since we cannot pass a struct pointer into a Python constructor.
        raise TypeError("This class cannot be instantiated directly.")

    # Extension class properties
    @property
    def a(self):
        return self._ptr.a if self._ptr is not cython.NULL else None

    @property
    def b(self):
        return self._ptr.b if self._ptr is not cython.NULL else None

    @staticmethod
    @cython.cfunc
    def from_ptr(_ptr: cython.pointer(my_c_struct), owner: cython.bint=False) -> WrapperClass:
        """Factory function to create WrapperClass objects from
        given my_c_struct pointer.

        Setting ``owner`` flag to ``True`` causes
        the extension type to ``free`` the structure pointed to by ``_ptr``
        when the wrapper object is deallocated."""
        # Fast call to __new__() that bypasses the __init__() constructor.
        wrapper: WrapperClass  = WrapperClass.__new__(WrapperClass)
        wrapper._ptr = _ptr
        wrapper.ptr_owner = owner
        return wrapper

    @staticmethod
    @cython.cfunc
    def new_struct() -> WrapperClass:
        """Factory function to create WrapperClass objects with
        newly allocated my_c_struct"""
        _ptr: cython.pointer(my_c_struct) = cython.cast(
                cython.pointer(my_c_struct), malloc(cython.sizeof(my_c_struct)))
        if _ptr is cython.NULL:
            raise MemoryError
        _ptr.a = 0
        _ptr.b = 0
        return WrapperClass.from_ptr(_ptr, owner=True)

To then create a WrapperClass object from an existing my_c_struct pointer, WrapperClass.from_ptr(ptr) can be used in Cython code. To allocate a new structure and wrap it at the same time, WrapperClass.new_struct can be used instead.

It is possible to create multiple Python objects all from the same pointer which point to the same in-memory data, if that is wanted, though care must be taken when de-allocating as can be seen above. Additionally, the ptr_owner flag can be used to control which WrapperClass object owns the pointer and is responsible for de-allocation - this is set to False by default in the example and can be enabled by calling from_ptr(ptr, owner=True).

The GIL must not be released in __dealloc__ either, or another lock used if it is, in such cases or race conditions can occur with multiple de-allocations.

Being a part of the object constructor, the __cinit__ method has a Python signature, which makes it unable to accept a my_c_struct pointer as an argument.

Attempts to use pointers in a Python signature will result in errors like:

Cannot convert 'my_c_struct *' to Python object

This is because Cython cannot automatically convert a pointer to a Python object, unlike with native types like int.

Note that for native types, Cython will copy the value and create a new Python object while in the above case, data is not copied and deallocating memory is a responsibility of the extension class.

Making extension types weak-referenceable

By default, extension types do not support having weak references made to them. You can enable weak referencing by declaring a C attribute of type object called __weakref__. For example:

@cython.cclass
class ExplodingAnimal:
    """This animal will self-destruct when it is
    no longer strongly referenced."""

    __weakref__: object

Controlling deallocation and garbage collection in CPython

Note

This section only applies to the usual CPython implementation of Python. Other implementations like PyPy work differently.

Introduction

First of all, it is good to understand that there are two ways to trigger deallocation of Python objects in CPython: CPython uses reference counting for all objects and any object with a reference count of zero is immediately deallocated. This is the most common way of deallocating an object. For example, consider

>>> x = "foo"
>>> x = "bar"

After executing the second line, the string "foo" is no longer referenced, so it is deallocated. This is done using the tp_dealloc slot, which can be customized in Cython by implementing __dealloc__.

The second mechanism is the cyclic garbage collector. This is meant to resolve cyclic reference cycles such as

>>> class Object:
...     pass
>>> def make_cycle():
...     x = Object()
...     y = [x]
...     x.attr = y

When calling make_cycle, a reference cycle is created since x references y and vice versa. Even though neither x or y are accessible after make_cycle returns, both have a reference count of 1, so they are not immediately deallocated. At regular times, the garbage collector runs, which will notice the reference cycle (using the tp_traverse slot) and break it. Breaking a reference cycle means taking an object in the cycle and removing all references from it to other Python objects (we call this clearing an object). Clearing is almost the same as deallocating, except that the actual object is not yet freed. For x in the example above, the attributes of x would be removed from x.

Note that it suffices to clear just one object in the reference cycle, since there is no longer a cycle after clearing one object. Once the cycle is broken, the usual refcount-based deallocation will actually remove the objects from memory. Clearing is implemented in the tp_clear slot. As we just explained, it is sufficient that one object in the cycle implements tp_clear.

Enabling the deallocation trashcan

In CPython, it is possible to create deeply recursive objects. For example:

>>> L = None
>>> for i in range(2**20):
...     L = [L]

Now imagine that we delete the final L. Then L deallocates L[0], which deallocates L[0][0] and so on until we reach a recursion depth of 2**20. This deallocation is done in C and such a deep recursion will likely overflow the C call stack, crashing Python.

CPython invented a mechanism for this called the trashcan. It limits the recursion depth of deallocations by delaying some deallocations.

By default, Cython extension types do not use the trashcan but it can be enabled by setting the trashcan directive to True. For example:

import cython
@cython.trashcan(True)
@cython.cclass
class Object:
    __dict__: dict

Trashcan usage is inherited by subclasses (unless explicitly disabled by @cython.trashcan(False)). Some builtin types like list use the trashcan, so subclasses of it use the trashcan by default.

Disabling cycle breaking (tp_clear)

By default, each extension type will support the cyclic garbage collector of CPython. If any Python objects can be referenced, Cython will automatically generate the tp_traverse and tp_clear slots. This is usually what you want.

There is at least one reason why this might not be what you want: If you need to cleanup some external resources in the __dealloc__ special function and your object happened to be in a reference cycle, the garbage collector may have triggered a call to tp_clear to clear the object (see Introduction).

In that case, any object references have vanished when __dealloc__ is called. Now your cleanup code lost access to the objects it has to clean up. To fix this, you can disable clearing instances of a specific class by using the no_gc_clear directive:

@cython.no_gc_clear
@cython.cclass
class DBCursor:
    conn: DBConnection
    raw_cursor: cython.pointer(DBAPI_Cursor)
    # ...
    def __dealloc__(self):
        DBAPI_close_cursor(self.conn.raw_conn, self.raw_cursor)

This example tries to close a cursor via a database connection when the Python object is destroyed. The DBConnection object is kept alive by the reference from DBCursor. But if a cursor happens to be in a reference cycle, the garbage collector may delete the database connection reference, which makes it impossible to clean up the cursor.

If you use no_gc_clear, it is important that any given reference cycle contains at least one object without no_gc_clear. Otherwise, the cycle cannot be broken, which is a memory leak.

Disabling cyclic garbage collection

In rare cases, extension types can be guaranteed not to participate in cycles, but the compiler won’t be able to prove this. This would be the case if the class can never reference itself, even indirectly. In that case, you can manually disable cycle collection by using the no_gc directive, but beware that doing so when in fact the extension type can participate in cycles could cause memory leaks:

@cython.no_gc
@cython.cclass
class UserInfo:
    name: str
    addresses: tuple

If you can be sure addresses will contain only references to strings, the above would be safe, and it may yield a significant speedup, depending on your usage pattern.

Controlling pickling

By default, Cython will generate a __reduce__() method to allow pickling an extension type if and only if each of its members are convertible to Python and it has no __cinit__ method. To require this behavior (i.e. throw an error at compile time if a class cannot be pickled) decorate the class with @cython.auto_pickle(True). One can also annotate with @cython.auto_pickle(False) to get the old behavior of not generating a __reduce__ method in any case.

Manually implementing a __reduce__ or __reduce_ex__ method will also disable this auto-generation and can be used to support pickling of more complicated types.

Public and external extension types

Extension types can be declared extern or public. An extern extension type declaration makes an extension type defined in external C code available to a Cython module. A public extension type declaration makes an extension type defined in a Cython module available to external C code.

Note

Cython currently does not support Extension types declared as extern or public in Pure Python mode. This is not considered an issue since public/extern extension types are most commonly declared in .pxd files and not in .py files.

External extension types

An extern extension type allows you to gain access to the internals of Python objects defined in the Python core or in a non-Cython extension module.

Note

In previous versions of Pyrex, extern extension types were also used to reference extension types defined in another Pyrex module. While you can still do that, Cython provides a better mechanism for this. See Sharing Declarations Between Cython Modules.

Here is an example which will let you get at the C-level members of the built-in complex object:

from __future__ import print_function

cdef extern from "complexobject.h":

    struct Py_complex:
        double real
        double imag

    ctypedef class __builtin__.complex [object PyComplexObject]:
        cdef Py_complex cval

# A function which uses the above type
def spam(complex c):
    print("Real:", c.cval.real)
    print("Imag:", c.cval.imag)

Note

Some important things:

  1. In this example, ctypedef class has been used. This is because, in the Python header files, the PyComplexObject struct is declared with:

    typedef struct {
        ...
    } PyComplexObject;
    

    At runtime, a check will be performed when importing the Cython c-extension module that __builtin__.complex’s tp_basicsize matches sizeof(`PyComplexObject). This check can fail if the Cython c-extension module was compiled with one version of the complexobject.h header but imported into a Python with a changed header. This check can be tweaked by using check_size in the name specification clause.

  2. As well as the name of the extension type, the module in which its type object can be found is also specified. See the implicit importing section below.

  3. When declaring an external extension type, you don’t declare any methods. Declaration of methods is not required in order to call them, because the calls are Python method calls. Also, as with struct and union, if your extension class declaration is inside a cdef extern from block, you only need to declare those C members which you wish to access.

Name specification clause

The part of the class declaration in square brackets is a special feature only available for extern or public extension types. The full form of this clause is:

[object object_struct_name, type type_object_name, check_size cs_option]

Where:

  • object_struct_name is the name to assume for the type’s C struct.

  • type_object_name is the name to assume for the type’s statically declared type object.

  • cs_option is warn (the default), error, or ignore and is only used for external extension types. If error, the sizeof(object_struct) that was found at compile time must match the type’s runtime tp_basicsize exactly, otherwise the module import will fail with an error. If warn or ignore, the object_struct is allowed to be smaller than the type’s tp_basicsize, which indicates the runtime type may be part of an updated module, and that the external module’s developers extended the object in a backward-compatible fashion (only adding new fields to the end of the object). If warn, a warning will be emitted in this case.

The clauses can be written in any order.

If the extension type declaration is inside a cdef extern from block, the object clause is required, because Cython must be able to generate code that is compatible with the declarations in the header file. Otherwise, for extern extension types, the object clause is optional.

For public extension types, the object and type clauses are both required, because Cython must be able to generate code that is compatible with external C code.

Attribute name matching and aliasing

Sometimes the type’s C struct as specified in object_struct_name may use different labels for the fields than those in the PyTypeObject. This can easily happen in hand-coded C extensions where the PyTypeObject_Foo has a getter method, but the name does not match the name in the PyFooObject. In NumPy, for instance, python-level dtype.itemsize is a getter for the C struct field elsize. Cython supports aliasing field names so that one can write dtype.itemsize in Cython code which will be compiled into direct access of the C struct field, without going through a C-API equivalent of dtype.__getattr__('itemsize').

For example, we may have an extension module foo_extension:

cdef class Foo:
    cdef public int field0, field1, field2;

    def __init__(self, f0, f1, f2):
        self.field0 = f0
        self.field1 = f1
        self.field2 = f2

but a C struct in a file foo_nominal.h:

typedef struct {
     PyObject_HEAD
     int f0;
     int f1;
     int f2;
 } FooStructNominal;

Note that the struct uses f0, f1, f2 but they are field0, field1, and field2 in Foo. We are given this situation, including a header file with that struct, and we wish to write a function to sum the values. If we write an extension module wrapper:

cdef extern from "foo_nominal.h":

    ctypedef class foo_extension.Foo [object FooStructNominal]:
        cdef:
            int field0
            int field1
            int feild2

def sum(Foo f):
    return f.field0 + f.field1 + f.field2

then wrapper.sum(f) (where f = foo_extension.Foo(1, 2, 3)) will still use the C-API equivalent of:

return f.__getattr__('field0') +
       f.__getattr__('field1') +
       f.__getattr__('field1')

instead of the desired C equivalent of return f->f0 + f->f1 + f->f2. We can alias the fields by using:

cdef extern from "foo_nominal.h":

    ctypedef class foo_extension.Foo [object FooStructNominal]:
        cdef:
            int field0 "f0"
            int field1 "f1"
            int field2 "f2"

def sum(Foo f) except -1:
    return f.field0 + f.field1 + f.field2

and now Cython will replace the slow __getattr__ with direct C access to the FooStructNominal fields. This is useful when directly processing Python code. No changes to Python need be made to achieve significant speedups, even though the field names in Python and C are different. Of course, one should make sure the fields are equivalent.

C inline properties

Similar to Python property attributes, Cython provides a way to declare C-level properties on external extension types. This is often used to shadow Python attributes through faster C level data access, but can also be used to add certain functionality to existing types when using them from Cython. The declarations must use cdef inline.

For example, the above complex type could also be declared like this:

cdef extern from "complexobject.h":

    struct Py_complex:
        double real
        double imag

    ctypedef class __builtin__.complex [object PyComplexObject]:
        cdef Py_complex cval

        @property
        cdef inline double real(self):
            return self.cval.real

        @property
        cdef inline double imag(self):
            return self.cval.imag


def cprint(complex c):
    print(f"{c.real :.4f}{c.imag :+.4f}j")  # uses C calls to the above property methods.

Implicit importing

Cython requires you to include a module name in an extern extension class declaration, for example,:

cdef extern class MyModule.Spam:
    ...

The type object will be implicitly imported from the specified module and bound to the corresponding name in this module. In other words, in this example an implicit:

from MyModule import Spam

statement will be executed at module load time.

The module name can be a dotted name to refer to a module inside a package hierarchy, for example,:

cdef extern class My.Nested.Package.Spam:
    ...

You can also specify an alternative name under which to import the type using an as clause, for example,:

cdef extern class My.Nested.Package.Spam as Yummy:
   ...

which corresponds to the implicit import statement:

from My.Nested.Package import Spam as Yummy

Type names vs. constructor names

Inside a Cython module, the name of an extension type serves two distinct purposes. When used in an expression, it refers to a module-level global variable holding the type’s constructor (i.e. its type-object). However, it can also be used as a C type name to declare variables, arguments and return values of that type.

When you declare:

cdef extern class MyModule.Spam:
    ...

the name Spam serves both these roles. There may be other names by which you can refer to the constructor, but only Spam can be used as a type name. For example, if you were to explicitly import MyModule, you could use MyModule.Spam() to create a Spam instance, but you wouldn’t be able to use MyModule.Spam as a type name.

When an as clause is used, the name specified in the as clause also takes over both roles. So if you declare:

cdef extern class MyModule.Spam as Yummy:
    ...

then Yummy becomes both the type name and a name for the constructor. Again, there are other ways that you could get hold of the constructor, but only Yummy is usable as a type name.

Public extension types

An extension type can be declared public, in which case a .h file is generated containing declarations for its object struct and type object. By including the .h file in external C code that you write, that code can access the attributes of the extension type.

Dataclass extension types

Cython supports extension types that behave like the dataclasses defined in the Python 3.7+ standard library. The main benefit of using a dataclass is that it can auto-generate simple __init__, __repr__ and comparison functions. The Cython implementation behaves as much like the Python standard library implementation as possible and therefore the documentation here only briefly outlines the differences - if you plan on using them then please read the documentation for the standard library module.

Dataclasses can be declared using the @cython.dataclasses.dataclass decorator on a Cython extension type. @cython.dataclasses.dataclass can only be applied to extension types (types marked cdef or created with the cython.cclass decorator) and not to regular classes. If you need to define special properties on a field then use cython.dataclasses.field

import cython
try:
    import typing
    import dataclasses
except ImportError:
    pass  # The modules don't actually have to exists for Cython to use them as annotations

@cython.dataclasses.dataclass
@cython.cclass
class MyDataclass:
    # fields can be declared using annotations
    a: cython.int = 0
    b: double = cython.dataclasses.field(default_factory = lambda: 10, repr=False)


    c: str = 'hello'


    # typing.InitVar and typing.ClassVar also work
    d: dataclasses.InitVar[double] = 5
    e: typing.ClassVar[list] = []

You may use C-level types such as structs, pointers, or C++ classes. However, you may find these types are not compatible with the auto-generated special methods - for example if they cannot be converted from a Python type they cannot be passed to a constructor, and so you must use a default_factory to initialize them. Like with the Python implementation, you can also control which special functions an attribute is used in using field().