Anaconda Enterprise: Documentation

# Pure Python Mode

In some cases, it’s desirable to speed up Python code without losing the ability to run it with the Python interpreter. While pure Python scripts can be compiled with Cython, it usually results only in a speed gain of about 20%-50%.

To go beyond that, Cython provides language constructs to add static typing and cythonic functionalities to a Python module to make it run much faster when compiled, while still allowing it to be interpreted. This is accomplished either via an augmenting `.pxd` file, or via special functions and decorators available after importing the magic `cython` module.

Although it is not typically recommended over writing straight Cython code in a `.pyx` file, there are legitimate reasons to do this - easier testing, collaboration with pure Python developers, etc. In pure mode, you are more or less restricted to code that can be expressed (or at least emulated) in Python, plus static type declarations. Anything beyond that can only be done in .pyx files with extended language syntax, because it depends on features of the Cython compiler.

## Augmenting .pxd

Using an augmenting `.pxd` allows to let the original `.py` file completely untouched. On the other hand, one needs to maintain both the `.pxd` and the `.py` to keep them in sync.

While declarations in a `.pyx` file must correspond exactly with those of a `.pxd` file with the same name (and any contradiction results in a compile time error, see pxd files), the untyped definitions in a `.py` file can be overridden and augmented with static types by the more specific ones present in a `.pxd`.

If a `.pxd` file is found with the same name as the `.py` file being compiled, it will be searched for `cdef` classes and `cdef`/`cpdef` functions and methods. The compiler will then convert the corresponding classes/functions/methods in the `.py` file to be of the declared type. Thus if one has a file `A.py`:

    def myfunction(x, y=2):
        a = x-y
        return a + x * y

    def _helper(a):
        return a + 1

    class A:
        def __init__(self, b=0):
            self.a = 3
            self.b = b

        def foo(self, x):
            print x + _helper(1.0)

and adds `A.pxd`:

    cpdef int myfunction(int x, int y=*)
    cdef double _helper(double a)

    cdef class A:
        cdef public int a,b
        cpdef foo(self, double x)

then Cython will compile the `A.py` as if it had been written as follows:

    cpdef int myfunction(int x, int y=2):
        a = x-y
        return a + x * y

    cdef double _helper(double a):
        return a + 1

    cdef class A:
        cdef public int a,b
        def __init__(self, b=0):
            self.a = 3
            self.b = b

        cpdef foo(self, double x):
            print x + _helper(1.0)

Notice how in order to provide the Python wrappers to the definitions in the `.pxd`, that is, to be accessible from Python,

- Python visible function signatures must be declared as cpdef (with default arguments replaced by a * to avoid repetition):

      cpdef int myfunction(int x, int y=*)

- C function signatures of internal functions can be declared as cdef:

      cdef double _helper(double a)

- cdef classes (extension types) are declared as cdef class;

- cdef class attributes must be declared as cdef public if read/write Python access is needed, cdef readonly for read-only Python access, or plain cdef for internal C level attributes;

- cdef class methods must be declared as cpdef for Python visible methods or cdef for internal C methods.

In the example above, the type of the local variable a in myfunction() is not fixed and will thus be a Python object. To statically type it, one can use Cython’s `@cython.locals` decorator (see Magic Attributes, and Magic Attributes within the .pxd).

Normal Python (`def`) functions cannot be declared in `.pxd` files. It is therefore currently impossible to override the types of plain Python functions in `.pxd` files, e.g. to override types of their local variables. In most cases, declaring them as cpdef will work as expected.

## Magic Attributes

Special decorators are available from the magic `cython` module that can be used to add static typing within the Python file, while being ignored by the interpreter.

This option adds the `cython` module dependency to the original code, but does not require to maintain a supplementary `.pxd` file. Cython provides a fake version of this module as Cython.Shadow, which is available as cython.py when Cython is installed, but can be copied to be used by other modules when Cython is not installed.

### “Compiled” switch

- `compiled` is a special variable which is set to `True` when the compiler runs, and `False` in the interpreter. Thus, the code

      if cython.compiled:
          print("Yep, I'm compiled.")
      else:
          print("Just a lowly interpreted script.")

  will behave differently depending on whether or not the code is executed as a compiled extension (`.so`/`.pyd`) module or a plain `.py` file.

### Static typing

- `cython.declare` declares a typed variable in the current scope, which can be used in place of the `cdef type var [= value]` construct. This has two forms, the first as an assignment (useful as it creates a declaration in interpreted mode as well):

      x = cython.declare(cython.int)              # cdef int x
      y = cython.declare(cython.double, 0.57721)  # cdef double y = 0.57721

  and the second mode as a simple function call:

      cython.declare(x=cython.int, y=cython.double)  # cdef int x; cdef double y

  It can also be used to type class constructors:

      class A:
          cython.declare(a=cython.int, b=cython.int)
          def __init__(self, b=0):
              self.a = 3
              self.b = b

  And even to define extension type private, readonly and public attributes:

      @cython.cclass
      class A:
          cython.declare(a=cython.int, b=cython.int)
          c = cython.declare(cython.int, visibility='public')
          d = cython.declare(cython.int, 5)  # private by default.
          e = cython.declare(cython.int, 5, visibility='readonly')

- `@cython.locals` is a decorator that is used to specify the types of local variables in the function body (including the arguments):

      @cython.locals(a=cython.double, b=cython.double, n=cython.p_double)
      def foo(a, b, x, y):
          n = a*b
          ...

- `@cython.returns(<type>)` specifies the function’s return type.

- Starting with Cython 0.21, Python signature annotations can be used to declare argument types. Cython recognises three ways to do this, as shown in the following example. Note that it currently needs to be enabled explicitly with the directive `annotation_typing=True`. This might change in a later version.

      # cython: annotation_typing=True

      def func(plain_python_type: dict,
               named_python_type: 'dict',
               explicit_python_type: {'type': dict},
               explicit_c_type: {'ctype': 'int'}):
          ...

### C types

There are numerous types built into the Cython module. It provides all the standard C types, namely `char`, `short`, `int`, `long`, `longlong` as well as their unsigned versions `uchar`, `ushort`, `uint`, `ulong`, `ulonglong`. The special `bint` type is used for C boolean values and `Py_ssize_t` for (signed) sizes of Python containers.

For each type, there are pointer types `p_int`, `pp_int`, etc., up to three levels deep in interpreted mode, and infinitely deep in compiled mode. Further pointer types can be constructed with `cython.pointer(cython.int)`, and arrays as `cython.int[10]`. A limited attempt is made to emulate these more complex types, but only so much can be done from the Python language.

The Python types int, long and bool are interpreted as C `int`, `long` and `bint` respectively. Also, the Python builtin types `list`, `dict`, `tuple`, etc. may be used, as well as any user defined types.

Typed C-tuples can be declared as a tuple of C types.

### Extension types and cdef functions

- The class decorator `@cython.cclass` creates a `cdef class`.
- The function/method decorator `@cython.cfunc` creates a `cdef` function.
- `@cython.ccall` creates a `cpdef` function, i.e. one that Cython code can call at the C level.
- `@cython.locals` declares local variables (see above). It can also be used to declare types for arguments, i.e. the local variables that are used in the signature.
- `@cython.inline` is the equivalent of the C `inline` modifier.

Here is an example of a `cdef` function:

    @cython.cfunc
    @cython.returns(cython.bint)
    @cython.locals(a=cython.int, b=cython.int)
    def c_compare(a,b):
        return a == b

### Further Cython functions and declarations

- `address` is used in place of the `&` operator:

      cython.declare(x=cython.int, x_ptr=cython.p_int)
      x_ptr = cython.address(x)

- `sizeof` emulates the sizeof operator. It can take both types and expressions.

      cython.declare(n=cython.longlong)
      print cython.sizeof(cython.longlong)
      print cython.sizeof(n)

- `struct` can be used to create struct types.:

      MyStruct = cython.struct(x=cython.int, y=cython.int, data=cython.double)
      a = cython.declare(MyStruct)

  is equivalent to the code:

      cdef struct MyStruct:
          int x
          int y
          double data

      cdef MyStruct a

- `union` creates union types with exactly the same syntax as `struct`.

- `typedef` defines a type under a given name:

      T = cython.typedef(cython.p_int)   # ctypedef int* T

- `cast` will (unsafely) reinterpret an expression type. `cython.cast(T, t)` is equivalent to `<T>t`. The first attribute must be a type, the second is the expression to cast. Specifying the optional keyword argument `typecheck=True` has the semantics of `<T?>t`.

      t1 = cython.cast(T, t)
      t2 = cython.cast(T, t, typecheck=True)

### Magic Attributes within the .pxd

The special cython module can also be imported and used within the augmenting `.pxd` file. For example, the following Python file `dostuff.py`:

    def dostuff(n):
        t = 0
        for i in range(n):
            t += i
        return t

can be augmented with the following `.pxd` file `dostuff.pxd`:

    import cython

    @cython.locals(t = cython.int, i = cython.int)
    cpdef int dostuff(int n)

The `cython.declare()` function can be used to specify types for global variables in the augmenting `.pxd` file.

## Tips and Tricks

### Calling C functions

Normally, it isn’t possible to call C functions in pure Python mode as there is no general way to support it in normal (uncompiled) Python. However, in cases where an equivalent Python function exists, this can be achieved by combining C function coercion with a conditional import as follows:

    # in mymodule.pxd:

    # declare a C function as "cpdef" to export it to the module
    cdef extern from "math.h":
        cpdef double sin(double x)

    # in mymodule.py:

    import cython

    # override with Python import if not in compiled code
    if not cython.compiled:
        from math import sin

    # calls sin() from math.h when compiled with Cython and math.sin() in Python
    print(sin(0))

Note that the “sin” function will show up in the module namespace of “mymodule” here (i.e. there will be a `mymodule.sin()` function). You can mark it as an internal name according to Python conventions by renaming it to “_sin” in the `.pxd` file as follows:

    cdef extern from "math.h":
        cpdef double _sin "sin" (double x)

You would then also change the Python import to `from math import sin as _sin` to make the names match again.

### Using C arrays for fixed size lists

Since Cython 0.22, C arrays can automatically coerce to Python lists or tuples. This can be exploited to replace fixed size Python lists in Python code by C arrays when compiled. An example:

    import cython

    @cython.locals(counts=cython.int[10], digit=cython.int)
    def count_digits(digits):
        """
        >>> digits = '01112222333334445667788899'
        >>> count_digits(map(int, digits))
        [1, 3, 4, 5, 3, 1, 2, 2, 3, 2]
        """
        counts = [0] * 10
        for digit in digits:
            assert 0 <= digit <= 9
            counts[digit] += 1
        return counts

In normal Python, this will use a Python list to collect the counts, whereas Cython will generate C code that uses a C array of C ints.