cffi / doc / source / index.rst

CFFI documentation

Foreign Function Interface for Python calling C code. The aim of this project is to provide a convenient and reliable way of calling C code from Python. The interface is based on LuaJIT's FFI and follows a few principles:

  • The goal is to call C code from Python. You should be able to do so without learning a 3rd language: every alternative requires you to learn their own language (Cython, SWIG) or API (ctypes). So we tried to assume that you know Python and C and minimize the extra bits of API that you need to learn.
  • Keep all the Python-related logic in Python so that you don't need to write much C code (unlike CPython native C extensions).
  • Work either at the level of the ABI (Application Binary Interface) or the API (Application Programming Interface). Usually, C libraries have a specified C API but often not an ABI (e.g. they may document a "struct" as having at least these fields, but maybe more). (ctypes works at the ABI level, whereas Cython and native C extensions work at the API level.)
  • We try to be complete. For now some C99 constructs are not supported, but all C89 should be, including macros (and including macro "abuses", which you can manually wrap in saner-looking C functions).
  • We attempt to support both PyPy and CPython (although PyPy support is not complete yet) with a reasonable path for other Python implementations like IronPython and Jython.
  • Note that this project is not about embedding executable C code in Python, unlike Weave. This is about calling existing C libraries from Python.

Installation and Status

Quick installation:

In more details:

This code has been developed on Linux but should work on any POSIX platform as well as on Win32. There are some Windows-specific issues left.

It currently supports CPython 2.x. Support for CPython 3.x should not be too hard. Support for PyPy is coming soon. (In fact, the authors of CFFI are also on the PyPy team; we plan to make it the first (and fastest) choice for PyPy.)

Requirements:

  • CPython 2.6 or 2.7 (you need python-dev)
  • pycparser 2.06 or 2.07: http://code.google.com/p/pycparser/
  • libffi (you need libffi-dev); for Windows, it is included with CFFI.
  • a C compiler is required to use CFFI during development, but not to run correctly-installed programs that use CFFI.

Download and Installation:

  • https://bitbucket.org/cffi/cffi/downloads
  • python setup.py install or python setup_base.py install (should work out of the box on Linux or Windows; see below for MacOS 10.6)
  • or you can directly import and use cffi, but if you don't compile the _cffi_backend extension module, it will fall back to using internally ctypes (much slower and does not support verify(); we recommend not to use it).
  • running the tests: py.test c/ testing/ -x (if you didn't install cffi yet, you may need python setup_base.py build and PYTHONPATH=build/lib.xyz.../)

Demos:

Platform-specific instructions

libffi is notoriously messy to install and use --- to the point that CPython includes its own copy to avoid relying on external packages. CFFI does the same for Windows, but (so far) not for other platforms. Modern Linuxes work out of the box thanks to pkg-config. Here are some (user-supplied) instructions for other platforms.

MacOS 10.6

(Thanks Juraj Sukop for this)

For building libffi you can use the default install path, but then, in setup.py you need to change:

include_dirs = []

to:

include_dirs = ['/usr/local/lib/libffi-3.0.11/include']

Then running python setup.py build complains about "fatal error: error writing to -: Broken pipe", which can be fixed by running:

ARCHFLAGS="-arch i386 -arch x86_64" python setup.py build

as described here.


Examples

Simple example (ABI level)

>>> from cffi import FFI
>>> ffi = FFI()
>>> ffi.cdef("""
...     int printf(const char *format, ...);   // copy-pasted from the man page
... """)
>>> C = ffi.dlopen(None)                     # loads the entire C namespace
>>> arg = ffi.new("char[]", "world")         # equivalent to C code: char arg[] = "world";
>>> C.printf("hi there, %s!\n", arg)         # call printf
hi there, world!

Real example (API level)

from cffi import FFI
ffi = FFI()
ffi.cdef("""     // some declarations from the man page
    struct passwd {
        char *pw_name;
        ...;
    };
    struct passwd *getpwuid(int uid);
""")
C = ffi.verify("""   // passed to the real C compiler
#include <sys/types.h>
#include <pwd.h>
""")
p = C.getpwuid(0)
assert str(p.pw_name) == 'root'

Note that the above example works independently of the exact layout of struct passwd. It requires a C compiler the first time you run it, unless the module is distributed and installed according to the Distributing modules using CFFI intructions below. See also the note about Cleaning up the __pycache__ directory.

You will find a number of larger examples using verify() in the demo directory.

Struct/Array Example

from cffi import FFI
ffi = FFI()
ffi.cdef("""
    typedef struct {
        unsigned char r, g, b;
    } pixel_t;
""")
image = ffi.new("pixel_t[]", 800*600)

f = open('data', 'rb')     # binary mode -- important
f.readinto(ffi.buffer(image))
f.close()

image[100].r = 255
image[100].g = 192
image[100].b = 128

f = open('data', 'wb')
f.write(ffi.buffer(image))
f.close()

This can be used as a more flexible replacement of the struct and array modules. You could also call ffi.new("pixel_t[600][800]") and get a two-dimensional array.

What actually happened?

The CFFI interface operates on the same level as C - you declare types and functions using the same syntax as you would define them in C. This means that most of the documentation or examples can be copied straight from the man pages.

The declarations can contain types, functions and global variables. The cdef in the above examples are just that - they declared "there is a function in the C level with this given signature", or "there is a struct type with this shape".

The dlopen() line loads libraries. C has multiple namespaces - a global one and local ones per library. In this example we load the global one (None as argument to dlopen()) which always contains the standard C library. You get as a result a <FFILibrary> object that has as attributes all symbols declared in the cdef() and coming from this library.

The verify() line in the second example is an alternative: instead of doing a dlopen, it generates and compiles a piece of C code. When using verify() you have the advantage that you can use "..." at various places in the cdef(), and the missing information will be completed with the help of the C compiler. It also does checking, to verify that your declarations are correct. If the C compiler gives warnings or errors, they are reported here.

Finally, the ffi.new() lines allocate C objects. They are filled with zeroes initially, unless the optional second argument is used. If specified, this argument gives an "initializer", like you can use with C code to initialize global variables.

The actual function calls should be obvious. It's like C.


Distributing modules using CFFI

If you use CFFI and verify() in a project that you plan to distribute, other users will install it on machines that may not have a C compiler. Here is how to write a setup.py script using distutils in such a way that the extension modules are listed too. This lets normal setup.py commands compile and package the C extension modules too.

Example:

from distutils.core import setup
from distutils.extension import Extension

# you must import at least the module(s) that define the ffi's
# that you use in your application
import yourmodule

setup(...
      ext_modules=[yourmodule.ffi.verifier.get_extension()])

Usually that's all you need, but see the Reference: verifier section for more details about the verifier object.

Cleaning up the __pycache__ directory

During development, every time you change the C sources that you pass to cdef() or verify(), then the latter will create a new module file name, based on the MD5 hash of these strings. This creates more and more files in the __pycache__ directory. It is recommended that you clean it up from time to time. A nice way to do that is to add, in your test suite, a call to cffi.verifier.cleanup_tmpdir(). Alternatively, you can just completely remove the __pycache__ directory.


Reference

As a guideline: you have already seen in the above examples all the major pieces except maybe ffi.cast(). The rest of this documentation gives a more complete reference.

Declaring types and functions

ffi.cdef(source) parses the given C source. This should be done first. It registers all the functions, types, and global variables in the C source. The types can be used immediately in ffi.new() and other functions. Before you can access the functions and global variables, you need to give ffi another piece of information: where they actually come from (which you do with either ffi.dlopen() or ffi.verify()).

The C source is parsed internally (using pycparser). This code cannot contain #include. It should typically be a self-contained piece of declarations extracted from a man page. The only things it can assume to exist are the standard types:

  • char, short, int, long, long long (both signed and unsigned)
  • float, double
  • intN_t, uintN_t (for N=8,16,32,64), intptr_t, uintptr_t, ptrdiff_t, size_t, ssize_t
  • wchar_t (if supported by the backend)

As we will see on the verification step below, the declarations can also contain "..." at various places; these are placeholders that will be completed by a call to verify().

Loading libraries

ffi.dlopen(libpath): this function opens a shared library and returns a module-like library object. You need to use either ffi.dlopen() or ffi.verify(), documented below.

You can use the library object to call the functions previously declared by ffi.cdef(), and to read or write global variables. Note that you can use a single cdef() to declare functions from multiple libraries, as long as you load each of them with dlopen() and access the functions from the correct one.

The libpath is the file name of the shared library, which can contain a full path or not (in which case it is searched in standard locations, as described in man dlopen). Alternatively, if libpath is None, it returns the standard C library (which can be used to access the functions of glibc, on Linux).

This gives ABI-level access to the library: you need to have all types declared manually exactly as they were while the library was made. No checking is done. For this reason, we recommend to use ffi.verify() instead when possible.

Note that only functions and global variables are in library objects; types exist in the ffi instance independently of library objects. This is due to the C model: the types you declare in C are not tied to a particular library, as long as you #include their headers; but you cannot call functions from a library without linking it in your program, as dlopen() does dynamically in C.

The verification step

ffi.verify(source, **kwargs): verifies that the current ffi signatures compile on this machine, and return a dynamic library object. The dynamic library can be used to call functions and access global variables declared by a previous ffi.cdef(). You don't need to use ffi.dlopen() in this case.

The returned library is a custom one, compiled just-in-time by the C compiler: it gives you C-level API compatibility (including calling macros, as long as you declared them as functions in ffi.cdef()). This differs from ffi.dlopen(), which requires ABI-level compatibility and must be called several times to open several shared libraries.

On top of CPython, the new library is actually a CPython C extension module.

The arguments to ffi.verify() are:

  • source: C code that is pasted verbatim in the generated code (it is not parsed internally). It should contain at least the necessary #include. It can also contain the complete implementation of some functions declared in cdef(); this is useful if you really need to write a piece of C code, e.g. to access some advanced macros (see the example of getyx() in demo/_curses.py).
  • include_dirs, define_macros, undef_macros, libraries, library_dirs, extra_objects, extra_compile_args, extra_link_args (keyword arguments): these are used when compiling the C code, and are passed directly to distutils.

On the plus side, this solution gives more "C-like" flexibility:

  • functions taking or returning integer or float-point arguments can be misdeclared: if e.g. a function is declared by cdef() as taking a int, but actually takes a long, then the C compiler handles the difference.
  • other arguments are checked: you get a compilation warning or error if you pass a int * argument to a function expecting a long *.

Moreover, you can use "..." in the following places in the cdef() for leaving details unspecified, which are then completed by the C compiler during verify():

  • structure declarations: any struct that ends with "...;" is partial: it may be missing fields and/or have them declared out of order. This declaration will be corrected by the compiler. (But note that you can only access fields that you declared, not others.) Any struct declaration which doesn't use "..." is assumed to be exact, but this is checked: you get a VerificationError if it is not.
  • unknown types: the syntax "typedef ... foo_t;" declares the type foo_t as opaque. Useful mainly for when the API takes and returns foo_t * without you needing to look inside the foo_t. Note that such an opaque struct has no known size, which prevents some operations from working (mostly like in C). In some cases you need to say that foo_t is not opaque, but you just don't know any field in it; then you would use "typedef struct { ...; } foo_t;".
  • array lengths: when used as structure fields, arrays can have an unspecified length, as in "int n[];" or "int n[...];. The length is completed by the C compiler.
  • enums: in "enum foo { A, B, C, ... };" (with a trailing "..."), the enumerated values are not necessarily in order; the C compiler will reorder them as needed and skip any unmentioned value. Like with structs, an enum that does not end in "..." is assumed to be exact, and this is checked.
  • integer macros: you can write in the cdef the line "#define FOO ...", with any macro name FOO. Provided the macro is defined to be an integer value, this value will be available via an attribute of the library object returned by verify(). The same effect can be achieved by writing a declaration static const int FOO;. The latter is more general because it supports other types than integer types (note: the syntax is then to write the const together with the variable name, as in static char *const FOO;).

Currently, finding automatically the size of an integer type is not supported. You need to declare them with typedef int myint; or typedef long myint; or typedef long long myint; or their unsigned equivalent. Depending on the usage, the C compiler might give warnings if you misdeclare myint as the wrong type even if it is equivalent on this platform (e.g. using long instead of long long or vice-versa on 64-bit Linux).

Working with pointers, structures and arrays

The C code's integers and floating-point values are mapped to Python's regular int, long and float. Moreover, the C type char corresponds to single-character strings in Python. (If you want it to map to small integers, use either signed char or unsigned char.)

Similarly, the C type wchar_t corresponds to single-character unicode strings, if supported by the backend. Note that in some situations (a narrow Python build with an underlying 4-bytes wchar_t type), a single wchar_t character may correspond to a pair of surrogates, which is represented as a unicode string of length 2. If you need to convert a wchar_t to an integer, do not use ord(x), because it doesn't accept such unicode strings; use instead int(ffi.cast('int', x)), which does.

Pointers, structures and arrays are more complex: they don't have an obvious Python equivalent. Thus, they correspond to objects of type cdata, which are printed for example as <cdata 'struct foo_s *' 0xa3290d8>.

ffi.new(ctype, [initializer]): this function builds and returns a new cdata object of the given ctype. The ctype is usually some constant string describing the C type. It must be a pointer or array type. If it is a pointer, e.g. "int *" or struct foo *, then it allocates the memory for one int or struct foo. If it is an array, e.g. int[10], then it allocates the memory for ten int. In both cases the returned cdata is of type ctype.

The memory is initially filled with zeros. An initializer can be given too, as described later.

Example:

>>> ffi.new("char *")
<cdata 'char *' owning 1 bytes>
>>> ffi.new("int *")
<cdata 'int *' owning 4 bytes>
>>> ffi.new("int[10]")
<cdata 'int[10]' owning 40 bytes>

Unlike C, the returned pointer object has ownership on the allocated memory: when this exact object is garbage-collected, then the memory is freed. If, at the level of C, you store a pointer to the memory somewhere else, then make sure you also keep the object alive for as long as needed. (This also applies if you immediately cast the returned pointer to a pointer of a different type: only the original object has ownership, so you must keep it alive. As soon as you forget it, then the casted pointer will point to garbage.)

The cdata objects support mostly the same operations as in C: you can read or write from pointers, arrays and structures. Dereferencing a pointer is done usually in C with the syntax *p, which is not valid Python, so instead you have to use the alternative syntax p[0] (which is also valid C). Additionally, the p.x and p->x syntaxes in C both become p.x in Python.

There is no equivalent to the & operator in C (because it would not fit nicely in the model, and it does not seem to be needed here).

Any operation that would in C return a pointer or array or struct type gives you a fresh cdata object. Unlike the "original" one, these fresh cdata objects don't have ownership: they are merely references to existing memory.

As an exception the above rule, dereferencing a pointer that owns a struct or union object returns a cdata struct or union object that "co-owns" the same memory. Thus in this case there are two objects that can keep the same memory alive. This is done for cases where you really want to have a struct object but don't have any convenient place to keep alive the original pointer object (returned by ffi.new()).

Example:

ffi.cdef("void somefunction(int *);")
lib = ffi.verify("#include <foo.h>")

x = ffi.new("int *")      # allocate one int, and return a pointer to it
x[0] = 42                 # fill it
lib.somefunction(x)       # call the C function
print x[0]                # read the possibly-changed value

The equivalent of C casts are provided with ffi.cast("type", value). They should work in the same cases as they do in C. Additionally, this is the only way to get cdata objects of integer or floating-point type:

>>> x = ffi.cast("int", 42)
>>> x
<cdata 'int' 42>
>>> int(x)
42

The initializer given as the optional second argument to ffi.new() can be mostly anything that you would use as an initializer for C code, with lists or tuples instead of using the C syntax { .., .., .. }. Example:

typedef struct { int x, y; } foo_t;

foo_t v = { 1, 2 };            // C syntax
v = ffi.new("foo_t *", [1, 2]) # CFFI equivalent

foo_t v = { .y=1, .x=2 };                // C99 syntax
v = ffi.new("foo_t *", {'y': 1, 'x': 2}) # CFFI equivalent

Like C, arrays of chars can also be initialized from a string, in which case a terminating null character is appended implicitly:

>>> x = ffi.new("char[]", "hello")
>>> x
<cdata 'char[]' owning 6 bytes>
>>> len(x)        # the actual size of the array
6
>>> x[5]          # the last item in the array
'\x00'
>>> x[0] = 'H'    # change the first item
>>> str(x)        # interpret 'x' as a regular null-terminated string
'Hello'

Similarly, arrays of wchar_t can be initialized from a unicode string, and calling unicode() on the cdata object returns the current unicode string stored in the wchar_t array (encoding and decoding surrogates as needed if necessary).

Note that unlike Python lists or tuples, but like C, you cannot index in a C array from the end using negative numbers.

More generally, the C array types can have their length unspecified in C types, as long as their length can be derived from the initializer, like in C:

int array[] = { 1, 2, 3, 4 };           // C syntax
array = ffi.new("int[]", [1, 2, 3, 4])  # CFFI equivalent

As an extension, the initializer can also be just a number, giving the length (in case you just want zero-initialization):

int array[1000];                  // C syntax
array = ffi.new("int[1000]")      # CFFI 1st equivalent
array = ffi.new("int[]", 1000)    # CFFI 2nd equivalent

This is useful if the length is not actually a constant, to avoid things like ffi.new("int[%d]" % x). Indeed, this is not recommended: ffi normally caches the string "int[]" to not need to re-parse it all the time.

An example of calling a main-like thing

Imagine we have something like this:

from cffi import FFI
ffi = FFI()
ffi.cdef("""
   int main_like(int argv, char *argv[]);
""")

Now, everything is simple, except, how do we create the char** argument here? The first idea:

argv = ffi.new("char *[]", ["arg0", "arg1"])

Does not work, because the initializer receives python str instead of char*. Now, the following would almost work:

argv = ffi.new("char *[]", [ffi.new("char[]", "arg0"),
                            ffi.new("char[]", "arg1")])

However, the two char[] objects will not be automatically kept alive. To keep them alive, one solution is to make sure that the list is stored somewhere for long enough. For example:

argv_keepalive = [ffi.new("char[]", "arg0"),
                  ffi.new("char[]", "arg1")]
argv = ffi.new("char *[]", argv_keepalive)

will work.

Function calls

When calling C functions, passing arguments follows mostly the same rules as assigning to structure fields, and the return value follows the same rules as reading a structure field. For example:

ffi.cdef("""
    int foo(short a, int b);
""")
lib = ffi.verify("#include <foo.h>")

n = lib.foo(2, 3)     # returns a normal integer
lib.foo(40000, 3)     # raises OverflowError

As an extension, you can pass to char * arguments a normal Python string (but don't pass a normal Python string to functions that take a char * argument and may mutate it!):

ffi.cdef("""
    size_t strlen(const char *);
""")
C = ffi.dlopen(None)

assert C.strlen("hello") == 5

So far passing unicode strings as wchar_t * arguments is not implemented. You need to write e.g.:

>>> C.wcslen(ffi.new("wchar_t[]", u"foo"))
3

CFFI supports passing and returning structs to functions and callbacks. Example (sketch):

>>> ffi.cdef("""
...     struct foo_s { int a, b; };
...     struct foo_s function_returning_a_struct(void);
... """)
>>> lib = ffi.verify("#include <somewhere.h>")
>>> lib.function_returning_a_struct()
<cdata 'struct foo_s' owning 8 bytes>

There are a few (obscure) limitations to the argument types and return type. You cannot pass directly as argument a union, nor a struct which uses bitfields (note that passing a pointer to anything is fine). If you pass a struct, the struct type cannot have been declared with "...;" and completed with verify(); you need to declare it completely in cdef().

Aside from these limitations, functions and callbacks can return structs.

Variadic function calls

Variadic functions in C (which end with "..." as their last argument) can be declared and called normally, with the exception that all the arguments passed in the variable part must be cdata objects. This is because it would not be possible to guess, if you wrote this:

C.printf("hello, %d\n", 42)

that you really meant the 42 to be passed as a C int, and not a long or long long. The same issue occurs with float versus double. So you have to force cdata objects of the C type you want, if necessary with ffi.cast():

C.printf("hello, %d\n", ffi.cast("int", 42))
C.printf("hello, %ld\n", ffi.cast("long", 42))
C.printf("hello, %f\n", ffi.cast("double", 42))
C.printf("hello, %s\n", ffi.new("char[]", "world"))

Callbacks

C functions can also be viewed as cdata objects, and so can be passed as callbacks. To make new C callback objects that will invoke a Python function, you need to use:

>>> def myfunc(x, y):
...    return x + y
...
>>> ffi.callback("int(*)(int, int)", myfunc)
<cdata 'int(*)(int, int)' calling <function myfunc at 0xf757bbc4>>

Warning: like ffi.new(), ffi.callback() returns a cdata that has ownership of its C data. (In this case, the necessary C data contains the libffi data structures to do a callback.) This means that the callback can only be invoked as long as this cdata object is alive. If you store the function pointer into C code, then make sure you also keep this object alive for as long as the callback may be invoked. (If you want the callback to remain valid forever, store the object in a fresh global variable somewhere.)

Note that callbacks of a variadic function type are not supported.

Windows: you can't yet specify the calling convention of callbacks. (For regular calls, the correct calling convention should be automatically inferred by the C backend.)

Be careful when writing the Python callback function: if it returns an object of the wrong type, or more generally raises an exception, then the exception cannot be propagated. Instead, it is printed to stderr and the C-level callback is made to return a default value.

The returned value in case of errors is 0 or null by default, but can be specified with the error keyword argument to ffi.callback():

>>> ffi.callback("int(*)(int, int)", myfunc, error=42)

In all cases the exception is printed to stderr, so this should be used only as a last-resort solution.

Miscellaneous

ffi.errno: the value of errno received from the most recent C call in this thread, and passed to the following C call, is available via reads and writes of the property ffi.errno. On Windows we also save and restore the GetLastError() value, but to access it you need to declare and call the GetLastError() function as usual.

ffi.buffer(pointer, [size]): return a read-write buffer object that references the raw C data pointed to by the given 'cdata', of 'size' bytes. The 'cdata' must be a pointer or an array. To get a copy of it in a regular string, call str() on the result. If unspecified, the default size of the buffer is sizeof(*pointer) or the whole size of the array. Getting a buffer is useful because you can read from it without an extra copy, or write into it to change the original value; you can use for example file.write() and file.readinto() with such a buffer (for files opened in binary mode). (Remember that like in C, you use array + index to get the pointer to the index'th item of an array.)

ffi.typeof("C type" or cdata object): return an object of type <ctype> corresponding to the parsed string, or to the C type of the cdata instance. Usually you don't need to call this function or to explicitly manipulate <ctype> objects in your code: any place that accepts a C type can receive either a string or a pre-parsed ctype object (and because of caching of the string, there is no real performance difference). It can still be useful in writing typechecks, e.g.:

def myfunction(ptr):
    assert ffi.typeof(ptr) is ffi.typeof("foo_t*")
    ...

ffi.sizeof("C type" or cdata object): return the size of the argument in bytes. The argument can be either a C type, or a cdata object, like in the equivalent sizeof operator in C.

ffi.alignof("C type"): return the alignment of the C type. Corresponds to the __alignof__ operator in GCC.

ffi.offsetof("C struct type", "fieldname"): return the offset within the struct of the given field. Corresponds to offsetof() in C.

ffi.getcname("C type" or <ctype>, extra=""): return the string representation of the given C type. If non-empty, the "extra" string is appended (or inserted at the right place in more complicated cases); it can be the name of a variable to declare, or an extra part of the type like "*" or "[5]". For example ffi.getcname(ffi.typeof(x), "*") returns the string representation of the C type "pointer to the same type than x".

Reference: conversions

This section documents all the conversions that are allowed when writing into a C data structure (or passing arguments to a function call), and reading from a C data structure (or getting the result of a function call). The last column gives the type-specific operations allowed.

C type writing into reading from other operations
integers an integer or anything on which int() works (but not a float!). Must be within range. a Python int or long, depending on the type int()
char a string of length 1 or another <cdata char> a string of length 1 str(), int()
wchar_t a unicode of length 1 (or maybe 2 if surrogates) or another <cdata wchar_t> a unicode of length 1 (or maybe 2 if surrogates) unicode(), int()
float, double a float or anything on which float() works a Python float float(), int()
pointers another <cdata> with a compatible type (i.e. same type or char* or void*, or as an array instead) a <cdata> [], +, -
void * another <cdata> with any pointer or array type  
char * another <cdata> with any pointer or array type, or a Python string when passed as func argument [], +, -, str()
wchar_t * same as pointers (passing a unicode as func argument is not implemented) [], +, -, unicode()
pointers to structure or union same as pointers [], +, -, and read/write struct fields
function pointers call
arrays a list or tuple of items a <cdata> len(), iter(), [], +, -
char[] same as arrays, or a Python string len(), iter(), [], +, -, str()
wchar_t[] same as arrays, or a Python unicode len(), iter(), [], +, -, unicode()
structure a list or tuple or dict of the field values, or a same-type <cdata> a <cdata> read/write fields
union same as struct, but with at most one field read/write fields
enum an integer, or the enum value as a string or as "#NUMBER" the enum value as a string, or "#NUMBER" if out of range int(), str()

Reference: verifier

For advanced use cases, the Verifier class from cffi.verifier can be instantiated directly. It is normally instantiated for you by ffi.verify(), and the instance is attached as ffi.verifier.

  • Verifier(ffi, preamble, **kwds): instantiate the class with an FFI object and a preamble, which is C text that will be pasted into the generated C source. The keyword arguments are passed directly to distutils when building the Extension object.

Verifier objects have the following public attributes and methods:

  • sourcefilename: name of a C file. Defaults to __pycache__/_cffi_MD5HASH.c, with the MD5HASH part computed from the strings you passed to cdef() and verify() as well as the version numbers of Python and CFFI. Can be changed before calling write_source() if you want to write the source somewhere else.
  • modulefilename: name of the .so file (or .pyd on Windows). Defaults to __pycache__/_cffi_MD5HASH.so. Can be changed before calling compile_module().
  • get_module_name(): extract the module name from modulefilename.
  • write_source(file=None): produces the C source of the extension module. If file is specified, write it in that file (or file-like) object rather than to sourcefilename.
  • compile_module(): writes the C source code (if not done already) and compiles it. This produces a dynamic link library whose file is given by modulefilename.
  • load_library(): loads the C module (if necessary, making it first). Returns an instance of a FFILibrary class that behaves like the objects returned by ffi.dlopen(), but that delegates all operations to the C module. This is what is returned by ffi.verify().
  • get_extension(): returns a distutils-compatible Extension instance.

The following are global functions in the cffi.verifier module:

  • set_tmpdir(dirname): sets the temporary directory to use instead of __pycache__.
  • cleanup_tmpdir(): cleans up the temporary directory by removing all files in it called _cffi_*.{c,so} as well as all files in the build subdirectory.

Comments and bugs

The best way to contact us is on the IRC #pypy channel of irc.freenode.net. Feel free to discuss matters either there or in the mailing list. Please report to the issue tracker any bugs.

As a general rule, when there is a design issue to resolve, we pick the solution that is the "most C-like". We hope that this module has got everything you need to access C code and nothing more.

--- the authors, Armin Rigo and Maciej Fijalkowski

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