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Creating C callbacks with @cfunc

Interfacing with some native libraries (for example written in C or C++) can necessitate writing native callbacks to provide business logic to the library. The numba.cfunc() decorator creates a compiled function callable from foreign C code, using the signature of your choice.

Basic usage

The @cfunc decorator has a similar usage to @jit, but with an important difference: passing a single signature is mandatory. It determines the visible signature of the C callback:

from numba import cfunc

@cfunc("float64(float64, float64)")
def add(x, y):
    return x + y

The C function object exposes the address of the compiled C callback as the address attribute, so that you can pass it to any foreign C or C++ library. It also exposes a ctypes callback object pointing to that callback; that object is also callable from Python, making it easy to check the compiled code:

@cfunc("float64(float64, float64)")
def add(x, y):
    return x + y

print(add.ctypes(4.0, 5.0))  # prints "9.0"


In this example, we are going to be using the scipy.integrate.quad function. That function accepts either a regular Python callback or a C callback wrapped in a ctypes callback object.

Let’s define a pure Python integrand and compile it as a C callback:

>>> import numpy as np
>>> from numba import cfunc
>>> def integrand(t):
        return np.exp(-t) / t**2
>>> nb_integrand = cfunc("float64(float64)")(integrand)

We can pass the nb_integrand object’s ctypes callback to scipy.integrate.quad and check that the results are the same as with the pure Python function:

>>> import scipy.integrate as si
>>> def do_integrate(func):
        Integrate the given function from 1.0 to +inf.
        return si.quad(func, 1, np.inf)
>>> do_integrate(integrand)
(0.14849550677592208, 3.8736750296130505e-10)
>>> do_integrate(nb_integrand.ctypes)
(0.14849550677592208, 3.8736750296130505e-10)

Using the compiled callback, the integration function does not invoke the Python interpreter each time it evaluates the integrand. In our case, the integration is made 18 times faster:

>>> %timeit do_integrate(integrand)
1000 loops, best of 3: 242 µs per loop
>>> %timeit do_integrate(nb_integrand.ctypes)
100000 loops, best of 3: 13.5 µs per loop

Dealing with pointers and array memory

A less trivial use case of C callbacks involves doing operation on some array of data passed by the caller. As C doesn’t have a high-level abstraction similar to Numpy arrays, the C callback’s signature will pass low-level pointer and size arguments. Nevertheless, the Python code for the callback will expect to exploit the power and expressiveness of Numpy arrays.

In the following example, the C callback is expected to operate on 2-d arrays, with the signature void(double *input, double *output, int m, int n). You can implement such a callback thusly:

from numba import cfunc, types, carray

c_sig = types.void(types.CPointer(types.double),
                   types.intc, types.intc)

def my_callback(in_, out, m, n):
    in_array = carray(in_, (m, n))
    out_array = carray(out, (m, n))
    for i in range(m):
        for j in range(n):
            out_array[i, j] = 2 * in_array[i, j]

The numba.carray() function takes as input a data pointer and a shape and returns an array view of the given shape over that data. The data is assumed to be laid out in C order. If the data is laid out in Fortran order, numba.farray() should be used instead.

Handling C structures


For applications that have a lot of state, it is useful to pass data in C structures. To simplify the interoperability with C code, numba can convert a cffi type into a numba Record type using numba.core.typing.cffi_utils.map_type:

from numba.core.typing import cffi_utils

nbtype = cffi_utils.map_type(cffi_type, use_record_dtype=True)


use_record_dtype=True is needed otherwise pointers to C structures are returned as void pointers.


From v0.49 the numba.cffi_support module has been phased out in favour of numba.core.typing.cffi_utils

For example:

from cffi import FFI

src = """

/* Define the C struct */
typedef struct my_struct {
   int    i1;
   float  f2;
   double d3;
   float  af4[7]; // arrays are supported
} my_struct;

/* Define a callback function */
typedef double (*my_func)(my_struct*, size_t);

ffi = FFI()

# Get the function signature from *my_func*
sig = cffi_utils.map_type(ffi.typeof('my_func'), use_record_dtype=True)

# Make the cfunc
from numba import cfunc, carray

def foo(ptr, n):
   base = carray(ptr, n)  # view pointer as an array of my_struct
   tmp = 0
   for i in range(n):
      tmp += base[i].i1 * base[i].f2 / base[i].d3
      tmp += base[i].af4.sum()  # nested arrays are like normal numpy array
   return tmp

With numba.types.Record.make_c_struct

The numba.types.Record type can be created manually to follow a C-structure’s layout. To do that, use Record.make_c_struct, for example:

my_struct = types.Record.make_c_struct([
   # Provides a sequence of 2-tuples i.e. (name:str, type:Type)
   ('i1', types.int32),
   ('f2', types.float32),
   ('d3', types.float64),
   ('af4', types.NestedArray(dtype=types.float32, shape=(7,))),

Due to ABI limitations, structures should be passed as pointers using types.CPointer(my_struct) as the argument type. Inside the cfunc body, the my_struct* can be accessed with carray.

Full example

See full example in examples/notebooks/Accessing C Struct Data.ipynb.

Signature specification

The explicit @cfunc signature can use any Numba types, but only a subset of them make sense for a C callback. You should generally limit yourself to scalar types (such as int8 or float64) ,pointers to them (for example types.CPointer(types.int8)), or pointers to Record type.

Compilation options

A number of keyword-only arguments can be passed to the @cfunc decorator: nopython and cache. Their meaning is similar to those in the @jit decorator.