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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"

## Example

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.CPointer(types.double),
                       types.intc, types.intc)

    @cfunc(c_sig)
    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.

## 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`) or pointers to them (for example `types.CPointer(types.int8)`).

## 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.