# F2PY examples

Below are some examples of F2PY usage. This list is not comprehensive, but can be used as a starting point when wrapping your own code.

## F2PY walkthrough: a basic extension module

### Creating source for a basic extension module

Consider the following subroutine, contained in a file named `add.f`

    C
          SUBROUTINE ZADD(A,B,C,N)
    C
          DOUBLE COMPLEX A(*)
          DOUBLE COMPLEX B(*)
          DOUBLE COMPLEX C(*)
          INTEGER N
          DO 20 J = 1, N
             C(J) = A(J)+B(J)
     20   CONTINUE
          END

This routine simply adds the elements in two contiguous arrays and places the result in a third. The memory for all three arrays must be provided by the calling routine. A very basic interface to this routine can be automatically generated by f2py:

    python -m numpy.f2py -m add add.f

This command will produce an extension module named `addmodule.c` in the current directory. This extension module can now be compiled and used from Python just like any other extension module.

### Creating a compiled extension module

Note

This usage depends heavily on `numpy.distutils`, see F2PY and Build Systems for more details.

You can also get f2py to both compile `add.f` along with the produced extension module leaving only a shared-library extension file that can be imported from Python:

    python -m numpy.f2py -c -m add add.f

This command produces a Python extension module compatible with your platform. This module may then be imported from Python. It will contain a method for each subroutine in `add`. The docstring of each method contains information about how the module method may be called:

    >>> import add
    >>> print(add.zadd.__doc__)
    zadd(a,b,c,n)

    Wrapper for ``zadd``.

    Parameters
    ----------
    a : input rank-1 array('D') with bounds (*)
    b : input rank-1 array('D') with bounds (*)
    c : input rank-1 array('D') with bounds (*)
    n : input int

### Improving the basic interface

The default interface is a very literal translation of the Fortran code into Python. The Fortran array arguments are converted to NumPy arrays and the integer argument should be mapped to a `C` integer. The interface will attempt to convert all arguments to their required types (and shapes) and issue an error if unsuccessful. However, because `f2py` knows nothing about the semantics of the arguments (such that `C` is an output and `n` should really match the array sizes), it is possible to abuse this function in ways that can cause Python to crash. For example:

    >>> add.zadd([1, 2, 3], [1, 2], [3, 4], 1000)

will cause a program crash on most systems. Under the hood, the lists are being converted to arrays but then the underlying `add` function is told to cycle way beyond the borders of the allocated memory.

In order to improve the interface, `f2py` supports directives. This is accomplished by constructing a signature file. It is usually best to start from the interfaces that `f2py` produces in that file, which correspond to the default behavior. To get `f2py` to generate the interface file use the `-h` option:

    python -m numpy.f2py -h add.pyf -m add add.f

This command creates the `add.pyf` file in the current directory. The section of this file corresponding to `zadd` is:

    subroutine zadd(a,b,c,n) ! in :add:add.f
       double complex dimension(*) :: a
       double complex dimension(*) :: b
       double complex dimension(*) :: c
       integer :: n
    end subroutine zadd

By placing intent directives and checking code, the interface can be cleaned up quite a bit so the Python module method is both easier to use and more robust to malformed inputs.

    subroutine zadd(a,b,c,n) ! in :add:add.f
       double complex dimension(n) :: a
       double complex dimension(n) :: b
       double complex intent(out),dimension(n) :: c
       integer intent(hide),depend(a) :: n=len(a)
    end subroutine zadd

The intent directive, intent(out) is used to tell f2py that `c` is an output variable and should be created by the interface before being passed to the underlying code. The intent(hide) directive tells f2py to not allow the user to specify the variable, `n`, but instead to get it from the size of `a`. The depend( `a` ) directive is necessary to tell f2py that the value of n depends on the input a (so that it won’t try to create the variable n until the variable a is created).

After modifying `add.pyf`, the new Python module file can be generated by compiling both `add.f` and `add.pyf`:

    python -m numpy.f2py -c add.pyf add.f

The new interface’s docstring is:

    >>> import add
    >>> print(add.zadd.__doc__)
    c = zadd(a,b)

    Wrapper for ``zadd``.

    Parameters
    ----------
    a : input rank-1 array('D') with bounds (n)
    b : input rank-1 array('D') with bounds (n)

    Returns
    -------
    c : rank-1 array('D') with bounds (n)

Now, the function can be called in a much more robust way:

    >>> add.zadd([1, 2, 3], [4, 5, 6])
    array([5.+0.j, 7.+0.j, 9.+0.j])

Notice the automatic conversion to the correct format that occurred.

### Inserting directives in Fortran source

The robust interface of the previous section can also be generated automatically by placing the variable directives as special comments in the original Fortran code.

Note

For projects where the Fortran code is being actively developed, this may be preferred.

Thus, if the source code is modified to contain:

    C
          SUBROUTINE ZADD(A,B,C,N)
    C
    CF2PY INTENT(OUT) :: C
    CF2PY INTENT(HIDE) :: N
    CF2PY DOUBLE COMPLEX :: A(N)
    CF2PY DOUBLE COMPLEX :: B(N)
    CF2PY DOUBLE COMPLEX :: C(N)
          DOUBLE COMPLEX A(*)
          DOUBLE COMPLEX B(*)
          DOUBLE COMPLEX C(*)
          INTEGER N
          DO 20 J = 1, N
             C(J) = A(J) + B(J)
     20   CONTINUE
          END

Then, one can compile the extension module using:

    python -m numpy.f2py -c -m add add.f

The resulting signature for the function add.zadd is exactly the same one that was created previously. If the original source code had contained `A(N)` instead of `A(*)` and so forth with `B` and `C`, then nearly the same interface can be obtained by placing the `INTENT(OUT) :: C` comment line in the source code. The only difference is that `N` would be an optional input that would default to the length of `A`.

## A filtering example

This example shows a function that filters a two-dimensional array of double precision floating-point numbers using a fixed averaging filter. The advantage of using Fortran to index into multi-dimensional arrays should be clear from this example.

    C
          SUBROUTINE DFILTER2D(A,B,M,N)
    C
          DOUBLE PRECISION A(M,N)
          DOUBLE PRECISION B(M,N)
          INTEGER N, M
    CF2PY INTENT(OUT) :: B
    CF2PY INTENT(HIDE) :: N
    CF2PY INTENT(HIDE) :: M
          DO 20 I = 2,M-1
             DO 40 J = 2,N-1
                B(I,J) = A(I,J) +
         &           (A(I-1,J)+A(I+1,J) +
         &           A(I,J-1)+A(I,J+1) )*0.5D0 +
         &           (A(I-1,J-1) + A(I-1,J+1) +
         &           A(I+1,J-1) + A(I+1,J+1))*0.25D0
     40      CONTINUE
     20   CONTINUE
          END

This code can be compiled and linked into an extension module named filter using:

    python -m numpy.f2py -c -m filter filter.f

This will produce an extension module in the current directory with a method named `dfilter2d` that returns a filtered version of the input.

## `depends` keyword example

Consider the following code, saved in the file `myroutine.f90`:

    subroutine s(n, m, c, x)
    	implicit none
      	integer, intent(in) :: n, m
      	real(kind=8), intent(out), dimension(n,m) :: x
      	real(kind=8), intent(in) :: c(:)

    	x = 0.0d0
    	x(1, 1) = c(1)

    end subroutine s

Wrapping this with `python -m numpy.f2py -c myroutine.f90 -m myroutine`, we can do the following in Python:

    >>> import numpy as np
    >>> import myroutine
    >>> x = myroutine.s(2, 3, np.array([5, 6, 7]))
    >>> x
    array([[5., 0., 0.],
       [0., 0., 0.]])

Now, instead of generating the extension module directly, we will create a signature file for this subroutine first. This is a common pattern for multi-step extension module generation. In this case, after running

    python -m numpy.f2py myroutine.f90 -h myroutine.pyf

the following signature file is generated:

    !    -*- f90 -*-
    ! Note: the context of this file is case sensitive.

    python module myroutine ! in
        interface  ! in :myroutine
            subroutine s(n,m,c,x) ! in :myroutine:myroutine.f90
                integer intent(in) :: n
                integer intent(in) :: m
                real(kind=8) dimension(:),intent(in) :: c
                real(kind=8) dimension(n,m),intent(out),depend(m,n) :: x
            end subroutine s
        end interface
    end python module myroutine

    ! This file was auto-generated with f2py (version:1.23.0.dev0+120.g4da01f42d).
    ! See:
    ! https://web.archive.org/web/20140822061353/http://cens.ioc.ee/projects/f2py2e

Now, if we run `python -m numpy.f2py -c myroutine.pyf myroutine.f90` we see an error; note that the signature file included a `depend(m,n)` statement for `x` which is not necessary. Indeed, editing the file above to read

    !    -*- f90 -*-
    ! Note: the context of this file is case sensitive.

    python module myroutine ! in
        interface  ! in :myroutine
            subroutine s(n,m,c,x) ! in :myroutine:myroutine.f90
                integer intent(in) :: n
                integer intent(in) :: m
                real(kind=8) dimension(:),intent(in) :: c
                real(kind=8) dimension(n,m),intent(out) :: x
            end subroutine s
        end interface
    end python module myroutine

    ! This file was auto-generated with f2py (version:1.23.0.dev0+120.g4da01f42d).
    ! See:
    ! https://web.archive.org/web/20140822061353/http://cens.ioc.ee/projects/f2py2e

and running `f2py -c myroutine.pyf myroutine.f90` yields correct results.

## Read more

- Wrapping C codes using f2py

- F2py section on the SciPy Cookbook

- F2py example: Interactive System for Ice sheet Simulation

- “Interfacing With Other Languages” section on the SciPy Cookbook.