f2py:f2py.getting-started

Wrapping Fortran or C functions to Python using F2PY consists of the following steps:

Creating the so-called signature file that contains descriptions of wrappers to Fortran or C functions, also called the signatures of the functions. For Fortran routines, F2PY can create an initial signature file by scanning Fortran source codes and tracking all relevant information needed to create wrapper functions.
- Optionally, F2PY-created signature files can be edited to optimize wrapper functions, which can make them "smarter" and more "Pythonic".
F2PY reads a signature file and writes a Python C/API module containing Fortran/C/Python bindings.
F2PY compiles all sources and builds an extension module containing the wrappers.
- In building the extension modules, F2PY uses meson and used to use numpy.distutils For different build systems, see f2py-bldsys.

Depending on your operating system, you may need to install the Python development headers (which provide the file Python.h) separately. In Linux Debian-based distributions this package should be called python3-dev, in Fedora-based distributions it is python3-devel. For macOS, depending how Python was installed, your mileage may vary. In Windows, the headers are typically installed already, see f2py-windows.

Depending on the situation, these steps can be carried out in a single composite command or step-by-step; in which case some steps can be omitted or combined with others.

Below, we describe three typical approaches of using F2PY with Fortran 77. These can be read in order of increasing effort, but also cater to different access levels depending on whether the Fortran code can be freely modified.

The following example Fortran 77 code will be used for illustration, save it as fib1.f:

The quick way

The quickest way to wrap the Fortran subroutine FIB for use in Python is to run

python -m numpy.f2py -c fib1.f -m fib1

or, alternatively, if the f2py command-line tool is available,

f2py -c fib1.f -m fib1

This command compiles and wraps fib1.f (-c) to create the extension module fib1.so (-m) in the current directory. A list of command line options can be seen by executing python -m numpy.f2py. Now, in Python the Fortran subroutine FIB is accessible via fib1.fib

>>> import numpy as np
>>> import fib1
>>> print(fib1.fib.__doc__)
fib(a,[n])

Wrapper for ``fib``.

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

Other parameters
----------------
n : input int, optional
    Default: len(a)

>>> a = np.zeros(8, 'd')
>>> fib1.fib(a)
>>> print(a)
[  0.   1.   1.   2.   3.   5.   8.  13.]

note

Note that F2PY recognized that the second argument n is the dimension of the first array argument a. Since by default all arguments are input-only arguments, F2PY concludes that n can be optional with the default value len(a).

One can use different values for optional n

>>> a1 = np.zeros(8, 'd')
>>> fib1.fib(a1, 6)
>>> print(a1)
[ 0.  1.  1.  2.  3.  5.  0.  0.]

but an exception is raised when it is incompatible with the input array a

>>> fib1.fib(a, 10)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
fib.error: (len(a)>=n) failed for 1st keyword n: fib:n=10
>>>

F2PY implements basic compatibility checks between related arguments in order to avoid unexpected crashes.

When a NumPy array that is Fortran <Fortran order> contiguous and has a dtype corresponding to a presumed Fortran type is used as an input array argument, then its C pointer is directly passed to Fortran.
Otherwise, F2PY makes a contiguous copy (with the proper dtype) of the input array and passes a C pointer of the copy to the Fortran subroutine. As a result, any possible changes to the (copy of) input array have no effect on the original argument, as demonstrated below
```
>>> a = np.ones(8, 'i')
>>> fib1.fib(a)
>>> print(a)
[1 1 1 1 1 1 1 1]
```
Clearly, this is unexpected, as Fortran typically passes by reference. That the above example worked with dtype=float is considered accidental.
F2PY provides an intent(inplace) attribute that modifies the attributes of an input array so that any changes made by the Fortran routine will be reflected in the input argument. For example, if one specifies the intent(inplace) a directive (see f2py-attributes for details), then the example above would read
```
>>> a = np.ones(8, 'i')
>>> fib1.fib(a)
>>> print(a)
[  0.   1.   1.   2.   3.   5.   8.  13.]
```
However, the recommended way to have changes made by Fortran subroutine propagate to Python is to use the intent(out) attribute. That approach is more efficient and also cleaner.
The usage of fib1.fib in Python is very similar to using FIB in Fortran. However, using in situ output arguments in Python is poor style, as there are no safety mechanisms in Python to protect against wrong argument types. When using Fortran or C, compilers discover any type mismatches during the compilation process, but in Python the types must be checked at runtime. Consequently, using in situ output arguments in Python may lead to difficult to find bugs, not to mention the fact that the codes will be less readable when all required type checks are implemented.

Though the approach to wrapping Fortran routines for Python discussed so far is very straightforward, it has several drawbacks (see the comments above). The drawbacks are due to the fact that there is no way for F2PY to determine the actual intention of the arguments; that is, there is ambiguity in distinguishing between input and output arguments. Consequently, F2PY assumes that all arguments are input arguments by default.

There are ways (see below) to remove this ambiguity by "teaching" F2PY about the true intentions of function arguments, and F2PY is then able to generate more explicit, easier to use, and less error prone wrappers for Fortran functions.

The smart way

If we want to have more control over how F2PY will treat the interface to our Fortran code, we can apply the wrapping steps one by one.

First, we create a signature file from fib1.f by running:
```
python -m numpy.f2py fib1.f -m fib2 -h fib1.pyf
```
The signature file is saved to fib1.pyf (see the -h flag) and its contents are shown below.
Next, we'll teach F2PY that the argument n is an input argument (using the intent(in) attribute) and that the result, i.e., the contents of a after calling the Fortran function FIB, should be returned to Python (using the intent(out) attribute). In addition, an array a should be created dynamically using the size determined by the input argument n (using the depend(n) attribute to indicate this dependence relation).
The contents of a suitably modified version of fib1.pyf (saved as fib2.pyf) are as follows:
Finally, we build the extension module with numpy.distutils by running:
```
python -m numpy.f2py -c fib2.pyf fib1.f
```

In Python

>>> import fib2
>>> print(fib2.fib.__doc__)
a = fib(n)

Wrapper for ``fib``.

Parameters
----------
n : input int

Returns
-------
a : rank-1 array('d') with bounds (n)

>>> print(fib2.fib(8))
[  0.   1.   1.   2.   3.   5.   8.  13.]

The quick and smart way

The "smart way" of wrapping Fortran functions, as explained above, is suitable for wrapping (e.g. third party) Fortran codes for which modifications to their source codes are not desirable nor even possible.

However, if editing Fortran codes is acceptable, then the generation of an intermediate signature file can be skipped in most cases. F2PY specific attributes can be inserted directly into Fortran source codes using F2PY directives. A F2PY directive consists of special comment lines (starting with Cf2py or !f2py, for example) which are ignored by Fortran compilers but interpreted by F2PY as normal lines.

Consider a modified version of the previous Fortran code with F2PY directives, saved as fib3.f:

Building the extension module can be now carried out in one command

python -m numpy.f2py -c -m fib3 fib3.f

Notice that the resulting wrapper to FIB is as "smart" (unambiguous) as in the previous case

>>> import fib3
>>> print(fib3.fib.__doc__)
a = fib(n)

Wrapper for ``fib``.

Parameters
----------
n : input int

Returns
-------
a : rank-1 array('d') with bounds (n)

>>> print(fib3.fib(8))
[  0.   1.   1.   2.   3.   5.   8.  13.]