Three ways to wrap - 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 as signatures of the functions. In the case of Fortran routines, F2PY can create initial signature file by scanning Fortran source codes and catching all relevant information needed to create wrapper functions.
- Optionally, F2PY created signature files can be edited to optimize wrappers functions, 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 extension modules, F2PY uses numpy_distutils that supports a number of Fortran 77/90/95 compilers, including Gnu, Intel, Sun Fortre, SGI MIPSpro, Absoft, NAG, Compaq etc. compilers.
Depending on a particular situation, these steps can be carried out either by just in one command or step-by-step, some steps can be omitted or combined with others.
Below I’ll describe three typical approaches of using F2PY. The following example Fortran 77 code will be used for illustration:
C FILE: FIB1.F SUBROUTINE FIB(A,N) C C CALCULATE FIRST N FIBONACCI NUMBERS C INTEGER N REAL*8 A(N) DO I=1,N IF (I.EQ.1) THEN A(I) = 0.0D0 ELSEIF (I.EQ.2) THEN A(I) = 1.0D0 ELSE A(I) = A(I-1) + A(I-2) ENDIF ENDDO END C END FILE FIB1.F
The quick way¶
The quickest way to wrap the Fortran subroutine FIB to Python is to run
f2py -c fib1.f -m fib1
This command builds (see -c flag, execute f2py without arguments to see the explanation of command line options) an extension module fib1.so (see -m flag) to the current directory. Now, in Python the Fortran subroutine FIB is accessible via fib1.fib:
>>> import numpy >>> import fib1 >>> print fib1.fib.__doc__ fib - Function signature: fib(a,[n]) Required arguments: a : input rank-1 array('d') with bounds (n) Optional arguments: n := len(a) input int >>> a = numpy.zeros(8,'d') >>> fib1.fib(a) >>> print a [ 0. 1. 1. 2. 3. 5. 8. 13.]
Note that F2PY found 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 = numpy.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) fib:n=10 Traceback (most recent call last): File "<stdin>", line 1, in ? fib.error: (len(a)>=n) failed for 1st keyword n >>>
This demonstrates one of the useful features in F2PY, that it, F2PY implements basic compatibility checks between related arguments in order to avoid any unexpected crashes.
When a Numpy array, that is Fortran contiguous and has a dtype corresponding to 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 a proper dtype) of the input array and passes C pointer of the copy to Fortran subroutine. As a result, any possible changes to the (copy of) input array have no effect to the original argument, as demonstrated below:
>>> a = numpy.ones(8,'i') >>> fib1.fib(a) >>> print a [1 1 1 1 1 1 1 1]
Clearly, this is not an expected behaviour. The fact that the above example worked with dtype=float is considered accidental.
F2PY provides intent(inplace) attribute that would modify the attributes of an input array so that any changes made by Fortran routine will be effective also in input argument. For example, if one specifies intent(inplace) a (see below, how), then the example above would read:
>>> a = numpy.ones(8,'i') >>> fib1.fib(a) >>> print a [ 0. 1. 1. 2. 3. 5. 8. 13.]
However, the recommended way to get changes made by Fortran subroutine back to python is to use intent(out) attribute. It is more efficient and a cleaner solution.
The usage of fib1.fib in Python is very similar to using FIB in Fortran. However, using in situ output arguments in Python indicates a poor style as there is no safety mechanism in Python with respect to wrong argument types. When using Fortran or C, compilers naturally discover any type mismatches during compile time but in Python the types must be checked in runtime. So, using in situ output arguments in Python may cause difficult to find bugs, not to mention that the codes will be less readable when all required type checks are implemented.
Though the demonstrated way of wrapping Fortran routines to Python is very straightforward, it has several drawbacks (see the comments above). These drawbacks are due to the fact that there is no way that F2PY can determine what is the actual intention of one or the other argument, is it input or output argument, or both, or something else. So, F2PY conservatively assumes that all arguments are input arguments by default.
However, there are ways (see below) how to “teach” F2PY about the true intentions (among other things) of function arguments; and then F2PY is able to generate more Pythonic (more explicit, easier to use, and less error prone) wrappers to Fortran functions.
The smart way¶
Let’s apply the steps of wrapping Fortran functions to Python one by one.
First, we create a signature file from fib1.f by running
f2py fib1.f -m fib2 -h fib1.pyf
The signature file is saved to fib1.pyf (see -h flag) and its contents is shown below.
! -*- f90 -*- python module fib2 ! in interface ! in :fib2 subroutine fib(a,n) ! in :fib2:fib1.f real*8 dimension(n) :: a integer optional,check(len(a)>=n),depend(a) :: n=len(a) end subroutine fib end interface end python module fib2 ! This file was auto-generated with f2py (version:2.28.198-1366). ! See http://cens.ioc.ee/projects/f2py2e/
Next, we’ll teach F2PY that the argument n is a input argument (use intent(in) attribute) and that the result, i.e. the contents of a after calling Fortran function FIB, should be returned to Python (use intent(out) attribute). In addition, an array a should be created dynamically using the size given by the input argument n (use depend(n) attribute to indicate dependence relation).
The content of a modified version of fib1.pyf (saved as fib2.pyf) is as follows:
! -*- f90 -*- python module fib2 interface subroutine fib(a,n) real*8 dimension(n),intent(out),depend(n) :: a integer intent(in) :: n end subroutine fib end interface end python module fib2
And finally, we build the extension module by running
f2py -c fib2.pyf fib1.f
>>> import fib2 >>> print fib2.fib.__doc__ fib - Function signature: a = fib(n) Required arguments: n : input int Return objects: a : rank-1 array('d') with bounds (n) >>> print fib2.fib(8) [ 0. 1. 1. 2. 3. 5. 8. 13.]
- Clearly, the signature of fib2.fib now corresponds to the intention of Fortran subroutine FIB more closely: given the number n, fib2.fib returns the first n Fibonacci numbers as a Numpy array. Also, the new Python signature fib2.fib rules out any surprises that we experienced with fib1.fib.
- Note that by default using single intent(out) also implies intent(hide). Argument that has intent(hide) attribute specified, will not be listed in the argument list of a wrapper function.
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. Namely, F2PY specific attributes can be inserted directly to Fortran source codes using the so-called F2PY directive. A F2PY directive defines special comment lines (starting with Cf2py, for example) which are ignored by Fortran compilers but F2PY interprets them as normal lines.
Here is shown a modified version of the example Fortran code, saved as fib3.f:
C FILE: FIB3.F SUBROUTINE FIB(A,N) C C CALCULATE FIRST N FIBONACCI NUMBERS C INTEGER N REAL*8 A(N) Cf2py intent(in) n Cf2py intent(out) a Cf2py depend(n) a DO I=1,N IF (I.EQ.1) THEN A(I) = 0.0D0 ELSEIF (I.EQ.2) THEN A(I) = 1.0D0 ELSE A(I) = A(I-1) + A(I-2) ENDIF ENDDO END C END FILE FIB3.F
Building the extension module can be now carried out in one command:
f2py -c -m fib3 fib3.f
Notice that the resulting wrapper to FIB is as “smart” as in previous case:
>>> import fib3 >>> print fib3.fib.__doc__ fib - Function signature: a = fib(n) Required arguments: n : input int Return objects: a : rank-1 array('d') with bounds (n) >>> print fib3.fib(8) [ 0. 1. 1. 2. 3. 5. 8. 13.]