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_distutilsthat 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, save it as fib1.f:
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
python -m numpy.f2py -c fib1.f -m fib1
This command builds (see
-c flag, execute
python -m numpy.f2py without
arguments to see the explanation of command line options) an extension
-m flag) to the current directory. Now, in
Python the Fortran subroutine
FIB is accessible via
>>> import numpy >>> 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 = 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
nis the dimension of the first array argument
a. Since by default all arguments are input-only arguments, F2PY concludes that
ncan be optional with the default value
One can use different values for optional
>>> 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
>>> 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 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=floatis considered accidental.
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.fibin Python is very similar to using
FIBin 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
python -m numpy.f2py fib1.f -m fib2 -h fib1.pyf
The signature file is saved to
-hflag) 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
nis an input argument (use
intent(in)attribute) and that the result, i.e. the contents of
aafter calling Fortran function
FIB, should be returned to Python (use
intent(out)attribute). In addition, an array
ashould be created dynamically using the size given by the input argument
depend(n)attribute to indicate dependence relation).
The content of a modified version of
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
python -m numpy.f2py -c fib2.pyf fib1.f
>>> 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.]
Clearly, the signature of
fib2.fibnow corresponds to the intention of Fortran subroutine
FIBmore closely: given the number
fib2.fibreturns the first
nFibonacci numbers as a NumPy array. Also, the new Python signature
fib2.fibrules out any surprises that we experienced with
Note that by default using single
intent(hide). Arguments that have the
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
example) which are ignored by Fortran compilers but F2PY interprets
them as normal lines.
Here is shown a modified version of the previous Fortran code, save it
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:
python -m numpy.f2py -c -m fib3 fib3.f
Notice that the resulting wrapper to
FIB is as “smart” as in
>>> 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.]