Using F2PY bindings in Python¶
All wrappers for Fortran/C routines, common blocks, or for Fortran
90 module data generated by F2PY are exposed to Python as fortran
type objects. Routine wrappers are callable fortran
type objects
while wrappers to Fortran data have attributes referring to data
objects.
All fortran
type object have attribute _cpointer
that contains
CObject referring to the C pointer of the corresponding Fortran/C
function or variable in C level. Such CObjects can be used as a
callback argument of F2PY generated functions to bypass Python C/API
layer of calling Python functions from Fortran or C when the
computational part of such functions is implemented in C or Fortran
and wrapped with F2PY (or any other tool capable of providing CObject
of a function).
Consider a Fortran 77 file ftype.f
:
C FILE: FTYPE.F SUBROUTINE FOO(N) INTEGER N Cf2py integer optional,intent(in) :: n = 13 REAL A,X COMMON /DATA/ A,X(3) PRINT*, "IN FOO: N=",N," A=",A," X=[",X(1),X(2),X(3),"]" END C END OF FTYPE.F
and build a wrapper using f2py -c ftype.f -m ftype
.
In Python:
>>> import ftype >>> print ftype.__doc__ This module 'ftype' is auto-generated with f2py (version:2.28.198-1366). Functions: foo(n=13) COMMON blocks: /data/ a,x(3) . >>> type(ftype.foo),type(ftype.data) (<type 'fortran'>, <type 'fortran'>) >>> ftype.foo() IN FOO: N= 13 A= 0. X=[ 0. 0. 0.] >>> ftype.data.a = 3 >>> ftype.data.x = [1,2,3] >>> ftype.foo() IN FOO: N= 13 A= 3. X=[ 1. 2. 3.] >>> ftype.data.x[1] = 45 >>> ftype.foo(24) IN FOO: N= 24 A= 3. X=[ 1. 45. 3.] >>> ftype.data.x array([ 1., 45., 3.],'f')
Scalar arguments¶
In general, a scalar argument of a F2PY generated wrapper function can be ordinary Python scalar (integer, float, complex number) as well as an arbitrary sequence object (list, tuple, array, string) of scalars. In the latter case, the first element of the sequence object is passed to Fortran routine as a scalar argument.
Note that when type-casting is required and there is possible loss of information (e.g. when type-casting float to integer or complex to float), F2PY does not raise any exception. In complex to real type-casting only the real part of a complex number is used.
intent(inout)
scalar arguments are assumed to be array objects in
order to in situ changes to be effective. It is recommended to use
arrays with proper type but also other types work.
Consider the following Fortran 77 code:
C FILE: SCALAR.F SUBROUTINE FOO(A,B) REAL*8 A, B Cf2py intent(in) a Cf2py intent(inout) b PRINT*, " A=",A," B=",B PRINT*, "INCREMENT A AND B" A = A + 1D0 B = B + 1D0 PRINT*, "NEW A=",A," B=",B END C END OF FILE SCALAR.F
and wrap it using f2py -c -m scalar scalar.f
.
In Python:
>>> import scalar >>> print scalar.foo.__doc__ foo - Function signature: foo(a,b) Required arguments: a : input float b : in/output rank-0 array(float,'d') >>> scalar.foo(2,3) A= 2. B= 3. INCREMENT A AND B NEW A= 3. B= 4. >>> import numpy >>> a=numpy.array(2) # these are integer rank-0 arrays >>> b=numpy.array(3) >>> scalar.foo(a,b) A= 2. B= 3. INCREMENT A AND B NEW A= 3. B= 4. >>> print a,b # note that only b is changed in situ 2 4
String arguments¶
F2PY generated wrapper functions accept (almost) any Python object as
a string argument, str
is applied for non-string objects.
Exceptions are NumPy arrays that must have type code 'c'
or
'1'
when used as string arguments.
A string can have arbitrary length when using it as a string argument
to F2PY generated wrapper function. If the length is greater than
expected, the string is truncated. If the length is smaller that
expected, additional memory is allocated and filled with \0
.
Because Python strings are immutable, an intent(inout)
argument
expects an array version of a string in order to in situ changes to
be effective.
Consider the following Fortran 77 code:
C FILE: STRING.F SUBROUTINE FOO(A,B,C,D) CHARACTER*5 A, B CHARACTER*(*) C,D Cf2py intent(in) a,c Cf2py intent(inout) b,d PRINT*, "A=",A PRINT*, "B=",B PRINT*, "C=",C PRINT*, "D=",D PRINT*, "CHANGE A,B,C,D" A(1:1) = 'A' B(1:1) = 'B' C(1:1) = 'C' D(1:1) = 'D' PRINT*, "A=",A PRINT*, "B=",B PRINT*, "C=",C PRINT*, "D=",D END C END OF FILE STRING.F
and wrap it using f2py -c -m mystring string.f
.
Python session:
>>> import mystring >>> print mystring.foo.__doc__ foo - Function signature: foo(a,b,c,d) Required arguments: a : input string(len=5) b : in/output rank-0 array(string(len=5),'c') c : input string(len=-1) d : in/output rank-0 array(string(len=-1),'c') >>> import numpy >>> a=numpy.array('123') >>> b=numpy.array('123') >>> c=numpy.array('123') >>> d=numpy.array('123') >>> mystring.foo(a,b,c,d) A=123 B=123 C=123 D=123 CHANGE A,B,C,D A=A23 B=B23 C=C23 D=D23 >>> a.tostring(),b.tostring(),c.tostring(),d.tostring() ('123', 'B23', '123', 'D23')
Array arguments¶
In general, array arguments of F2PY generated wrapper functions accept
arbitrary sequences that can be transformed to NumPy array objects.
An exception is intent(inout)
array arguments that always must be
proper-contiguous and have proper type, otherwise an exception is
raised. Another exception is intent(inplace)
array arguments that
attributes will be changed in-situ if the argument has different type
than expected (see intent(inplace)
attribute for more
information).
In general, if a NumPy array is proper-contiguous and has a proper type then it is directly passed to wrapped Fortran/C function. Otherwise, an element-wise copy of an input array is made and the copy, being proper-contiguous and with proper type, is used as an array argument.
There are two types of proper-contiguous NumPy arrays:
- Fortran-contiguous arrays when data is stored column-wise, i.e. indexing of data as stored in memory starts from the lowest dimension;
- C-contiguous or simply contiguous arrays when data is stored row-wise, i.e. indexing of data as stored in memory starts from the highest dimension.
For one-dimensional arrays these notions coincide.
For example, a 2x2 array A
is Fortran-contiguous if its elements
are stored in memory in the following order:
A[0,0] A[1,0] A[0,1] A[1,1]
and C-contiguous if the order is as follows:
A[0,0] A[0,1] A[1,0] A[1,1]
To test whether an array is C-contiguous, use .iscontiguous()
method of NumPy arrays. To test for Fortran contiguity, all
F2PY generated extension modules provide a function
has_column_major_storage(<array>)
. This function is equivalent to
<array>.flags.f_contiguous
but more efficient.
Usually there is no need to worry about how the arrays are stored in memory and whether the wrapped functions, being either Fortran or C functions, assume one or another storage order. F2PY automatically ensures that wrapped functions get arguments with proper storage order; the corresponding algorithm is designed to make copies of arrays only when absolutely necessary. However, when dealing with very large multidimensional input arrays with sizes close to the size of the physical memory in your computer, then a care must be taken to use always proper-contiguous and proper type arguments.
To transform input arrays to column major storage order before passing
them to Fortran routines, use a function
as_column_major_storage(<array>)
that is provided by all F2PY
generated extension modules.
Consider Fortran 77 code:
C FILE: ARRAY.F SUBROUTINE FOO(A,N,M) C C INCREMENT THE FIRST ROW AND DECREMENT THE FIRST COLUMN OF A C INTEGER N,M,I,J REAL*8 A(N,M) Cf2py intent(in,out,copy) a Cf2py integer intent(hide),depend(a) :: n=shape(a,0), m=shape(a,1) DO J=1,M A(1,J) = A(1,J) + 1D0 ENDDO DO I=1,N A(I,1) = A(I,1) - 1D0 ENDDO END C END OF FILE ARRAY.F
and wrap it using f2py -c -m arr array.f -DF2PY_REPORT_ON_ARRAY_COPY=1
.
In Python:
>>> import arr >>> from numpy import array >>> print arr.foo.__doc__ foo - Function signature: a = foo(a,[overwrite_a]) Required arguments: a : input rank-2 array('d') with bounds (n,m) Optional arguments: overwrite_a := 0 input int Return objects: a : rank-2 array('d') with bounds (n,m) >>> a=arr.foo([[1,2,3], ... [4,5,6]]) copied an array using PyArray_CopyFromObject: size=6, elsize=8 >>> print a [[ 1. 3. 4.] [ 3. 5. 6.]] >>> a.iscontiguous(), arr.has_column_major_storage(a) (0, 1) >>> b=arr.foo(a) # even if a is proper-contiguous ... # and has proper type, a copy is made ... # forced by intent(copy) attribute ... # to preserve its original contents ... copied an array using copy_ND_array: size=6, elsize=8 >>> print a [[ 1. 3. 4.] [ 3. 5. 6.]] >>> print b [[ 1. 4. 5.] [ 2. 5. 6.]] >>> b=arr.foo(a,overwrite_a=1) # a is passed directly to Fortran ... # routine and its contents is discarded ... >>> print a [[ 1. 4. 5.] [ 2. 5. 6.]] >>> print b [[ 1. 4. 5.] [ 2. 5. 6.]] >>> a is b # a and b are actually the same objects 1 >>> print arr.foo([1,2,3]) # different rank arrays are allowed copied an array using PyArray_CopyFromObject: size=3, elsize=8 [ 1. 1. 2.] >>> print arr.foo([[[1],[2],[3]]]) copied an array using PyArray_CopyFromObject: size=3, elsize=8 [ [[ 1.] [ 3.] [ 4.]]] >>> >>> # Creating arrays with column major data storage order: ... >>> s = arr.as_column_major_storage(array([[1,2,3],[4,5,6]])) copied an array using copy_ND_array: size=6, elsize=4 >>> arr.has_column_major_storage(s) 1 >>> print s [[1 2 3] [4 5 6]] >>> s2 = arr.as_column_major_storage(s) >>> s2 is s # an array with column major storage order # is returned immediately 1
Call-back arguments¶
F2PY supports calling Python functions from Fortran or C codes.
Consider the following Fortran 77 code:
C FILE: CALLBACK.F SUBROUTINE FOO(FUN,R) EXTERNAL FUN INTEGER I REAL*8 R Cf2py intent(out) r R = 0D0 DO I=-5,5 R = R + FUN(I) ENDDO END C END OF FILE CALLBACK.F
and wrap it using f2py -c -m callback callback.f
.
In Python:
>>> import callback >>> print callback.foo.__doc__ foo - Function signature: r = foo(fun,[fun_extra_args]) Required arguments: fun : call-back function Optional arguments: fun_extra_args := () input tuple Return objects: r : float Call-back functions: def fun(i): return r Required arguments: i : input int Return objects: r : float >>> def f(i): return i*i ... >>> print callback.foo(f) 110.0 >>> print callback.foo(lambda i:1) 11.0
In the above example F2PY was able to guess accurately the signature
of a call-back function. However, sometimes F2PY cannot establish the
signature as one would wish and then the signature of a call-back
function must be modified in the signature file manually. Namely,
signature files may contain special modules (the names of such modules
contain a substring __user__
) that collect various signatures of
call-back functions. Callback arguments in routine signatures have
attribute external
(see also intent(callback)
attribute). To
relate a callback argument and its signature in __user__
module
block, use use
statement as illustrated below. The same signature
of a callback argument can be referred in different routine
signatures.
We use the same Fortran 77 code as in previous example but now
we’ll pretend that F2PY was not able to guess the signatures of
call-back arguments correctly. First, we create an initial signature
file callback2.pyf
using F2PY:
f2py -m callback2 -h callback2.pyf callback.f
Then modify it as follows
! -*- f90 -*- python module __user__routines interface function fun(i) result (r) integer :: i real*8 :: r end function fun end interface end python module __user__routines python module callback2 interface subroutine foo(f,r) use __user__routines, f=>fun external f real*8 intent(out) :: r end subroutine foo end interface end python module callback2
Finally, build the extension module using f2py -c callback2.pyf callback.f
.
An example Python session would be identical to the previous example except that argument names would differ.
Sometimes a Fortran package may require that users provide routines that the package will use. F2PY can construct an interface to such routines so that Python functions could be called from Fortran.
Consider the following `Fortran 77 subroutine`__ that takes an array
and applies a function func
to its elements.
subroutine calculate(x,n) cf2py intent(callback) func external func c The following lines define the signature of func for F2PY: cf2py real*8 y cf2py y = func(y) c cf2py intent(in,out,copy) x integer n,i real*8 x(n) do i=1,n x(i) = func(x(i)) end do end
It is expected that function func
has been defined
externally. In order to use a Python function as func
, it must
have an attribute intent(callback)
(it must be specified before
the external
statement).
Finally, build an extension module using f2py -c -m foo calculate.f
In Python:
>>> import foo >>> foo.calculate(range(5), lambda x: x*x) array([ 0., 1., 4., 9., 16.]) >>> import math >>> foo.calculate(range(5), math.exp) array([ 1. , 2.71828175, 7.38905621, 20.08553696, 54.59814835])
The function is included as an argument to the python function call to the Fortran subroutine even though it was not in the Fortran subroutine argument list. The “external” refers to the C function generated by f2py, not the python function itself. The python function must be supplied to the C function.
The callback function may also be explicitly set in the module. Then it is not necessary to pass the function in the argument list to the Fortran function. This may be desired if the Fortran function calling the python callback function is itself called by another Fortran function.
Consider the following Fortran 77 subroutine:
subroutine f1() print *, "in f1, calling f2 twice.." call f2() call f2() return end subroutine f2() cf2py intent(callback, hide) fpy external fpy print *, "in f2, calling f2py.." call fpy() return end
and wrap it using f2py -c -m pfromf extcallback.f
.
In Python:
>>> import pfromf >>> pfromf.f2() Traceback (most recent call last): File "<stdin>", line 1, in ? pfromf.error: Callback fpy not defined (as an argument or module pfromf attribute). >>> def f(): print "python f" ... >>> pfromf.fpy = f >>> pfromf.f2() in f2, calling f2py.. python f >>> pfromf.f1() in f1, calling f2 twice.. in f2, calling f2py.. python f in f2, calling f2py.. python f >>>
Resolving arguments to call-back functions¶
F2PY generated interface is very flexible with respect to call-back
arguments. For each call-back argument an additional optional
argument <name>_extra_args
is introduced by F2PY. This argument
can be used to pass extra arguments to user provided call-back
arguments.
If a F2PY generated wrapper function expects the following call-back argument:
def fun(a_1,...,a_n):
...
return x_1,...,x_k
but the following Python function
def gun(b_1,...,b_m):
...
return y_1,...,y_l
is provided by a user, and in addition,
fun_extra_args = (e_1,...,e_p)
is used, then the following rules are applied when a Fortran or C
function calls the call-back argument gun
:
- If
p == 0
thengun(a_1, ..., a_q)
is called, hereq = min(m, n)
. - If
n + p <= m
thengun(a_1, ..., a_n, e_1, ..., e_p)
is called. - If
p <= m < n + p
thengun(a_1, ..., a_q, e_1, ..., e_p)
is called, hereq=m-p
. - If
p > m
thengun(e_1, ..., e_m)
is called. - If
n + p
is less than the number of required arguments togun
then an exception is raised.
The function gun
may return any number of objects as a tuple. Then
following rules are applied:
- If
k < l
, theny_{k + 1}, ..., y_l
are ignored. - If
k > l
, then onlyx_1, ..., x_l
are set.
Common blocks¶
F2PY generates wrappers to common
blocks defined in a routine
signature block. Common blocks are visible by all Fortran codes linked
with the current extension module, but not to other extension modules
(this restriction is due to how Python imports shared libraries). In
Python, the F2PY wrappers to common
blocks are fortran
type
objects that have (dynamic) attributes related to data members of
common blocks. When accessed, these attributes return as NumPy array
objects (multidimensional arrays are Fortran-contiguous) that
directly link to data members in common blocks. Data members can be
changed by direct assignment or by in-place changes to the
corresponding array objects.
Consider the following Fortran 77 code:
C FILE: COMMON.F SUBROUTINE FOO INTEGER I,X REAL A COMMON /DATA/ I,X(4),A(2,3) PRINT*, "I=",I PRINT*, "X=[",X,"]" PRINT*, "A=[" PRINT*, "[",A(1,1),",",A(1,2),",",A(1,3),"]" PRINT*, "[",A(2,1),",",A(2,2),",",A(2,3),"]" PRINT*, "]" END C END OF COMMON.F
and wrap it using f2py -c -m common common.f
.
In Python:
>>> import common >>> print common.data.__doc__ i - 'i'-scalar x - 'i'-array(4) a - 'f'-array(2,3) >>> common.data.i = 5 >>> common.data.x[1] = 2 >>> common.data.a = [[1,2,3],[4,5,6]] >>> common.foo() I= 5 X=[ 0 2 0 0] A=[ [ 1., 2., 3.] [ 4., 5., 6.] ] >>> common.data.a[1] = 45 >>> common.foo() I= 5 X=[ 0 2 0 0] A=[ [ 1., 2., 3.] [ 45., 45., 45.] ] >>> common.data.a # a is Fortran-contiguous array([[ 1., 2., 3.], [ 45., 45., 45.]],'f')
Fortran 90 module data¶
The F2PY interface to Fortran 90 module data is similar to Fortran 77 common blocks.
Consider the following Fortran 90 code:
module mod integer i integer :: x(4) real, dimension(2,3) :: a real, allocatable, dimension(:,:) :: b contains subroutine foo integer k print*, "i=",i print*, "x=[",x,"]" print*, "a=[" print*, "[",a(1,1),",",a(1,2),",",a(1,3),"]" print*, "[",a(2,1),",",a(2,2),",",a(2,3),"]" print*, "]" print*, "Setting a(1,2)=a(1,2)+3" a(1,2) = a(1,2)+3 end subroutine foo end module mod
and wrap it using f2py -c -m moddata moddata.f90
.
In Python:
>>> import moddata >>> print moddata.mod.__doc__ i - 'i'-scalar x - 'i'-array(4) a - 'f'-array(2,3) foo - Function signature: foo() >>> moddata.mod.i = 5 >>> moddata.mod.x[:2] = [1,2] >>> moddata.mod.a = [[1,2,3],[4,5,6]] >>> moddata.mod.foo() i= 5 x=[ 1 2 0 0 ] a=[ [ 1.000000 , 2.000000 , 3.000000 ] [ 4.000000 , 5.000000 , 6.000000 ] ] Setting a(1,2)=a(1,2)+3 >>> moddata.mod.a # a is Fortran-contiguous array([[ 1., 5., 3.], [ 4., 5., 6.]],'f')
Allocatable arrays¶
F2PY has basic support for Fortran 90 module allocatable arrays.
Consider the following Fortran 90 code:
module mod real, allocatable, dimension(:,:) :: b contains subroutine foo integer k if (allocated(b)) then print*, "b=[" do k = 1,size(b,1) print*, b(k,1:size(b,2)) enddo print*, "]" else print*, "b is not allocated" endif end subroutine foo end module mod
and wrap it using f2py -c -m allocarr allocarr.f90
.
In Python:
>>> import allocarr >>> print allocarr.mod.__doc__ b - 'f'-array(-1,-1), not allocated foo - Function signature: foo() >>> allocarr.mod.foo() b is not allocated >>> allocarr.mod.b = [[1,2,3],[4,5,6]] # allocate/initialize b >>> allocarr.mod.foo() b=[ 1.000000 2.000000 3.000000 4.000000 5.000000 6.000000 ] >>> allocarr.mod.b # b is Fortran-contiguous array([[ 1., 2., 3.], [ 4., 5., 6.]],'f') >>> allocarr.mod.b = [[1,2,3],[4,5,6],[7,8,9]] # reallocate/initialize b >>> allocarr.mod.foo() b=[ 1.000000 2.000000 3.000000 4.000000 5.000000 6.000000 7.000000 8.000000 9.000000 ] >>> allocarr.mod.b = None # deallocate array >>> allocarr.mod.foo() b is not allocated