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<!DOCTYPE html>

<head>

<title>Using F2PY bindings in Python — NumPy v2.5.dev0 Manual</title>

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<h1>Using F2PY bindings in Python<a class="headerlink" href="#using-f2py-bindings-in-python" title="Link to this heading">#</a></h1>

In this page, you can find a full description and a few examples of common usage

patterns for F2PY with Python and different argument types. For more examples

and use cases, see <a class="reference internal" href="f2py-examples.html#f2py-examples">F2PY examples</a>.

<h2>Fortran type objects<a class="headerlink" href="#fortran-type-objects" title="Link to this heading">#</a></h2>

All wrappers for Fortran/C routines, common blocks, or Fortran 90 module data

generated by F2PY are exposed to Python as <code class="docutils literal notranslate">fortran</code> type objects. Routine

wrappers are callable <code class="docutils literal notranslate">fortran</code> type objects while wrappers to Fortran data

have attributes referring to data objects.

All <code class="docutils literal notranslate">fortran</code> type objects have an attribute <code class="docutils literal notranslate">_cpointer</code> that contains a

<a class="reference external" href="https://docs.python.org/3/c-api/capsule.html#c.PyCapsule" title="(in Python v3.14)"><code class="xref c c-type docutils literal notranslate">PyCapsule</code></a> referring to the C pointer of the corresponding Fortran/C function

or variable at the C level. Such <code class="docutils literal notranslate">PyCapsule</code> objects can be used as callback

arguments for F2PY generated functions to bypass the Python C/API layer for

calling Python functions from Fortran or C. This can be useful when the

computational aspects of such functions are implemented in C or Fortran and

wrapped with F2PY (or any other tool capable of providing the <code class="docutils literal notranslate">PyCapsule</code>

containing a function).

Consider a Fortran 77 file <code class="docutils literal notranslate">`ftype.f</code>:

<div class="highlight-fortran notranslate"><div class="highlight"><pre>C FILE: FTYPE.F

SUBROUTINE FOO(N)

INTEGER N

Cf2py integer optional,intent(in) :: n = 13

REAL A,X

COMMON /DATA/ A,X(3)

C PRINT*, "IN FOO: N=",N," A=",A," X=[",X(1),X(2),X(3),"]"

END

C END OF FTYPE.F

</pre></div>

</div>

and a wrapper built using <code class="docutils literal notranslate">f2py -c ftype.f -m ftype</code>.

In Python, you can observe the types of <code class="docutils literal notranslate">foo</code> and <code class="docutils literal notranslate">data</code>, and how to access

individual objects of the wrapped Fortran code.

<div class="highlight-python notranslate"><div class="highlight"><pre>>>> import ftype

>>> print(ftype.__doc__)

This module 'ftype' is auto-generated with f2py (version:2).

Functions:

foo(n=13)

COMMON blocks:

/data/ a,x(3)

.

>>> type(ftype.foo), type(ftype.data)

(<class 'fortran'>, <class '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.], dtype=float32)

</pre></div>

</div>

</section>

<h2>Scalar arguments<a class="headerlink" href="#scalar-arguments" title="Link to this heading">#</a></h2>

In general, a scalar argument for a F2PY generated wrapper function can be an

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 the Fortran routine as a

scalar argument.

Note

<li>When type-casting is required and there is possible loss of information via

narrowing e.g. when type-casting float to integer or complex to float, F2PY

does not raise an exception.

<ul>

<li>For complex to real type-casting only the real part of a complex number

is used.</li>

</ul>

</li>

<li><code class="docutils literal notranslate">intent(inout)</code> scalar arguments are assumed to be array objects in

order to have in situ changes be effective. It is recommended to use

arrays with proper type but also other types work. <a class="reference internal" href="signature-file.html#f2py-attributes">Read more about

the intent attribute</a>.</li>

</ul>

</div>

Consider the following Fortran 77 code:

<div class="highlight-fortran notranslate"><div class="highlight"><pre>C FILE: SCALAR.F

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

</pre></div>

</div>

and wrap it using <code class="docutils literal notranslate">f2py -c -m scalar scalar.f</code>.

In Python:

<div class="highlight-python notranslate"><div class="highlight"><pre>>>> import scalar

>>> print(scalar.foo.__doc__)

foo(a,b)

Wrapper for ``foo``.

Parameters

----------

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

</pre></div>

</div>

</section>

<h2>String arguments<a class="headerlink" href="#string-arguments" title="Link to this heading">#</a></h2>

F2PY generated wrapper functions accept almost any Python object as a string

argument, since <code class="docutils literal notranslate">str</code> is applied for non-string objects. Exceptions are NumPy

arrays that must have type code <code class="docutils literal notranslate">'S1'</code> or <code class="docutils literal notranslate">'b'</code> (corresponding to the

outdated <code class="docutils literal notranslate">'c'</code> or <code class="docutils literal notranslate">'1'</code> typecodes, respectively) when used as string

arguments. See <a class="reference internal" href="../reference/arrays.scalars.html#arrays-scalars">Scalars</a> for more information on these typecodes.

A string can have an arbitrary length when used as a string argument for an F2PY

generated wrapper function. If the length is greater than expected, the string

is truncated silently. If the length is smaller than expected, additional memory

is allocated and filled with <code class="docutils literal notranslate">\0</code>.

Because Python strings are immutable, an <code class="docutils literal notranslate">intent(inout)</code> argument expects an

array version of a string in order to have in situ changes be effective.

Consider the following Fortran 77 code:

<div class="highlight-fortran notranslate"><div class="highlight"><pre>C FILE: STRING.F

SUBROUTINE FOO(A,B,C,D)

CHARACTER*5 A, B

CHARACTER*(*) C,D

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'

END

C END OF FILE STRING.F

</pre></div>

</div>

and wrap it using <code class="docutils literal notranslate">f2py -c -m mystring string.f</code>.

Python session:

<div class="highlight-python notranslate"><div class="highlight"><pre>>>> import mystring

>>> print(mystring.foo.__doc__)

foo(a,b,c,d)

Wrapper for ``foo``.

Parameters

----------

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')

>>> from numpy import array

>>> a = array(b'123\0\0')

>>> b = array(b'123\0\0')

>>> c = array(b'123')

>>> d = array(b'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[()], b[()], c[()], d[()]

(b'123', b'B23', b'123', b'D2')

</pre></div>

</div>

</section>

<h2>Array arguments<a class="headerlink" href="#array-arguments" title="Link to this heading">#</a></h2>

In general, array arguments for F2PY generated wrapper functions accept

arbitrary sequences that can be transformed to NumPy array objects. There are

two notable exceptions:

<li><code class="docutils literal notranslate">intent(inout)</code> array arguments must always be

<a class="reference internal" href="../glossary.html#term-contiguous">proper-contiguous</a> and have a compatible <code class="docutils literal notranslate">dtype</code>,

otherwise an exception is raised.</li>

<li><code class="docutils literal notranslate">intent(inplace)</code> array arguments must be arrays. If these have

incompatible order or size, a converted copy is passed in, which is

copied back into the original array on exit (see the <code class="docutils literal notranslate">intent(inplace)</code>

<a class="reference internal" href="signature-file.html#f2py-attributes">attribute</a> for more information).</li>

</ul>

In general, if a NumPy array is <a class="reference internal" href="../glossary.html#term-contiguous">proper-contiguous</a> and has

a proper type then it is directly passed to the wrapped Fortran/C function.

Otherwise, an element-wise copy of the input array is made and the copy, being

proper-contiguous and with proper type, is used as the array argument.

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 the proper storage order; the underlying 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 care must be taken to ensure the usage of

proper-contiguous and proper type arguments.

To transform input arrays to column major storage order before passing

them to Fortran routines, use the function <a class="reference internal" href="../reference/generated/numpy.asfortranarray.html#numpy.asfortranarray" title="numpy.asfortranarray"><code class="xref py py-obj docutils literal notranslate">numpy.asfortranarray</code></a>.

Consider the following Fortran 77 code:

<div class="highlight-fortran notranslate"><div class="highlight"><pre>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

</pre></div>

</div>

and wrap it using <code class="docutils literal notranslate">f2py -c -m arr array.f -DF2PY_REPORT_ON_ARRAY_COPY=1</code>.

In Python:

<div class="highlight-python notranslate"><div class="highlight"><pre>>>> import arr

>>> from numpy import asfortranarray

a = foo(a,[overwrite_a])

Wrapper for ``foo``.

Parameters

----------

a : input rank-2 array('d') with bounds (n,m)

Other Parameters

----------------

overwrite_a : input int, optional

Default: 0

Returns

-------

a : rank-2 array('d') with bounds (n,m)

>>> a = arr.foo([[1, 2, 3],

... [4, 5, 6]])

created an array from object

>>> print(a)

[[ 1. 3. 4.]

[ 3. 5. 6.]]

>>> a.flags.c_contiguous

False

>>> a.flags.f_contiguous

True

# 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: 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

True

>>> print(arr.foo([1, 2, 3])) # different rank arrays are allowed

created an array from object

[ 1. 1. 2.]

>>> print(arr.foo([[[1], [2], [3]]]))

created an array from object

[[[ 1.]

[ 1.]

[ 2.]]]

>>>

>>> # Creating arrays with column major data storage order:

...

>>> s = asfortranarray([[1, 2, 3], [4, 5, 6]])

>>> s.flags.f_contiguous

True

>>> print(s)

[[1 2 3]

[4 5 6]]

>>> s2 = asfortranarray(s)

>>> s2 is s # an array with column major storage order

# is returned immediately

True

>>> # Note that arr.foo returns a column major data storage order array:

...

>>> s3 = ascontiguousarray(s)

>>> s3.flags.f_contiguous

False

>>> s3.flags.c_contiguous

True

>>> s3 = arr.foo(s3)

copied an array: size=6, elsize=8

>>> s3.flags.f_contiguous

True

>>> s3.flags.c_contiguous

False

</pre></div>

</div>

</section>

<h2>Call-back arguments<a class="headerlink" href="#call-back-arguments" title="Link to this heading">#</a></h2>

F2PY supports calling Python functions from Fortran or C codes.

Consider the following Fortran 77 code:

<div class="highlight-fortran notranslate"><div class="highlight"><pre>C FILE: CALLBACK.F

SUBROUTINE FOO(FUN,R)

EXTERNAL FUN

INTEGER I

REAL*8 R, FUN

Cf2py intent(out) r

R = 0D0

DO I=-5,5

R = R + FUN(I)

ENDDO

END

C END OF FILE CALLBACK.F

</pre></div>

</div>

and wrap it using <code class="docutils literal notranslate">f2py -c -m callback callback.f</code>.

In Python:

<div class="highlight-python notranslate"><div class="highlight"><pre>>>> import callback

>>> print(callback.foo.__doc__)

r = foo(fun,[fun_extra_args])

Wrapper for ``foo``.

Parameters

----------

fun : call-back function

Other Parameters

----------------

fun_extra_args : input tuple, optional

Default: ()

Returns

-------

r : float

Notes

-----

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

</pre></div>

</div>

In the above example F2PY was able to guess accurately the signature

of the call-back function. However, sometimes F2PY cannot establish the

appropriate signature; in these cases the signature of the call-back

function must be explicitly defined in the signature file.

To facilitate this, signature files may contain special modules (the names of

these modules contain the special <code class="docutils literal notranslate">__user__</code> sub-string) that define the

various signatures for call-back functions. Callback arguments in routine

signatures have the <code class="docutils literal notranslate">external</code> attribute (see also the <code class="docutils literal notranslate">intent(callback)</code>

<a class="reference internal" href="signature-file.html#f2py-attributes">attribute</a>). To relate a callback argument with its

signature in a <code class="docutils literal notranslate">__user__</code> module block, a <code class="docutils literal notranslate">use</code> statement can be utilized as

illustrated below. The same signature for a callback argument can be referred to

in different routine signatures.

We use the same Fortran 77 code as in the previous example but now

we will pretend that F2PY was not able to guess the signatures of

call-back arguments correctly. First, we create an initial signature

file <code class="docutils literal notranslate">callback2.pyf</code> using F2PY:

<div class="highlight-default notranslate"><div class="highlight"><pre>f2py -m callback2 -h callback2.pyf callback.f

</pre></div>

</div>

Then modify it as follows

<div class="highlight-default notranslate"><div class="highlight"><pre>! -*- 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

</pre></div>

</div>

Finally, we build the extension module using

<code class="docutils literal notranslate">f2py -c callback2.pyf callback.f</code>.

An example Python session for this snippet would be identical to the previous

example except that the 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 can be called from Fortran.

Consider the following Fortran 77 subroutine that takes an array as its input

and applies a function <code class="docutils literal notranslate">func</code> to its elements.

<div class="highlight-fortran notranslate"><div class="highlight"><pre> 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), func

do i=1,n

x(i) = func(x(i))

end do

end

</pre></div>

</div>

The Fortran code expects that the function <code class="docutils literal notranslate">func</code> has been defined externally.

In order to use a Python function for <code class="docutils literal notranslate">func</code>, it must have an attribute

<code class="docutils literal notranslate">intent(callback)</code> and it must be specified before the <code class="docutils literal notranslate">external</code> statement.

Finally, build an extension module using <code class="docutils literal notranslate">f2py -c -m foo calculate.f</code>

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