niceSOFT/numpy: The fundamental package for scientific computing with Python.

doc/source/f2py/python-usage.rst
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Rohit Goswami
DOC: Note regarding modified fortran wrappers
10 ноя 2024, 00:11
10 ноя 2024, 00:11f21d18a
==================================
Using F2PY bindings in Python
==================================

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 :ref:`f2py-examples`.

Fortran type objects
====================

All wrappers for Fortran/C routines, common blocks, or 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 objects have an attribute ``_cpointer`` that contains a
:c:type:`PyCapsule` referring to the C pointer of the corresponding Fortran/C function
or variable at the C level. Such ``PyCapsule`` 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 ``PyCapsule``
containing a function).

Consider a Fortran 77 file ```ftype.f``:

.. literalinclude:: ./code/ftype.f
  :language: fortran

and a wrapper built using ``f2py -c ftype.f -m ftype``.

In Python, you can observe the types of ``foo`` and ``data``, and how to access
individual objects of the wrapped Fortran code.

.. literalinclude:: ./code/results/ftype_session.dat
  :language: python


Scalar arguments
=================

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::

   * 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.

     * For 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 have *in situ* changes be effective. It is recommended to use
     arrays with proper type but also other types work. :ref:`Read more about
     the intent attribute <f2py-attributes>`.

Consider the following Fortran 77 code:

.. literalinclude:: ./code/scalar.f
  :language: fortran

and wrap it using ``f2py -c -m scalar scalar.f``.

In Python:

.. literalinclude:: ./code/results/scalar_session.dat
  :language: python


String arguments
=================

F2PY generated wrapper functions accept almost any Python object as a string
argument, since ``str`` is applied for non-string objects. Exceptions are NumPy
arrays that must have type code ``'S1'`` or ``'b'`` (corresponding to the
outdated ``'c'`` or ``'1'`` typecodes, respectively) when used as string
arguments. See :ref:`arrays.scalars` 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 ``\0``.

.. TODO: review this section once https://github.com/numpy/numpy/pull/19388 is merged.

Because Python strings are immutable, an ``intent(inout)`` argument expects an
array version of a string in order to have *in situ* changes be effective.

Consider the following Fortran 77 code:

.. literalinclude:: ./code/string.f
  :language: fortran

and wrap it using ``f2py -c -m mystring string.f``.

Python session:

.. literalinclude:: ./code/results/string_session.dat
  :language: python


Array arguments
================

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:

* ``intent(inout)`` array arguments must always be
  :term:`proper-contiguous <contiguous>` and have a compatible ``dtype``,
  otherwise an exception is raised.
* ``intent(inplace)`` array arguments  will be changed *in situ* if the argument
  has a different type than expected (see the ``intent(inplace)``
  :ref:`attribute <f2py-attributes>` for more information).

In general, if a NumPy array is :term:`proper-contiguous <contiguous>` 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 `numpy.asfortranarray`.

Consider the following Fortran 77 code:

.. literalinclude:: ./code/array.f
  :language: fortran

and wrap it using ``f2py -c -m arr array.f -DF2PY_REPORT_ON_ARRAY_COPY=1``.

In Python:

.. literalinclude:: ./code/results/array_session.dat
  :language: python

.. _Call-back arguments:

Call-back arguments
====================

F2PY supports calling Python functions from Fortran or C codes.

Consider the following Fortran 77 code:

.. literalinclude:: ./code/callback.f
  :language: fortran

and wrap it using ``f2py -c -m callback callback.f``.

In Python:

.. literalinclude:: ./code/results/callback_session.dat
  :language: python

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 ``__user__`` sub-string) that define the
various signatures for call-back functions.  Callback arguments in routine
signatures have the ``external`` attribute (see also the ``intent(callback)``
:ref:`attribute <f2py-attributes>`). To relate a callback argument with its
signature in a ``__user__`` module block, a ``use`` 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 ``callback2.pyf`` using F2PY::

    f2py -m callback2 -h callback2.pyf callback.f

Then modify it as follows

.. include:: ./code/callback2.pyf
  :literal:

Finally, we build the extension module using
``f2py -c callback2.pyf callback.f``.

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 ``func`` to its elements.

.. literalinclude:: ./code/calculate.f
  :language: fortran

The Fortran code expects that the function ``func`` has been defined externally.
In order to use a Python function for ``func``, it must have an attribute
``intent(callback)`` and it must be specified before the ``external`` statement.

Finally, build an extension module using ``f2py -c -m foo calculate.f``

In Python:

.. literalinclude:: ./code/results/calculate_session.dat
  :language: python

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" keyword refers to the C function generated by f2py, not the
Python function itself. The python function is essentially being 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:

.. literalinclude:: ./code/extcallback.f
  :language: fortran

and wrap it using ``f2py -c -m pfromf extcallback.f``.

In Python:

.. literalinclude:: ./code/results/extcallback_session.dat
  :language: python

.. note::

   When using modified Fortran code via ``callstatement`` or other directives,
   the wrapped Python function must be called as a callback, otherwise only the
   bare Fortran routine will be used. For more details, see
   https://github.com/numpy/numpy/issues/26681#issuecomment-2466460943

Resolving arguments to call-back functions
------------------------------------------

F2PY generated interfaces are 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 functions.

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
evaluates the call-back argument ``gun``:

* If ``p == 0`` then ``gun(a_1, ..., a_q)`` is called, here
  ``q = min(m, n)``.
* If ``n + p <= m`` then ``gun(a_1, ..., a_n, e_1, ..., e_p)`` is called.
* If ``p <= m < n + p`` then ``gun(a_1, ..., a_q, e_1, ..., e_p)`` is called,
  and here ``q=m-p``.
* If ``p > m`` then ``gun(e_1, ..., e_m)`` is called.
* If ``n + p`` is less than the number of required arguments to ``gun`` then an
  exception is raised.

If the function ``gun`` may return any number of objects as a tuple; then the
following rules are applied:

* If ``k < l``, then ``y_{k + 1}, ..., y_l`` are ignored.
* If ``k > l``, then only ``x_1, ..., x_l`` are set.


Common blocks
==============

F2PY generates wrappers to ``common`` blocks defined in a routine signature
block. Common blocks are visible to all Fortran codes linked to the current
extension module, but not to other extension modules (this restriction is due to
the way Python imports shared libraries). In Python, the F2PY wrappers to
``common`` blocks are ``fortran`` type objects that have (dynamic) attributes
related to the data members of the common blocks. When accessed, these
attributes return as NumPy array objects (multidimensional arrays are
Fortran-contiguous) which 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:

.. literalinclude:: ./code/common.f
  :language: fortran

and wrap it using ``f2py -c -m common common.f``.

In Python:

.. literalinclude:: ./code/results/common_session.dat
  :language: python


Fortran 90 module data
=======================

The F2PY interface to Fortran 90 module data is similar to the handling of
Fortran 77 common blocks.

Consider the following Fortran 90 code:

.. literalinclude:: ./code/moddata.f90
  :language: fortran

and wrap it using ``f2py -c -m moddata moddata.f90``.

In Python:

.. literalinclude:: ./code/results/moddata_session.dat
  :language: python


Allocatable arrays
===================

F2PY has basic support for Fortran 90 module allocatable arrays.

Consider the following Fortran 90 code:

.. literalinclude:: ./code/allocarr.f90
  :language: fortran

and wrap it using ``f2py -c -m allocarr allocarr.f90``.

In Python:

.. literalinclude:: ./code/results/allocarr_session.dat
  :language: python