gfort2py

Library to allow calling Fortran code from Python. Requires gfortran>=8.0, Works with python >= 3.7

Build

pip3 install -r requirements.txt
python3 setup.py install --user

or install via pip

python -m pip install --upgrade --user gfort2py

Why use this over other Fortran to Python translators?

gfort2py has three main aims:

1. Make it trivially easy to call Fortran code from Python
2. Minimise the number of changes needed in the Fortran code to make this work.
3. Support as many Fortran features as possible.

We achieve this by tightly coupling the code to the gfortran compiler, by doing so we can easily embed assumptions about how advanced Fortran features work which makes development easier and minimises the number of changes needed on the Fortran side.

gfort2py use the gfortran mod files to translate your Fortran code's ABI to Python-compatible types using Python's ctype library. By using the mod file we can determine the call signature of all procedures, components of derived types, and the size and shapes of all module-level variables. As long as your code is inside a Fortran module, no other changes are needed to your Fortran code.

The downside to this approach is that we are tightly tied to gfortran's ABI, which means we can not support other non-gfortran compilers and we do not support all versions of gfortran. When gfortran next breaks its ABI (which happens rarely, the last break was gfortran 8) we will re-evaluate our supported gfortran versions.

Using

Fortran side

Your Fortran code must be inside a module and then compiled as a shared library.

On linux:

gfortran -fPIC -shared -c file.f90
gfortran -fPIC -shared -o libfile.so file.f90

On MacOS:

gfortran -dynamiclib -c file.f90
gfortran -dynamiclib -o libfile.dylib file.f90

On Windows:

gfortran -shared -c file.f90
gfortran -shared -o libfile.dll file.f90

If your code comes as program that does everything, then just turn the program into a function call inside a module, then create a new file with your program that uses the module and calls the function you just made.

If the shared library needs other shared libraries you will need to set LD_LIBRARY_PATH environment variable, and it is also recommended to run chrpath on the shared libraries so you can access them from anywhere.

Python side

import gfort2py as gf

SHARED_LIB_NAME=f'./test_mod.{gf.lib_ext()}' # Handle whether on Linux or Mac
MOD_FILE_NAME='tester.mod'

x=gf.fFort(SHARED_LIB_NAME,MOD_FILE_NAME)

x now contains all variables, parameters and procedures from the module (tab completable).

Functions

y = x.func_name(a,b,c)

Will call the Fortran function with variables a,b,c and returns the result in y.

y will be named tuple which contains (result, args). Where result is a python object for the return value (0 if a subroutine) and where args is a dict containing all arguments passed to the procedure (both those with intent (in) which will be unchanged and intent(inout/out) which may have changed).

Variables

x.some_var = 1

Sets a module variable to 1, will attempt to coerce it to the Fortran type

x.some_var

Will return a Python object

Optional arguments that are not present should be passed as a Python None.

Arrays

Arrays should be passed as a NumPy array of the correct size and shape.

Remember that Fortran by default has 1-based array numbering while Numpy is 0-based.

If a procedure expects an unallocated array, then pass None as the argument, otherwise pass an array of the correct shape.

Derived types

Derived types can be set with a dict

x.my_dt={'x':1,'y':'abc'}

y=x.my_dt
y['x']
y['y']

If the derived type contains another derived type then you can set a dict in a dict

x.my_dt={'x':1,'y':{'a':1}}

When setting the components of a derived type you do not need to specify all of them at the same time.

If you have an array of derived types

type(my_type), dimension(5) :: my_dt
type(my_type), dimension(5,5) :: my_dt2

Elements can be accessed via an index:

x.my_dt[0]['x']
x.my_dt2[0,0]['x']

You can only access one component at a time (i.e no striding [:]). Allocatable derived types are not yet supported.

Derived types that are dummy arguments to a procedure are returned as a fDT type. This is a dict-like object where the components can only be accessed via the item interface ['x'] and not as attributes .x. This was done so that we do not have a name collision between Python functions (keys, items etc) and any Fortran-derived type components.

You can pass a fDT as an argument to a procedure.

Testing

pytest

or

tox

To run unit tests

Things that work

Module variables

• Scalars
• Parameters
• Characters
• Explicit size arrays
• Complex numbers (Scalar and parameters)
• Getting a pointer
• Getting the value of a pointer
• Allocatable arrays
• Derived types
• Nested derived types
• Explicit Arrays of derived types
• Allocatable Arrays of derived types
• Procedure pointers inside derived types
• Derived types with dimension(:) array components (pointer, allocatable, target)
• Allocatable strings (partial)
• Explicit Arrays of strings
• Allocatable arrays of strings
• Classes
• Abstract interfaces
• Common blocks (partial)
• Equivalences
• Namelists
• Quad precision variables
• function overloading

Procedures

• Basic calling (no arguments)
• Argument passing (scalars)
• Argument passing (strings)
• Argument passing (explicit arrays)
• Argument passing (assumed size arrays)
• Argument passing (assumed shape arrays)
• Argument passing (allocatable arrays)
• Argument passing (derived types)
• Argument intents (in, out, inout and none)
• Passing characters of fixed size (len=10 or len=* etc)
• Functions that return a character as their result
• Allocatable strings (Only for things that do not get altered inside the procedure)
• Explicit arrays of strings
• Allocatable arrays of strings
• Pointer arguments
• Optional arguments
• Value arguments
• Keyword arguments
• Generic/Elemental functions
• Functions as an argument
• Unary operations (arguments that involve an expression to evaluate like dimension(n+1) or dimension((2*n)+1))
• Functions returning an explicit array as their result

Accessing common block elements

There's no direct way to access the common block elements, but if you declare the common block as a module variable you may access the elements by their name:

module my_mod
    implicit none
    
    integer :: a,b,c
    common /comm1/ a,b,c
    

Elements in the common block can thus be accessed as:

x.a
x.b
x.c

Accessing module file data

For those wanting to explore the module file format, there is a routine mod_info available from the top-level gfort2py module:

module = gf.mod_info('file.mod')

That will parse the mod file and convert it into an intermediate format inside module.

Variables or procedures can be looked up via the item interface (I also recommend using pprint for easier viewing):

from pprint import pprint

pprint(module['a_variable'])

Accessing the list of all available components can be had via module.keys().

Contributing

Bug reports are of course welcome and PR's should target the main branch.

For those wanting to get more involved, adding Fortran examples to the test suite of currently untested or unsupported features would be helpful. Bonus points if you also provide a Python test case (that can be marked @pytest.mark.skip if it does not work) that demonstrates the proposed interface to the new Fortran feature. Features with test cases will move higher in the order of things I add to the code.

See how to write a test case for details on how to write test cases.

For those wanting to go further and add the new feature themselves open a bug report and we can chat about what needs doing.

Debugging

For those wanting to dig further into gfort2py.

Assuming that you loaded things as:

x = gf.fFort(SO, MOD)

You can find out the available Fortran variables/procedures module information with:

var = x._module['variable_name']

Variables

For variables you can create a fVar (the object that handles converting too and from Python to Fortran) with:

fvar = gf.fVar.fVar(var,x._module)

Note that at this point the fvar has no idea where to look for the variable. If you want to access its value in a module then

fvar.in_dll(x._lib)
print(fvar.value)

and you can then set the value (after calling in_dll) with:

fvar.value = value

Procedures

For a procedure you can do:

proc = x._module['procedure_name']

and its Fortran object is:

fproc = gf.fProc.fProc(x._lib,proc,x._module)

Calling the procedure is then:

fproc(*args,**kwargs)

To access the arguments of a procedure then:

args = [x._module[i.ref] for i in proc.args().symbol]

The return value of a function is accessed via:

return_arg = x._module[proc.return_arg()]

Derived types

To access a derived type (the type definition not an instance of the type)

dt_type = x._module['Derived_name']

Note the capitalization, derived types start with a capital.

Similar to a variable, to access an instance of a derived type variable its:

dt_var = x._module['dt_variable_name']
dt_fvar = gf.fVar.fVar(dt_var,x._module)