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How to make external function work with f2py?

I am trying to compile a piece of old Fortran code with f2py so that it can be called within Python.
However, the part with external function wouldn’t work.
Here is an MWE, first I have the test.f:
function f(x)
implicit double precision (a-z)
f = x * x
return
end function f
subroutine gauss(fun)
implicit double precision (a-h, j-z)
! external fun
x = 1.5
write(*,*) fun(x)
return
end subroutine gauss
which is later compiled with makefile:
f2py -c --quiet --fcompiler=gnu95 \
--f90flags=“-Wtabs” \
-m test \
test.f
Lastly it is called from Python:
import test
f = lambda x: x
test.gauss(test.f)
and gives the error TypeError: test.gauss() 1st argument (fun) can’t be converted to double.
In a second attempt, I uncomment the line external fun in the subroutine gauss and get the following error message during compilation:
/tmp/tmpet9sk3e9/src.linux-x86_64-3.7/testmodule.c: In function ‘cb_fun_in_gauss__user__routines’:
/tmp/tmpet9sk3e9/src.linux-x86_64-3.7/testmodule.c:313:8: error: variable or field ‘return_value’ declared void
I am running out of ideas, any help will be greatly appreciated!

I have (partly) figured it out: f2py needs you to explicitly specify the use of the function.
Here is how:
function f(x)
implicit double precision (a-z)
f = x * x
return
end function f
subroutine gauss(fun)
implicit double precision (a-h, j-z)
!! <-------- updates starts HERE
external fun
!f2py real*8 y
!f2py y = fun(y)
double precision fun
!! <-------- updates ends HERE
x = 1.5
write(*,*) fun(x)
return
end subroutine gauss
then compile it using
main:
f2py -c --quiet --fcompiler=gnu95 \
--f90flags=“-Wtabs” \
-m test \
test.f
You can test with
import test
f = lambda x: x
test.gauss(test.f)
test.gauss(f)
and see that the external function works for both Fortran and Python functions.
Two side notes:
Although the documentation says !f2py intent(callback) fun is necessary, I find the code can work without it.
You can specify multiple external functions using the same syntax, you can even use the same dummy input variable y for all of them (maybe not a good practice though).

External function ‘f’ at (1) has no IMPLICIT type in subroutine with f2py

I've used Fortran 90 for a while now and recently decided to wrap up some modules using f2py to simplifying prototyping by using Python as a front end.
However, I've come across an error when trying to compile a subroutine that is passed an external function (from Python). Here is code that replicates the error:
! file: callback.f90
subroutine callback(f,x,y)
implicit none
! - args -
! in
external :: f
real(kind=kind(0.0d0)), intent(in) :: x
! out
real(kind=kind(0.0d0)), intent(inout) :: y
y = f(x)
end subroutine
Now, if I use f2py to generate a signature file (.pyf), here is what I obtain:
! -*- f90 -*-
! Note: the context of this file is case sensitive.
python module callback__user__routines
interface callback_user_interface
function f(x) result (y) ! in :callback:callback.f90
intent(inout) f
real(kind=kind(0.0d0)) intent(in) :: x
real(kind=kind(0.0d0)) intent(inout) :: y
end function f
end interface callback_user_interface
end python module callback__user__routines
python module callback ! in
interface ! in :callback
subroutine callback(f,x,y) ! in :callback:callback.f90
use callback__user__routines
external f
real(kind=kind(0.0d0)) intent(in) :: x
real(kind=kind(0.0d0)) intent(inout) :: y
end subroutine callback
end interface
end python module callback
! This file was auto-generated with f2py (version:2).
! See http://cens.ioc.ee/projects/f2py2e/
Which is not correct, so I alter it in the following way:
! -*- f90 -*-
! Note: the context of this file is case sensitive.
python module __user__routines
interface
function f(x) result (y)
real(kind=kind(0.0d0)) intent(in) :: x
real(kind=kind(0.0d0)) :: y
end function f
end interface
end python module __user__routines
python module callback ! in
interface ! in :callback
subroutine callback(f,x,y)
use __user__routines
external f
real(kind=kind(0.0d0)) intent(in) :: x
real(kind=kind(0.0d0)) intent(inout) :: y
end subroutine callback
end interface
end python module callback
! This file was auto-generated with f2py (version:2).
! See http://cens.ioc.ee/projects/f2py2e/
Nonetheless, both throw an error telling me that I have not defined f:
callback.f90:1:21:
subroutine callback(f,x,y)
1
Error: Symbol ‘f’ at (1) has no IMPLICIT type
callback.f90:10:6:
y = f(x)
1
Error: Can't convert UNKNOWN to REAL(8) at (1)
callback.f90:1:21:
subroutine callback(f,x,y)
1
Error: Symbol ‘f’ at (1) has no IMPLICIT type
callback.f90:10:6:
y = f(x)
1
Error: Can't convert UNKNOWN to REAL(8) at (1)
I've done as much research as I can on properly editing the .pyf signature file manually, but to no avail in this case. Has anyone encountered a similar error when trying to use f2py with external functions?

The error is correct and it is a Fortran error, it will happen even without any f2py or Python
subroutine callback(f,x,y)
implicit none
external :: f
real(kind=kind(0.0d0)), intent(in) :: x
real(kind=kind(0.0d0)), intent(inout) :: y
y = f(x)
end subroutine
f has no type declared and implicit none is enabled so that is a Fortran error.
Use
real(kind=kind(0.0d0)), external :: f
or
external :: f
real(kind=kind(0.0d0)) :: f

How to write a genfromtxt with f2py?

I've found that the function genfromtxt from numpy in python is very slow.
Therefore I decided to wrap a module with f2py to read my data. The data is a matrix.
subroutine genfromtxt(filename, nx, ny, a)
implicit none
character(100):: filename
real, dimension(ny,nx) :: a
integer :: row, col, ny, nx
!f2py character(100), intent(in) ::filename
!f2py integer, intent(in) :: nx
!f2py integer, intent(in) :: ny
!f2py real, intent(out), dimension(nx,ny) :: a
!Opening file
open(5, file=filename)
!read data again
do row = 1, ny
read(5,*) (a(row,col), col =1,nx) !reading line by line
end do
close (5)
end subroutine genfromtxt
The length of the filename is fixed to 100 because if f2py can't deal with dynamic sizes. The code works for sizes shorter than 100, otherwise the code in python crashes.
This is called in python as:
import Fmodules as modules
w_map=modules.genfromtxt(filename,100, 50)
How can I do this dynamically without passing nx, ny as parameters nor fixing the filename length to 100?

The filename length issue is easily dealt with: you make filename: character(len_filename):: filename, have len_filename as a parameter of the fortran function, and use the f2py intent(hide) to hide it from your Python interface (see the code below).
Your issue of not wanting to have to pass in nx and ny is more complicated, and is essentially is the problem of how to return an allocatable array from f2py. I can see two methods, both of which require a (small and very light) Python wrapper to give you a nice interface.
I've haven't actually addressed the problem of how to read the csv file - I've just generated and returned some dummy data. I'm assuming you have a good implementation for that.
Method 1: module level allocatable arrays
This seems to be a fairly standard method - I found at least one newsgroup post recommending this. You essentially have an allocatable global variable, initialise it to the right size, and write to it.
module mod
real, allocatable, dimension(:,:) :: genfromtxt_output
contains
subroutine genfromtxt_v1(filename, len_filename)
implicit none
character(len_filename), intent(in):: filename
integer, intent(in) :: len_filename
!f2py intent(hide) :: len_filename
integer :: row, col
! for the sake of a quick demo, assume 5*6
! and make it all 2
if (allocated(genfromtxt_output)) deallocate(genfromtxt_output)
allocate(genfromtxt_output(1:5,1:6))
do row = 1,5
do col = 1,6
genfromtxt_output(row,col) = 2
end do
end do
end subroutine genfromtxt_v1
end module mod
The short Python wrapper then looks like this:
import _genfromtxt
def genfromtxt_v1(filename):
_genfromtxt.mod.genfromtxt_v1(filename)
return _genfromtxt.mod.genfromtxt_output.copy()
# copy is needed, otherwise subsequent calls overwrite the data
The main issue with this is that it won't be thread safe: if two Python threads call genfromtxt_v1 at very similar times the data could get overwritten before you've had a change to copy it out.
Method 2: Python callback function that saves the data
This is slightly more involved and a horrendous hack of my own invention. You pass your array to a callback function which then saves it. The Fortran code looks like:
subroutine genfromtxt_v2(filename,len_filename,callable)
implicit none
character(len_filename), intent(in):: filename
integer, intent(in) :: len_filename
!f2py intent(hide) :: len_filename
external callable
real, allocatable, dimension(:,:) :: result
integer :: row, col
integer :: rows,cols
! for the sake of a quick demo, assume 5*6
! and make it all 2
rows = 5
cols = 6
allocate(result(1:rows,1:cols))
do row = 1,rows
do col = 1,cols
result(row,col) = 2
end do
end do
call callable(result,rows,cols)
deallocate(result)
end subroutine genfromtxt_v2
You then need to generate the "signature file" with f2py -m _genfromtxt -h _genfromtxt.pyf genfromtxt.f90 (assuming genfromtxt.f90 is the file with your Fortran code). You then modify the "user routines block" to clarify the signature of your callback:
python module genfromtxt_v2__user__routines
interface genfromtxt_v2_user_interface
subroutine callable(result,rows,cols)
real, dimension(rows,cols) :: result
integer :: rows
integer :: cols
end subroutine callable
end interface genfromtxt_v2_user_interface
end python module genfromtxt_v2__user__routines
(i.e. you specify the dimensions). The rest of the file is left unchanged.
Compile with f2py -c genfromtxt.f90 _genfromtxt.pyf
The small Python wrapper is
import _genfromtxt
def genfromtxt_v2(filename):
class SaveArrayCallable(object):
def __call__(self,array):
self.array = array.copy() # needed to avoid data corruption
f = SaveArrayCallable()
_genfromtxt.genfromtxt_v2(filename,f)
return f.array
I think this should be thread safe: although I think Python callback functions are implemented as global variables, but default f2py doesn't release the GIL so no Python code can be run between the global being set and the callback being run. I doubt you care about thread safety for this application though...

I think you can just use:
open(5, file=trim(filename))
to deal with filenames shorter than the length of filename. (i.e. you could make filename much longer than it needs to be and just trim it here)
I don't know of any nice clean way to remove the need to pass nx and ny to the fortran subroutine. Perhaps if you can determine the size and shape of the data file programatically (e.g. read the first line to find nx, call some function or have a first pass over the file to determine the number of lines in the file), then you could allocate your a array after finding those values. That would slow everything down though, so may be counter productive

Segmentation fault after removing debug printing

I have a (for me) very weird segmentation error. At first, I thought it was interference between my 4 cores due to openmp, but removing openmp from the equation is not what I want. It turns out that when I do, the segfault still occurs.
What's weird is that if I add a print or write anywhere within the inner-do, it works.
subroutine histogrambins(rMatrix, N, L, dr, maxBins, bins)
implicit none;
double precision, dimension(N,3), intent(in):: rMatrix;
integer, intent(in) :: maxBins, N;
double precision, intent(in) :: L, dr;
integer, dimension(maxBins, 1), intent(out) :: bins;
integer :: i, j, b;
double precision, dimension(N,3) :: cacheParticle, cacheOther;
double precision :: r;
do b= 1, maxBins
bins(b,1) = 0;
end do
!$omp parallel do &
!$omp default(none) &
!$omp firstprivate(N, L, dr, rMatrix, maxBins) &
!$omp private(cacheParticle, cacheOther, r, b) &
!$omp shared(bins)
do i = 1,N
do j = 1,N
!Check the pair distance between this one (i) and its (j) closest image
if (i /= j) then
!should be faster, because it doesn't have to look for matrix indices
cacheParticle(1, :) = rMatrix(i,:);
cacheOther(1, :) = rMatrix(j, :);
call inbox(cacheParticle, L);
call inbox(cacheOther, L);
call closestImage(cacheParticle, cacheOther, L);
r = sum( (cacheParticle - cacheOther) * (cacheParticle - cacheOther) ) ** .5;
if (r /= r) then
! r is NaN
bins(maxBins,1) = bins(maxBins,1) + 1;
else
b = floor(r/dr);
if (b > maxBins) then
b = maxBins;
end if
bins(b,1) = bins(b,1) + 1;
end if
end if
end do
end do
!$omp end parallel do
end subroutine histogramBins
I enabled -debug-capi in the f2py command:
f2py --fcompiler=gfortran --f90flags="-fopenmp -fcheck=all" -lgomp --debug-capi --debug -m -c modulename module.f90;
Which gives me this:
debug-capi:Fortran subroutine histogrambins(rmatrix,&n,&l,&dr,&maxbins,bins)'
At line 320 of file mol-dy.f90
Fortran runtime error: Aborted
It also does a load of other checking, listing arguments given and other subroutines called and so on.
Anyway, the two subroutines called in are both non-parallel subroutines. I use them in several other subroutines and I thought it best not to call a parallel subroutine with the parallel code of another subroutine. So, at the time of processing this function, no other function should be active.
What's going on here? How can adding "print *, ;"" cause a segfault to go away?
Thank you for your time.

It's not unusual for print statements to impact - and either create or remove the segfault. The reason is that they change the way memory is laid out to make room for the string being printed, or you will be making room for temporary strings if you're doing some formatting. That change can be sufficient to cause a bug to either appear as a crash for the first time, or to disappear.
I see you're calling this from Python. If you're using Linux - you could try following a guide to using a debugger with Fortran called from Python and find the line and the data values that cause the crash. This method also works for OpenMP. You can also try using GDB as the debugger.
Without the source code to your problem, I don't think you're likely to get an "answer" to the question - but hopefully the above ideas will help you to solve this yourself.
Using a debugger is (in my experience) considerably less likely to have this now-you-see-it-now-you-don't behaviour than with print statements (almost certainly so if only using one thread).

Understanding Cython "typedness" report

I'm using Cython to make my
Python code more efficient. I have read about the Cython's function cython -a filename.pyx to see the "typedness" of my cython code. Here is the short reference from Cython web page. My environment is Windows 7, Eclipse PyDev, Python 2.7.5 32-bit, Cython 0.20.1 32-bit, MinGW 32-bit.
Here is the report for my code:
So does the color yellow mean efficient or non-efficient code? The more yellow it is the more....what?
Another question, I can click on the numbered rows and the following report opens (e.g. row 23):
What does this mean? P.S. If you can't see the image well enough --> right click --> view image (on Windows 7) ;)
Thnx for any assistance =)
UPDATE:
In case somebody wants to try my toy code here they are:
hello.pyx
import time
cdef char say_hello_to(char name):
print("Hello %s!" % name)
cdef double f(double x) except? -2:
return x**2-x
cdef double integrate_f(double a, double b, int N) except? -2:
cdef int i
cdef double s, dx
s = 0
dx = (b-a)/N
for i in range(N):
s += f(a+i*dx)
return s * dx
cpdef p():
s = 0
for i in range(0, 1000000):
c = time.time()
integrate_f(0,100,5)
s += time.time()- c
print s
test_cython.py
import hello as hel
hel.p()
setup.py
from distutils.core import setup
from Cython.Build import cythonize
setup(
name = 'Hello world app',
ext_modules = cythonize("hello.pyx"),
)
From command line prompt I used the command (to generate C, pyd etc files):
python setup.py install build --compiler=mingw32
To generate the report I used:
cython -a hello.pyx

This is more or less what you wrote. To be more precise:
Yellowish line signal Cython command which are not directly translated to pure C code but which work by calling CPython API to do the job. Those line includes:
Python object allocation
calls to Python function and builtins
operating on Python high level data stuctures (eg: list, tuples, dictionary)
use of overloaded operation on Python types (eg: +, * in Python integers vs C int)
In any case, this is a good indication that thing might be improved.

I think I perhaps got it myself already. Someone can correct me if I'm mistaken:
The more "yellowish" the line is, then less efficient it is
The most efficient lines are the white-colored lines, because these are translated into pure C-code.