The most successful method I tried was using f2py. What f2py does is take in a FORTRAN module (see the tvd module below, for example) and makes a python interface for it. What this means is that you can import fortran code and run it at the the speeds you would expect from compiled code!
Let me first take you through the module. You can see from the 'include "omp_lib.h"' that I'm using openMP. For those that don't know, openMP is a great way of making any code parallel on shared memory machines (ie: if you have a computer with multiple cores or hyper-threading). The lines with "!$OMP", called pragmas, are in fact calls to openMP and effect to proceeding code. In addition, I created a function called "set_num_threads" to manually change the ammount of parallelization that is used by the program (by default, the number of threads is equal to the number of CPU's on the machine). A great thing about this "!$OMP" convention is that, since ! designates a comment, you can still compile and run this code without having openMP enabled!
Now, let's move on to f2py! You can see every subroutine (and function) has lines starting with "!f2py". Similar to openMP, this is used to designate various options. For example, the "!f2py threadsafe" tells python that the function will be using various threads and that the gil shouldn't be used. As a result, we cannot take in or return in python objects (there are methods to take in python callbacks when not using threadsafe). The intent option sets if a parameter is going to be inputed or outputted. With intent, python knows how to interpret python input and what should be given back as return values.
So, let's look at the function set. It takes in, in python, u and CFL. The other parameters are optional. In addition, you can see that "dt" and "maxv" are inputs but are calculated in the function. I did this little hack so that I can return multiple values to python. So in the end, this function is defined, in the python space, as "u, dt, maxv = step(u, cfl)". The most beautiful part is that when passing u to this module from python, u is a numpy array! Note that the other functions that are called (namely, do[XYZ] are also defined in the module, however for the sake of brevity they are not listed below).
In order to use this fortran module, we need to compile it with f2py. The end result is a .so that can be imported into fortran. I compiled this particular module with `f2py -c -m tvd --f90flags="-fopenmp -lm " --opt="-O3 --ffast-math" -lgomp --fcompiler=gnu95 tvd.f90` So, let's go through this. "-c" tells f2py to look at the pragmas in the code in order to learn how each function should be handled. "-m tvd" defined the name of the resulting module. the --f90flags, as expected, dictate the flags send to the FORTRAN compiler. I have chosen to use openmp (not needed, by the way, with newer versions of GCC where openMP is built-in) and libmath. opt sets the optimization flags. -lgomp tells f2py to use openMP (this redundancy seems necessary although I have a feeling that one of my statements telling f2py to use openMP is redundant). Finally, "fcompiler" tells f2py to use gfortran.
Let's look at a little session with this module, as to understand what f2py created. (I use ipython for its tab-completion)
In [1]: import numpy
In [2]: from tvd import tvd
In [3]: u = numpy.random.random((4,100,100,100)) #create a random velocity field
In [4]: u[0,:,:,:] = 1 #set density to 1 to avoid instabilities
In [5]: %timeit output = tvd.step(u, 0.65)
10 loops, best of 3: 704 ms per loop
Amazing! It takes 704ms to evolve a 100x100x100 isothermal grid! Normal usage of this would be: `u, dt, maxv = tvd.step(u, 0.65)`. One note, I write "from tvd import tvd" because the first "tvd" represents the file and the second "tvd" represents the FORTRAN module. If instead I had just written tvd.f90 as a series of subroutines and not as a FORTRAN module, I could have simply written "import tvd". Now, I can get to doing real science instead of being swamped by the inhuman syntax of FORTRAN (a language best spelled in all caps to truly illustrate its crimes).
And now, for the code... I'm leaving out the actual computational part however I may release that in the near future (comment if you'd like to see it!).
tvd.f90:
MODULE tvd
implicit none
include "omp_lib.h"
DOUBLE PRECISION :: csound=1
CONTAINS
SUBROUTINE set_num_threads(n)
!f2py threadsafe
!f2py intent(in) n
INTEGER :: n
CALL OMP_SET_NUM_THREADS(n)
END SUBROUTINE set_num_threads
SUBROUTINE step(u, n, CFL, dt, maxv)
!f2py threadsafe
!f2py intent(in) u
!f2py intent(in) n
!f2py intent(in) CFL
!f2py intent(in), optional dt
!f2py intent(in), optional maxv
!f2py intent(out) :: u, dt, maxv
!f2py depend(u) n
INTEGER :: n, k
DOUBLE PRECISION, DIMENSION(4,n,n,n) :: u
DOUBLE PRECISION :: CFL, dt, maxv
!$OMP PARALLEL DO shared(u) private(n,k) &
!$OMP SCHEDULE(dynamic) REDUCTION(MAX:maxv)
DO k=1,n
maxv = max( maxval( abs(u(2,:,:,k)) / u(1,:,:,k) ), &
maxval( abs(u(3,:,:,k)) / u(1,:,:,k) ), &
maxval( abs(u(4,:,:,k)) / u(1,:,:,k) ) &
)
END DO
!$OMP END PARALLEL DO
!Calculate the timestep based on the courant condition
if (dt .eq. 0.0) dt = CFL / (csound+maxv)
!perform strang splitting using the hydro only 2nd order
!TVD algorithm given by http://arxiv.org/abs/astro-ph/0305088
CALL doX(u,n,dt, maxv)
CALL doY(u,n,dt, maxv)
CALL doZ(u,n,dt, maxv)
CALL doZ(u,n,dt, maxv)
CALL doY(u,n,dt, maxv)
CALL doX(u,n,dt, maxv)
END subroutine step
END MODULE
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