我想要设置一个工作流,以便在Windows计算机上使用Cython从Python访问fortran例程
经过一番搜索,我找到了:http://www.fortran90.org/src/best-practices.html#interfacing-with-c和https://stackoverflow.com/tags/fortran-iso-c-binding/info
和一些代码片段:
Fortran端:
pygfunc.h:
void c_gfunc(double x, int n, int m, double *a, double *b, double *c);
pygfunc.f90
module gfunc1_interface
use iso_c_binding
use gfunc_module
implicit none
contains
subroutine c_gfunc(x, n, m, a, b, c) bind(c)
real(C_FLOAT), intent(in), value :: x
integer(C_INT), intent(in), value :: n, m
type(C_PTR), intent(in), value :: a, b
type(C_PTR), value :: c
real(C_FLOAT), dimension(:), pointer :: fa, fb
real(C_FLOAT), dimension(:,:), pointer :: fc
call c_f_pointer(a, fa, (/ n /))
call c_f_pointer(b, fb, (/ m /))
call c_f_pointer(c, fc, (/ n, m /))
call gfunc(x, fa, fb, fc)
end subroutine
end module
gfunc.f90
module gfunc_module
use iso_c_binding
implicit none
contains
subroutine gfunc(x, a, b, c)
real, intent(in) :: x
real, dimension(:), intent(in) :: a, b
real, dimension(:,:), intent(out) :: c
integer :: i, j, n, m
n = size(a)
m = size(b)
do j=1,m
do i=1,n
c(i,j) = exp(-x * (a(i)**2 + b(j)**2))
end do
end do
end subroutine
end module
Cython端:
pygfunc.pyx
cimport numpy as cnp
import numpy as np
cdef extern from "./pygfunc.h":
void c_gfunc(double, int, int, double *, double *, double *)
cdef extern from "./pygfunc.h":
pass
def f(float x, a=-10.0, b=10.0, n=100):
cdef cnp.ndarray ax, c
ax = np.arange(a, b, (b-a)/float(n))
n = ax.shape[0]
c = np.ndarray((n,n), dtype=np.float64, order='F')
c_gfunc(x, n, n, <double *> ax.data, <double *> ax.data, <double *> c.data)
return c
和设置文件:
from distutils.core import setup
from distutils.extension import Extension
from Cython.Distutils import build_ext
import numpy as np
ext_modules = [Extension('pygfunc', ['pygfunc.pyx'])]
setup(
name = 'pygfunc',
include_dirs = [np.get_include()],
cmdclass = {'build_ext': build_ext},
ext_modules = ext_modules )
所有文件都在一个目录中
fortran文件编译(使用NAG Fortran Builder ) pygfunc编译
但将它们联系起来会抛出一个问题:
错误LNK2019:函数___pyx_pf_7pygfunc_f中引用的未解析外部符号_c_gfunc
当然还有:
致命错误LNK1120: 1未解析的外部变量
我错过了什么?或者,这种在Python和Fortran之间设置工作流的方式从一开始就遭到了谴责?
THX Martin
发布于 2014-03-20 13:18:50
这是一个最小的工作示例。我使用gfortran并将编译命令直接写入设置文件。
gfunc.f90
module gfunc_module
implicit none
contains
subroutine gfunc(x, n, m, a, b, c)
double precision, intent(in) :: x
integer, intent(in) :: n, m
double precision, dimension(n), intent(in) :: a
double precision, dimension(m), intent(in) :: b
double precision, dimension(n, m), intent(out) :: c
integer :: i, j
do j=1,m
do i=1,n
c(i,j) = exp(-x * (a(i)**2 + b(j)**2))
end do
end do
end subroutine
end module
pygfunc.f90
module gfunc1_interface
use iso_c_binding, only: c_double, c_int
use gfunc_module, only: gfunc
implicit none
contains
subroutine c_gfunc(x, n, m, a, b, c) bind(c)
real(c_double), intent(in) :: x
integer(c_int), intent(in) :: n, m
real(c_double), dimension(n), intent(in) :: a
real(c_double), dimension(m), intent(in) :: b
real(c_double), dimension(n, m), intent(out) :: c
call gfunc(x, n, m, a, b, c)
end subroutine
end module
pygfunc.h
extern void c_gfunc(double* x, int* n, int* m, double* a, double* b, double* c);
pygfunc.pyx
from numpy import linspace, empty
from numpy cimport ndarray as ar
cdef extern from "pygfunc.h":
void c_gfunc(double* a, int* n, int* m, double* a, double* b, double* c)
def f(double x, double a=-10.0, double b=10.0, int n=100):
cdef:
ar[double] ax = linspace(a, b, n)
ar[double,ndim=2] c = empty((n, n), order='F')
c_gfunc(&x, &n, &n, <double*> ax.data, <double*> ax.data, <double*> c.data)
return c
setup.py
from distutils.core import setup
from distutils.extension import Extension
from Cython.Distutils import build_ext
# This line only needed if building with NumPy in Cython file.
from numpy import get_include
from os import system
# compile the fortran modules without linking
fortran_mod_comp = 'gfortran gfunc.f90 -c -o gfunc.o -O3 -fPIC'
print fortran_mod_comp
system(fortran_mod_comp)
shared_obj_comp = 'gfortran pygfunc.f90 -c -o pygfunc.o -O3 -fPIC'
print shared_obj_comp
system(shared_obj_comp)
ext_modules = [Extension(# module name:
'pygfunc',
# source file:
['pygfunc.pyx'],
# other compile args for gcc
extra_compile_args=['-fPIC', '-O3'],
# other files to link to
extra_link_args=['gfunc.o', 'pygfunc.o'])]
setup(name = 'pygfunc',
cmdclass = {'build_ext': build_ext},
# Needed if building with NumPy.
# This includes the NumPy headers when compiling.
include_dirs = [get_include()],
ext_modules = ext_modules)
test.py
# A script to verify correctness
from pygfunc import f
print f(1., a=-1., b=1., n=4)
import numpy as np
a = np.linspace(-1, 1, 4)**2
A, B = np.meshgrid(a, a, copy=False)
print np.exp(-(A + B))
我所做的大多数改变都不是非常根本的。下面是一些重要的例子。
,
,
我还在安装文件中添加了一些内容,以显示在构建时可以在何处添加一些更有用的额外参数。
要进行编译,请运行python setup.py build_ext --inplace
。要验证它是否正常工作,请运行测试脚本。
下面是fortran90.org上显示的示例:mesh_exp
这是我一段时间前放在一起的两个例子:ftridiag,fssor我当然不是这方面的专家,但这些例子可能是一个很好的起点。
https://stackoverflow.com/questions/22404060
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