I'm currently doing a Cython wrapper for an existing C++ library. I have an overloaded non-member operator in C++ like
Data operator+(Data const& a, Data const& b)
And in the pxd file describing the header, I wrote
cdef extern from 'blabla.h':
Data operator+(const Data&, const Data&)
Now how can I use this operator+ in another pyx file?
For very simple cases, like in your example you can lie to Cython and tell it that the operator is a member function:
cdef extern from 'blabla.h':
cdef cppclass Data:
# the rest of the data definitions
Data operator+(const Data&)
It only uses this information to know that it can translate the code a+b (where a and b are data objects) to __pyx_v_a + __pyx_v_b and let the c++ compiler do the rest (which it knows how to because of the import from "blabla.h"). Therefore, the distinction between member and non-member is irrelevant.
However: one of the main reasons to use nonmember operators is to allow things like
Data operator+(int a, const Data& b);
You can make this work, but it's very slightly messy. In your pxd file do
cdef extern from 'blabla.h':
Data operator+(int, const Data&) # i.e. do nothing special
In your pyx file
from my_pxd_file import * # works fine
## but this isn't accepted unfortunately:
from my_pxd_file import operator+
If you wanted to avoid too much namespace pollution from doing import *, you could probably create a pxd file that only contains operators and not the class definitions (I haven't tested this though)
In conclusion - two methods depending on how complicated your use-case is...
Related
I have a C++ method declared as follow:
std::tuple<std::vector<int>, std::size_t, std::size_t> get_state(); // Should I use a struct and expose the struct instead?
And I would like to declare a cython extension to interface it in Python
cdef extern from "cpp_sources/CClass.hpp":
cdef cppclass CClass nogil:
tuple[vector[int], size_t, size_t] get_state()
Unfortunately, I don't see an easy import to make to have access to a C++ tuple.
I also checked here with no success.
My question is, is there an easy way to have access to a c++ tuple? Or maybe there is a better way to have access to some elements?
(We don't care about performances for this exact method)
Unfortunately this is not supported. More generally variadic templates are not supported - that's why you have pair for example, but not a generic tuple.
In the github issue I linked they have their own version of a workaround, which is what I would come up with first - for every amount of N arguments I will actually use,
template<typename T_1, ... typename T_N>
using tupleN = std::tuple<T_1, ..., TN>;
and exporting each tupleN individually. There is no magic I'm aware of to make a general variadic template here.
Most of my library is written with Cython in the "normal" C mode. Up to now I rarely needed any C++ functionality, but always assumed (and sometimes did!) I could just switch to C++-mode for one module if I wanted to.
So I have like 10+ modules in C-mode and 1 module in C++-mode.
The problem is now how Cython seems to handle complex numbers definitions. In C-mode it assumes I think C complex numbers, and in C++-mode it assumes I think C++ complex numbers. I've read they might be even the same by now, but in any case Cython complains that they are not:
openChargeState/utility/cheb.cpp:2895:35: error: cannot convert ‘__pyx_t_double_complex {aka std::complex<double>}’ to ‘__complex__ double’ for argument ‘1’ to ‘double cabs(__complex__ double)’
__pyx_t_5 = ((cabs(__pyx_v_num) == INFINITY) != 0);
In that case I'm trying to use cabs defined in a C-mode module, and calling it from the C++-mode module.
I know there are some obvious workarounds (right now I'm just not using C++-mode; I'd like to use vectors and instead use the slower Python lists for now).
Is there maybe a way to tell my C++-mode module to use C complex numbers, or tell it that they are the same? If there is I couldn't find a working way to ctypedef C complex numbers in my C++-mode module... Or are there any other solutions?
EDIT: Comments of DavidW and ead suggested some reasonable things. First the minimum working example.
setup.py
from distutils.core import setup
from distutils.extension import Extension
from Cython.Distutils import build_ext
from Cython.Build import cythonize
extra_compile_args=['-O3']
compdir = {'language_level' : '3'}
extensions = cythonize([
Extension("cmod", ["cmod.pyx"]),
Extension("cppmod", ["cppmod.pyx"], language='c++')
],
compiler_directives = compdir
)
setup(cmdclass = {'build_ext': build_ext},
ext_modules = extensions
)
import cppmod
cmod.pyx
cdef double complex c_complex_fun(double complex xx):
return xx**2
cmod.pxd
cdef double complex c_complex_fun(double complex xx)
cdef extern from "complex.h":
double cabs(double complex zz) nogil
cppmod.pyx
cimport cmod
cdef double complex cpp_complex_fun(double complex xx):
return cmod.c_complex_fun(xx)*abs(xx) # cmod.cabs(xx) doesn't work here
print(cpp_complex_fun(5.5))
Then just compile with python3 setup.py build_ext --inplace.
Now the interesting part is that (as written in the code) only "indirectly" imported c functions have a problem, in my case cabs. So the suggestion to just use abs actually does help, but I still don't understand the underlying logic. I hope I don't encounter this in another problem. I'm leaving the question up for now. Maybe somebody knows what's happening.
Your problem has nothing to do with the fact, that one module is compiled as a C-extension and the other as a C++-extension - one can easily reproduce the issue in a C++-extension alone:
%%cython -+
cdef extern from "complex.h":
double cabs(double complex zz) nogil
def cpp_complex_fun(double complex xx):
return cabs(xx)
results in your error-message:
error: cannot convert __pyx_t_double_complex {aka
std::complex<double>} to __complex__ double for argument 1 to
double cabs(__complex__ double)
The problem is that the complex numbers are ... well, complex. Cython's strategy (can be looked up here and here) to handle complex numbers is to use an available implementation from C/CPP and if none is found a hand-written fallback is used:
#if !defined(CYTHON_CCOMPLEX)
#if defined(__cplusplus)
#define CYTHON_CCOMPLEX 1
#elif defined(_Complex_I)
#define CYTHON_CCOMPLEX 1
#else
#define CYTHON_CCOMPLEX 0
#endif
#endif
....
#if CYTHON_CCOMPLEX
#ifdef __cplusplus
typedef ::std::complex< double > __pyx_t_double_complex;
#else
typedef double _Complex __pyx_t_double_complex;
#endif
#else
typedef struct { double real, imag; } __pyx_t_double_complex;
#endif
In case of a C++-extension, Cython's double complex is translated to std::complex<double> and thus cannot be called with cabs( double complex z ) because std::complex<double> isn't double complex.
So actually, it is your "fault": you lied to Cython and told him, that cabs has the signature double cabs(std::complex<double> z), but it was not enough to fool the c++-compiler.
That means, in c++-module std::abs(std::complex<double>) could be used, or just Cython's/Python's abs, which is automatically translated to the right function (this is however not possible for all standard-function).
In case of the C-extension, because you have included complex.h as an so called "early include" with cdef extern from "complex.h", thus for the above defines _Complex_I becomes defined and Cython's complex becomes an alias for double complex and thus cabs can be used.
Probably the right thing for Cython would be to always use the fallback per default and that the user should be able to choose the desired implementation (double complex or std::complex<double>) explicitly.
I don't know python and trying to wrap an existing C library that provides 200 init functions for some objects and 200 destructors with help of PyCapsule. So my idea is to return a PyCapsule from init functions` wrappers and forget about destructors that shall be called automatically.
According to documentation PyCapsule_New() accepts:
typedef void (*PyCapsule_Destructor)(PyObject *);
while C-library has destructors in a form of:
int foo(void*);
I'm trying to generate a C function in .pyx file with help of cdef that would generate a C-function that will wrap library destructor, hide its return type and pass a pointer taken with PyCapsule_GetPointer to destructor. (pyx file is programmatically generated for 200 functions).
After a few experiments I end up with following .pyx file:
from cpython.ref cimport PyObject
from cpython.pycapsule cimport PyCapsule_New, PyCapsule_IsValid, PyCapsule_GetPointer
cdef void stateFree( PyObject *capsule ):
cdef:
void * _state
# some code with PyCapsule_GetPointer
def stateInit():
cdef:
void * _state
return PyCapsule_New(_state, "T", stateFree)
And when I'm trying to compile it with cython I'm getting:
Cannot assign type 'void (PyObject *)' to 'PyCapsule_Destructor'
using PyCapsule_New(_state, "T", &stateFree) doesn't help.
Any idea what is wrong?
UPD:
Ok, I think I found a solution. At least it compiles. Will see if it works. I'll bold the places I think I made a mistake:
from cpython.ref cimport PyObject
from cpython.pycapsule cimport PyCapsule_New, PyCapsule_IsValid, PyCapsule_GetPointer, PyCapsule_Destructor
cpdef void stateFree( object capsule ):
cdef:
void* _state
_state = PyCapsule_GetPointer(capsule, "T")
print('destroyed')
def stateInit():
cdef:
int _state = 1
print ("initialized")
return PyCapsule_New(_state, "T", < PyCapsule_Destructor >stateFree)
The issue is that Cython distinguishes between
object - a Python object that it knows about and handles the reference-counting for, and
PyObject*, which as far as it's concerned is a mystery type that it basically nothing about except that it's a pointer to a struct.
This is despite the fact that the C code generated for Cython's object ends up written in terms of PyObject*.
The signature used by the Cython cimport is ctypedef void (*PyCapsule_Destructor)(object o) (which isn't quite the same as the C definition. Therefore, define the destructor as
cdef void stateFree( object capsule ):
Practically in this case the distinction makes no difference. It matters more in cases where a function steals a reference or returns a borrowed reference. Here capsule has the same reference count on both the input and output of the function whether Cython manages it or not.
In terms of your edited-in solution:
cpdef is wrong for stateFree. Use cdef since it is not a function that should be exposed in a Python interface (and if you use cpdef it isn't obvious whether the Python or C version is passed as a function pointer).
You shouldn't need the cast to PyCapsule_Destructor and should avoid it because casts can easily hide bugs.
Can I just take a moment to express my general dislike for PyCapsule (it's occasionally useful for passing an opaque type through Python code without touching it, but for anything more I think it's usually better to wrap it properly in a cdef class). It's possible you've thought about it and it is the right tool for the job, but I'm putting this warning in to try to discourage people in the future who might be trying to use it on a more "copy-and-paste" basis.
I have an array with bytes and its size:
cdef char *bp
cdef size_t size
How do I read the array into a Python bytearray (or another appropriate structure that can easily be pickled)?
Three reasonably straightforward ways to do it:
Use the appropriate C API function as I suggested in the comments:
from cpython.bytes cimport PyBytes_FromStringAndSize
output = PyBytes_FromStringAndSize(bp,size)
This makes a copy, which may be an issue with a sufficiently large string. For Python 2 the functions are similarly named but with PyString rather than PyBytes.
View the char pointer with a typed memoryview, get a numpy array from that:
cdef char[::1] mview = <char[:size:1]>(bp)
output = np.asarray(mview)
This shouldn't make a copy, so could be more efficient if large.
Do the copy manually:
output = bytearray(size)
for i in range(size):
output[i] = bp[i]
(this could be somewhat accelerated with Cython if needed)
This issue I think you're having with ctypes (based on the subsequent question you linked to in the comments) is that you cannot pass C pointer to the ctypes Python interface. If you try to pass a char* to a Python function Cython will try to convert it to a string. This fails because it stops at the first 0 element (hence you need size). Therefore you aren't passing ctypes a char*, you're passing it a nonsense Python string.
I have some C source code and want to wrap it in Cython. Now, the problem is, that there is a structure called print, and externing it throws a syntax error.
cdef extern from "foo.h":
struct print:
# ...
Same problem would appear when an attribute or a function or alike is called like a keyword.
cdef extern from "foo.h":
struct foo:
bint print
print(char*, int)
Is there a way to work around this, without modifieng the source? Maybe some technique that replaces a proxy-name with the real-name in the source-file ?
I think the solution you are looking for is something along the lines of:
cdef extern from "foo.h":
struct print "MY_print":
double var "MY_var"
print.var will then be defined by:
MY_print.MY_var
This way you can rename structs, functions, unions and enums from the header file. The names are converted when Cython compiles your code into C code.
The relevant part of Cython documentation can be found here.