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# Notice that we are passing a pointer to the first element in each arrayĬcomplexFunLib.mp_mlt_exp_c4(&k, &ee, sz, &res, threads)ĭef mp_exp_c8(np.ndarray k,ĬcomplexFunLib.mp_mlt_exp_c8(&k, &ee, sz, &res, threads) # Call the imported DLL functions on the parameters. # Note that types can be delcared in the signatureĭef mp_exp_c4(np.ndarray k, # Import some functionality from Python and the C stdlib Import numpy as np # Import the Python Numpy # Note for Numpy imports, the C import most come AFTER the Python import cimport ccomplexFunLib # Import the pxd "header" It is also possible to use the built in Cython memoryview object for arrays, but its performance may not be as good as ndarray objects (however the syntax is significantly cleaner). In this example, we will be passing pointers to Numpy ndarray objects to the import DLL functions. This file corresponds to the C++ cpp source file. Void mp_mlt_exp_c8(double complex* k, double complex* ee, int sz, Void mp_mlt_exp_c4(float complex* k, float complex* ee, int sz, This is not necessary, but we have found that such a naming convention helps maintain organization. Note the addition of c at the beginning of ccomplexFunLib.pxd. In this case, the specific Cython complex types were used. In most cases, you may copy-paste the header over to this file with minor Cython specific changes. This file corresponds the C++ header file. pyd file which may be imported as a Python module and exposes the functions written in C++. The ultimate goal is to create use these Cython source files in conjunction with the original DLL to compile a.
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In this step, we make the following assumptions: Next, we create the Cython source files necessary to wrap the C++ DLL. #pragma omp parallel num_threads(threads)įor (int i = 0 i < sz i++) res = k * exp(ee) Ĭython Source: ccomplexFunLib.pxd and complexFunLib.pyx Use Open MP parallel directive for multiprocessing
#Cygwin tutorial python 64 Bit#
two 64 bit (8 byte) floating point numbersĮXTERN_DLL_EXPORT void mp_mlt_exp_c8(std::complex* k, std::complex* ee,
#Cygwin tutorial python 32 bit#
two 32 bit (4 byte) floating point numbersĮXTERN_DLL_EXPORT void mp_mlt_exp_c4(std::complex* k, #define EXTERN_DLL_EXPORT extern "C" _declspec(dllexport) H File // Avoids C++ name mangling with extern "C" Note that these example functions use OpenMP, so make sure that OpenMP is enabled in the Visual Studio options for the project. There are two functions to accommodate both single and double precision. The following two functions perform the calculation k*exp(ee) on arrays k and ee, where the results are stored in res. In this case, we want to do fast array calculations with the complex exponential function. First, we create the C++ source from which the DLL will be compiled using Visual Studio. Skip this if you already have a DLL and header source file. run.py: Example Python file that imports the compiled, Cython wrapped DLLĬ++ DLL Source: complexFunLib.h and complexFunLib.cpp.setup.py: Python setup file for creating complexFunLib.pyd with Cython.complexFunLib.pyx: Cython "wrapper" file.ccomplexFunLib.pxd: Cython "header" file.complexFunLib.cpp: CPP file for the C++ DLL source.complexFunLib.h: Header file for the C++ DLL source.
#Cygwin tutorial python full#
The full example includes the creation of the following files: It is assumed that you have Cython installed and can successfully import it in Python.įor the DLL step, it is also assumed that you are familiar with creating a DLL in Visual Studio.