cython

问题 I am trying to use cython debugger to put in a break point: Here is my code: cython_file.pyx cimport cython def big_sum(): cdef int a[10000] for i in range(10000): a[i] = i # <==================== I want to put a break here cdef int my_sum my_sum = 0 for i in range(1000): my_sum += a[i] return my_sum python_file.py from cython_file import big_sum result = big_sum() print result setup.py from distutils.core import setup from distutils.extension import Extension from Cython.Distutils import

问题 I am trying to compile a simple cython extension from the example page here on my Windows 7 64-bit machine with Python 2.6 64-bit version installed. I installed Cython 0.15.1 for Windows 64-bit version from Gohlke's page. Basically, the answer from here and here are not my options because I really do need Python 64-bit version to address larger memory. Also, because I am trying to compile using Microsoft SDK for .NET 4, I cannot use the approach in the latter solution. I tried the steps here

问题 I am wrapping some c++ code using cython, and I am not sure what is the best best way to deal with parameters with default values. In my c++ code I have function for which the parameters have default values. I would like to wrap these in such a way that these default values get used if the parameters are not given. Is there a way to do this? At this point the only way that I can see to provide option parameters is to define them as part of the python code (in the def func satement in pycode

问题 I have a numpy array which came from a cv2.imread and so has dtype = np.uint8 and ndim = 3 . I want to convert it to a Cython unsigned int* for use with an external cpp library. I am trying cdef unsigned int* buff = <unsigned int*>im.data however I get the error Python objects cannot be cast to pointers of primitive types What am I doing wrong? Thanks 回答1: thanks for your comments. solved by: cdef np.ndarray[np.uint32_t, ndim=3, mode = 'c'] np_buff = np.ascontiguousarray(im, dtype = np.uint32

问题 I use numpexpr for fast math on large arrays but if the size of the array is less than the CPU cache, writing my code in Cython using simple array math is way faster, especially, if the function is called multiple times. The issue is, how do you work with arrays in Cython, or more explicitly: is there a direct interface to Python's array.array type in Cython? What I would like to do is something like this (simple example) cpdef array[double] running_sum(array[double] arr): cdef int i cdef int

问题 In terms of performance (algebraic operations, lookup, caching, etc.), is there a difference between C arrays (which can be exposed as a C array, or a cython.view.array [Cython array], or a memoryview of the aforementioned two) and a NumPy arrays (which in Cython should have no Python overhead) Edit: I should mention that in the NumPy array is statically typed using Cython, and the dtype s are NumPy compile-time datypes (e.g. cdef np.int_t or cdef np.float32_t ), and the types in the C case

问题 In terms of performance (algebraic operations, lookup, caching, etc.), is there a difference between C arrays (which can be exposed as a C array, or a cython.view.array [Cython array], or a memoryview of the aforementioned two) and a NumPy arrays (which in Cython should have no Python overhead) Edit: I should mention that in the NumPy array is statically typed using Cython, and the dtype s are NumPy compile-time datypes (e.g. cdef np.int_t or cdef np.float32_t ), and the types in the C case

问题 After compiling the following Cython code, I get the html file that looks like this: import numpy as np cimport numpy as np cpdef my_function(np.ndarray[np.double_t, ndim = 1] array_a, np.ndarray[np.double_t, ndim = 1] array_b, int n_rows, int n_columns): array_a[0:-1:n_columns] = 0 array_a[n_columns - 1:n_rows * n_columns:n_columns] = 0 array_a[0:n_columns] = 0 array_a[n_columns* (n_rows - 1):n_rows * n_columns] = 0 array_b[array_a == 3] = 0 return array_a, array_b My question is that why

问题 I am wrapping some C library, and I have one function which in some cases may result with segmentation fault. In that case I need to call second function, which will in that situation complete successfully. Does anyone knows how can I handle segmentation fault in cython? 回答1: A short example that might help (using signal ): example.h (assuming the Cython extension is named myext.pyx ) // Header autogenerated by Cython when declaring "public api" functions #include "../myext_api.h" void

问题 I am exporting C++ API for python code using Cython. The application will be executed on Ubuntu. The project files are present here The function I am wrapping, reads the file name of the image and displays the image. The Show_Img.pyx file looks as follows import cv2 cdef public void Print_image(char* name): img = cv2.imread(name) cv2.imshow("Image", img) while(True): if cv2.waitKey(1) & 0xFF == ord('q'): break The c++ interface generated from Cython looks as follows __PYX_EXTERN_C DL_IMPORT