A memoryview alone does not actually own any memory. The only effect # this has is to a) insert checks that the function arguments really are # NumPy arrays, and b) make some attribute access like f.shape[0] much # more efficient The type of the # arguments for a "def" function is checked at run-time when entering the # function. Compile time definitions for NumPy Maybe it's a matter of cython version? By declaring the type and dimensions of an array before actually creating it, Cython can access the NumPy array more efficiently: import numpy as np # Normal NumPy import cimport numpy as cnp # Import for NumPY C-API def func (): # declarations can be made only in function scope cdef cnp . However, it can be initialized with a Python object that supports the buffer protocol (typically “array” types, for example a Numpy array). Cython supports numpy arrays but since these are Python objects, we can’t manipulate them without the GIL. It was thus changed in Cython 0.21 to use the same type for all memory views of a fused type. It is possible to access the underlying C array of a Python array from within Cython. I tried to Cythonize part of my code as following to hopefully gain some speed: # cython: boundscheck=False import numpy as np cimport numpy as np import time cpdef object 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): cdef double minimum_of_neighbours, difference, change cdef int i cdef … In the past, the workaround was to use pointers on the data, but that can get ugly very quickly, especially when you need to care about the memory alignment of 2D arrays ( C vs Fortran ). Also, when additional Cython declarations are made for NumPy arrays, indexing can be as fast as indexing C arrays. The code below defines the variables discussed previously, which are maxval, total, k, t1, t2, and t. There is a new variable named arr which holds the array, with data type numpy.ndarray. int** for a pointer to a pointer to a C int. # # The arrays f, g and h is typed as "np.ndarray" instances. np.zeros((len(ArgArray), dtype = np.int32) works in Python just as well as in Cython. Arrays use the normal C array syntax, e.g. Its elements may be Python/C types (dtype), but the array as a whole is an object. so input is a numpy.ndarray that means a python object, and cython refuses to take its address. A useful Cython type for getting quick access to blocks of memory. int[10], and the size must be known at compile time for stack allocated arrays. The same is the case for the input[0,0] - it is a python object. for in range(N)), Cython can convert that into a pure C for loop. You can also explicitly define the data type using the dtype option as an argument of array function. An array object represents a multidimensional, homogeneous array of fixed-size items. No luck so far. Cython doesn’t support variable length arrays … In my limited testing both of your cdefs work. The dtype method determines the datatype of elements stored in NumPy array. numpy.ndarray¶ class numpy.ndarray [source] ¶. This was unexpected for most users, unlikely to be desired, and also inconsistent with other structured type declarations like C arrays of fused types, which were considered the same type. When taking Cython into the game that is no longer true. ndarray [ cnp . Working with Python arrays¶ Python has a builtin array module supporting dynamic 1-dimensional arrays of primitive types. When the Python for structure only loops over integer values (e.g. At the same time they are ordinary Python objects which can be stored in lists and serialized between processes when using multiprocessing. Pointer types are constructed as in C, by appending a * to the base type they point to, e.g. No conversion to a Python 'type' is needed. A numpy array is a Python object. 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