memoryview

I have a memoryview on a numpy array and want to copy the content of another numpy array into it by using this memoryview : import numpy as np cimport numpy as np cdef double[:,::1] test = np.array([[0,1],[2,3]], dtype=np.double) test[...] = np.array([[4,5],[6,7]], dtype=np.double) But why is this not possible? It keeps me telling TypeError: only length-1 arrays can be converted to Python scalars Blockquote It works fine if I copy from a memoryview to a memoryview , or from a numpy array to a numpy array, but how to copy from a numpy array to a memoryview ? hpaulj These assignments work: cdef

Python 2.7 has introduced a new API for buffers and memoryview objects . I read the documentation on them and I think I got the basic concept (accessing the internal data of an object in a raw form without copying it, which I suppose means a "faster and less memory-hungry" way to get object data), but to really understand the documentation, the reader should have a knowledge of C that is beyond the one I have. I would be very grateful if somebody would take the time to: explain buffers and memoryview objects in "layman terms" and describe a scenario in which using buffers and memoryview

How to convert a typed memoryview to an NumPy array in cython? The docs have cimport numpy as np import numpy as np numpy_array = np.asarray(<np.int32_t[:10, :10]> my_pointer) I took this for my case np.asarray(<np.float_t[:, :]> my_memview) Using this the compiler tells me: Can only create cython.array from pointer or array Copying or not is not so decicive. I couldn't find any help on this. You should just be able to use np.asarray directly on the memoryview itself, so something like: np.asarray(my_memview) should work. For example if your cython source file contains this: import numpy as np

Since I found memory-views handy and fast, I try to avoid creating NumPy arrays in cython and work with the views of the given arrays. However, sometimes it cannot be avoided, not to alter an existing array but create a new one. In upper functions this is not noticeable, but in often called subroutines it is. Consider the following function #@cython.profile(False) @cython.boundscheck(False) @cython.wraparound(False) @cython.nonecheck(False) cdef double [:] vec_eq(double [:] v1, int [:] v2, int cond): ''' Function output corresponds to v1[v2 == cond]''' cdef unsigned int n = v1.shape[0] cdef

int main(){ int i; int arr[4]; for(int i=0; i<=4; i++){ arr[i] = 0; } return 0; } I watched a video on youtube of CS107(lecture 13) in which this example is shown and told why the above program will lead to infinite loop by showing memory diagrams. arr[4] goes out of bounds and should lead to an address where i is stored and changing the value of i back to 0 hence leading to infinite loop. But when I tried to run this on my mac using gcc compiler, for loop executed (checked by inserting printf) 5 times. i.e for the value of i = 0,1,2,3,4. When there is undefined behavior you can't expect a

How can I sort a memoryview in-place in Cython? Is there a built-in function that can do it? Right now I have to use a numpy array instead and use numpy 's sort, which is very slow. To follow up on my comment, here are 3 options (numpy and a C and C++ standard library option) from libcpp.algorithm cimport sort from libc.stdlib cimport qsort import numpy as np def sort_numpy(double[:] a, kind): np.asarray(a).sort(kind=kind) # needs to be compiled with C++ def sort_cpp(double[::1] a): # a must be c continuous (enforced with [::1]) sort(&a[0], (&a[0]) + a.shape[0]) # The C version requires a