memoryview

问题 I am having trouble creating a simple class in Cython. There is little documentation related to handling memoryviews for arrays in C++ wrappers. I want to create a data class with time, x, y, and z attributes. I need these attributes to be arrays, which will ultimately be callable in Python. I previously had this working using numpy types, but I would like to do this properly using memoryviews. My background is not strong in C++. For now, I am only trying to get the time attribute working

问题 I am having trouble creating a simple class in Cython. There is little documentation related to handling memoryviews for arrays in C++ wrappers. I want to create a data class with time, x, y, and z attributes. I need these attributes to be arrays, which will ultimately be callable in Python. I previously had this working using numpy types, but I would like to do this properly using memoryviews. My background is not strong in C++. For now, I am only trying to get the time attribute working

问题 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,

问题 I have a cython module that uses memoryview arrays, that is... double[:,:] foo I want to run this module in parallel using multiprocessing. However I get the error: PicklingError: Can't pickle <type 'tile_class._memoryviewslice'>: attribute lookup tile_class._memoryviewslice failed Why can't I pickle a memory view and what can I do about it. 回答1: Maybe passing the actual array instead of the memory view can solve your problem. If you want to execute a function in parallel, all of it

问题 The python documentation on array clearly states that the array conforms to the buffer interface. It even suggest not using the buffer_info() method. But when I try to get a Py_Buffer from C/C++ code with PyObject_GetBuffer() or use python's memoryview, I get a failure. For example, in python (I use version 2.7): >>> a = array.array('c') >>> memoryview(a) Traceback (most recent call last): File "<stdin>", line 1, in <module> TypeError: cannot make memory view because object does not have the

问题 The python documentation on array clearly states that the array conforms to the buffer interface. It even suggest not using the buffer_info() method. But when I try to get a Py_Buffer from C/C++ code with PyObject_GetBuffer() or use python's memoryview, I get a failure. For example, in python (I use version 2.7): >>> a = array.array('c') >>> memoryview(a) Traceback (most recent call last): File "<stdin>", line 1, in <module> TypeError: cannot make memory view because object does not have the

问题 In my program code I've got numpy value arrays and numpy index arrays. Both kinds are preallocated and predefined during program initialization. Each part of the program has one array values on which calculations are performed, and three index arrays idx_from_exch , idx_values and idx_to_exch . There is on global value array to exchange the values of several parts: exch_arr . The index arrays most of the times have between 2 and 5 indices, seldom (most probably never) more indices are needed.

问题 I have a question regarding numpys memory views: Suppose we have two arrays with memory: import numpy as np import gc x = np.arange(4*3).reshape(4,3).astype(float) y = (np.arange(5) - 5).astype(float) y_ref = y We use these ( x , y ) in a framework, such that we cannot just redefine them, as the user may have linked them for himself (as in y_ref ). Now we want to combine their memory in one view. So, that the single view, say p shares the memory with both arrays. I did it in the following way

问题 I am trying to assign the output of linalg inverse function (la.inv) to a view in cython. Unfortunately this does not work. I can always assign the output of la.inv() to a temporary ndarray object and then copy its content to the view. Is there a better way to do it. cpdef int testfunc1(np.ndarray[np.float_t, ndim=2] A, double [:,:] B) except -1: print("inverse of A:", la.inv(A)) if np.isnan(A).any(): return -1 else: B = la.inv(A) return 1 cpdef int testfunc2(np.ndarray[np.float_t, ndim=2] A)

问题 I read a large file in the code below which has a special structure - among others two blocks that need be processed at the same time. Instead of seeking back and forth in the file I load these two blocks wrapped in memoryview calls with open(abs_path, 'rb') as bsa_file: # ... # load the file record block to parse later file_records_block = memoryview(bsa_file.read(file_records_block_size)) # load the file names block file_names_block = memoryview(bsa_file.read(total_file_name_length)) #