如何从指针创建n-dim numpy数组? [英] How to create n-dim numpy array from a pointer?

问题描述

我已经阅读了有关numpy.frombuffer的信息，但找不到任何从指针创建数组的方法.

I've read about numpy.frombuffer, but can't find any way to create array from pointer.

推荐答案

如上面的注释所述，您可以使用numpy.ctypeslib.as_array:

As pointed out in the comments above, you can use numpy.ctypeslib.as_array:

numpy.ctypeslib.as_array(obj，shape = None)

numpy.ctypeslib.as_array(obj, shape=None)

从ctypes数组或ctypes POINTER创建一个numpy数组. numpy数组与ctypes对象共享内存.

Create a numpy array from a ctypes array or a ctypes POINTER. The numpy array shares the memory with the ctypes object.

如果从ctypes POINTER进行转换，则必须提供size参数. 从ctypes数组转换时，将忽略size参数

The size parameter must be given if converting from a ctypes POINTER. The size parameter is ignored if converting from a ctypes array

因此，让我们模仿一个C函数，该函数返回一个对malloc的调用的指针:

So let's mimic a C function returning a pointer with a call to malloc:

import ctypes as C
from ctypes.util import find_library
import numpy as np

SIZE = 10

libc = C.CDLL(find_library('c'))
libc.malloc.restype = C.c_void_p

# get a pointer to a block of data from malloc
data_pointer = libc.malloc(SIZE * C.sizeof(C.c_int))
data_pointer = C.cast(data_pointer,C.POINTER(C.c_int))

您现在可以使该指针指向的数据可用于numpy

You can now make the data this pointer points to available to numpy

new_array = np.ctypeslib.as_array(data_pointer,shape=(SIZE,))

并证明他们正在访问相同的内存:

And to prove that they are accessing the same memory:

new_array[:] = range(SIZE)

print "Numpy array:",new_array[:SIZE]
print "Data pointer: ",data_pointer[:SIZE]

应输出:

Numpy array: [0 1 2 3 4 5 6 7 8 9]
Data pointer:  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

最后，请记住，numpy数组不拥有其内存，因此需要显式调用free以避免内存泄漏.

As a final note remember that the numpy array does not own its memory so explicit calls to free are required to avoid memory leaks.

del new_array
libc.free(data_pointer)

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