Question

我有一个带动态分配数组的ctypes结构，即：

array_1d_double=npct.ndpointer(dtype=np.double,ndim=1,
                               flags='CONTIGUOUS')
class Test(Structure):
    _fields_ = ("x", array_1d_double, ..)

test = Test()
do_some_init_in_c(  for example malloc)

如果我打印test.x，我会得到以下输出：

<ndpointer_<f8_1d_CONTIGUOUS object at 0x7f104dc0c3b0>

c结构看起来大致如此，

structure Test_s{
    double *x;....
};

如何像numpy数组一样访问这个元素？是否可能需要将数组分配为np.arrays而不是使用malloc？这样做的正确方法是什么？

Answer 1

你正在谈论的一种方法是直接在python端分配numpy数组，并且表现得像是C端的直接双数组。

import numpy as np
import ctypes as C

# allocate this as a normal numpy array with specified dtype
array_1d_double = np.array([1,2,3,4,5],dtype="float64")

# set the structure to contain a C double pointer
class Test(C.Structure):
    _fields_ = [("x", C.POINTER(C.c_double))]

# Instantiate the structure so it can be passed to the C code
test = Test(np.ctypeslib.as_ctypes(array_1d_double))

# You can also do:
# test = Test()
# test.x = np.ctypeslib.as_ctypes(array_1d_double)

print test.x
# outputs: <__main__.LP_c_double object at 0x1014aa320>

您现在应该能够将结构的x成员用作C代码中的普通双数组。

修改

澄清一下：如果您实例化一个没有参数的Structure，它会为其所有成员提供NULL个指针。

class Test(C.Structure): _fields_ = [("x", C.POINTER(C.c_double)), ("y", C.POINTER(C.c_int))] test = Test() print test.x # outputs: <__main__.LP_c_double object at 0x1014aa320> print test.y # outputs: <__main__.LP_c_int object at 0x101429320> print test.x[0] # raises ValueError: NULL pointer access print test.y[0] # raises ValueError: NULL pointer access

如果使用N个参数实例化Structure，那么这些参数将被分配给Structure的前N个成员。

test = Test(np.ctypeslib.as_ctypes(array_1d_double)) print text.x[0] # outputs: 1.0 print test.y[0] # raises ValueError: NULL pointer access

<强> EDIT2

如果要将numpy数组永久绑定到结构，可以覆盖__init__方法：

class MyDualArrayStruct(C.Structure): _fields_ = [("c_x", C.POINTER(C.c_double)), ("c_y", C.POINTER(C.c_int))] def __init__(self,*args,**kwargs): super(MyDualArrayStruct,self).__init__(*args,**kwargs) self.np_x = np.array([1,2,3,4,5],dtype="float64") self.c_x = np.ctypeslib.as_ctypes(self.np_x) self.np_y = np.array([5,4,3,2,1],dtype="int32") self.c_y = np.ctypeslib.as_ctypes(self.np_y) test = MyDualArrayStruct() print test.np_x print test.c_x[:5] # Note that here c_x and np_x both contain the same data. Thus modifying one of them # (inplace) modifies the other. You can use this to do easy inplace modification of # numpy arrays in C functions. # This implies that test.np_x.sum() is also the sum of test.c_x test.np_x[:] = 1 print test.np_x print test.c_x[:5]

输出：

[ 1. 2. 3. 4. 5.] [1.0, 2.0, 3.0, 4.0, 5.0] [ 1. 1. 1. 1. 1.] [1.0, 1.0, 1.0, 1.0, 1.0]

在ctypes结构中访问np.array

1 个答案: