从ctypes指针到结构数组的NumPy结构化/常规数组 [英] NumPy structured / regular array from ctypes pointer to array of structs

问题描述

说我有以下C函数:

void getArrOfStructs(SomeStruct** ptr, int* numElements)

以及以下C结构:

typedef struct SomeStruct
{
    int x;
    int y;
};

我能够成功获取Python列表:

I am able to successfully get a Python list:

class SomeStruct(Structure):
    _fields_ = [('x', c_int),
                ('y', c_int)]
ptr, numElements = pointer(SomeStruct()), c_int()
myDLL.getArrOfStructs(byref(ptr), byref(numElements)))

我想获得一个NumPy结构化/常规数组.

I want to get a NumPy structured / regular array.

1. 结构化数组与常规数组:从术语上讲，哪个更可取?
2. 我该怎么办?我正在寻找一种有效的方法(不复制每个单元格).我尝试了NumPy的frombuffer()函数，但只能与常规的C数组一起使用.

1. Structured vs Regular array: which one is preferable (in terms of terminology)?
2. How can I do it? I'm looking for an efficient way (without copy each cell). I tried NumPy's frombuffer() functions, but was only able to use it with regular C arrays.

推荐答案

numpy数组的视图共享一个数据缓冲区

Views of numpy arrays share a data buffer

In [267]: x=np.arange(6).reshape(3,2)
In [268]: x.tostring()
Out[268]: b'\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00\x04\x00\x00\x00\x05\x00\x00\x00'
In [269]: x.view('i,i')
Out[269]: 
array([[(0, 1)],
       [(2, 3)],
       [(4, 5)]], 
      dtype=[('f0', '<i4'), ('f1', '<i4')])

在此示例中，数据缓冲区是一个24字节的C数组，可以通过多种方式查看-平面数组，2列或具有2个字段的结构化数组.

In this example the databuffer is a C array of 24 bytes, which can viewed in various ways - a flat array, 2 columns, or structured array with 2 fields.

我还没有使用ctypes，但是我确定有与np.frombuffer等效的东西可以从字节缓冲区构造数组.

I haven't worked with ctypes but I'm sure there's something equivalent to np.frombuffer to construct an array from a byte buffer.

In [273]: np.frombuffer(x.tostring(),int)
Out[273]: array([0, 1, 2, 3, 4, 5])
In [274]: np.frombuffer(x.tostring(),'i,i')
Out[274]: 
array([(0, 1), (2, 3), (4, 5)], 
      dtype=[('f0', '<i4'), ('f1', '<i4')])

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