如何将一个动态分配的C数组转换为Numpy数组，并在C扩展模块中返回给Python

所以我在这里的线程中发现了类似的问题，但我还没有找到适合我的解决方案.

我正在用Python 3.9在Visual Studio 2022中构建Python的C扩展模块.该模块接受numpy数组作为输入并返回numpyarray.现在，我只需要让它读取输入数组的形状，并使用它来决定要创建的输出数组的大小.如果输入为shape(row，col)，则输出为shape(2，row，col).

我动态分配一个包含2*row*col个元素的Carray.

以下是功能static PyObject* transform(PyObject* self, PyObject* args) {}的全部内容

import_array();
PyObject* data_obj;
PyArrayObject* data_array;


if (!PyArg_ParseTuple(args, "O", &data_obj)) {
    return NULL;
}

data_array = (PyArrayObject*)PyArray_FROM_OTF(data_obj, NPY_DOUBLE, NPY_ARRAY_IN_ARRAY);

if (data_array == NULL) {
    PyErr_SetString(PyExc_TypeError, "The data input must be a NumPy Array (2D or 3D)");
    return NULL;
}

int ndim = PyArray_NDIM(data_array);
npy_intp* data_shape = PyArray_SHAPE(data_array);

size_t rows, columns;
rows = data_shape[0];
columns = data_shape[1];

double* r_arr = (double*)malloc((rows * columns * 2) * sizeof(double));
if (r_arr == NULL) {
    PyErr_SetString(PyExc_ValueError, "Failed to allocate memory to arrays.");
    Py_DECREF(data_array);
}

然后，我在行和列上循环，并进行几次计算，在每行和列上生成两个数字，并将它们存储为r_arr[rr * rows + cc]和r_arr[rr * rows + cc + rows * columns]

for (size_t cc = 0; cc < columns; ++cc) {
    // some calculation
    for (size_t rr = 0; rr < rows; ++rr) {
        // some calculation
        r_arr[rr * rows + cc] = result1;
        r_arr[rr * rows + cc + rows * columns] = result2;
        PySys_WriteStdout("%f, %f\n", r_arr[rr * rows + cc], r_arr[rr * rows + cc + rows * columns]);
    }
}

最后，我想把r_arr变成一个numpy数组，然后返回Python.我在这里try 了很多东西，但这是它的当前状态，基于阅读这里的其他线程.

npy_intp dims[3] = { 2, rows, columns };
PyObject* r_obj = (PyArrayObject *)PyArray_SimpleNewFromData(3, dims, NPY_DOUBLE, r_arr);

if (r_obj == NULL) {
    PyErr_Print("Failed to create internal arrays. Likely due to data input being incorrect shape.");
    return NULL;
}

Py_DECREF(data_array);
free(x);
free(y);
//free(r_arr);
PyArray_ENABLEFLAGS(r_obj, NPY_ARRAY_OWNDATA);
PySys_WriteStdout("Returning result\n");
return Py_BuildValue("O", r_obj);

在Python中，当我用shape(5，10)创建一个数组并将其用作输入时，我得到了以下结果.嵌套循环打印出正确的值，但返回的数组似乎是我使用np.empty((2, 5, 10))时所得到的结果.

我还try 使用PyArray_SimpleNewFromData()创建一个包含2*rows*columns个元素的1D numpy数组，这并没有什么不同.

输出:

0.023581, 4.986665
0.021305, 3.986672
0.018796, 2.986680
0.016282, 1.986689
0.014207, 0.986699
-0.986851, 4.986664
-0.986798, 3.986672
-0.986746, 2.986680
-0.986700, 1.986689
-0.986667, 0.986699
-1.986746, 4.986662
-1.986718, 3.986670
-1.986691, 2.986679
-1.986666, 1.986688
-1.986648, 0.986699
-2.986705, 4.986660
-2.986685, 3.986668
-2.986665, 2.986677
-2.986646, 1.986687
-2.986632, 0.986698
-3.986679, 4.986656
-3.986662, 3.986665
-3.986645, 2.986675
-3.986629, 1.986685
-3.986616, 0.986697
-4.986658, 4.986651
-4.986642, 3.986661
-4.986626, 2.986672
-4.986611, 1.986683
-4.986599, 0.986696
-5.986637, 4.986645
-5.986622, 3.986657
-5.986607, 2.986669
-5.986592, 1.986681
-5.986579, 0.986695
-6.986616, 4.986638
-6.986601, 3.986651
-6.986586, 2.986664
-6.986571, 1.986678
-6.986558, 0.986694
-7.986593, 4.986630
-7.986578, 3.986645
-7.986563, 2.986660
-7.986547, 1.986675
-7.986533, 0.986692
-8.986567, 4.986621
-8.986552, 3.986637
-8.986536, 2.986654
-8.986520, 1.986672
-8.986505, 0.986690
Returning result
array([[[ 1.14460020e-311,  1.14458244e-311,  1.14458542e-311,
          1.14478895e-311,  1.14478881e-311,  1.14478881e-311,
          1.14478893e-311,  1.14478894e-311,  1.14458542e-311,
          1.14478894e-311],
        [-8.98656729e+000,  1.14478893e-311,  1.14458542e-311,
          1.14478894e-311,  1.14478894e-311, -8.98655189e+000,
          1.14458542e-311,  1.14478895e-311,  1.14478895e-311,
          1.14478894e-311],
        [-8.98653594e+000,  1.14478884e-311,  1.14478894e-311,
          1.14478894e-311,  1.14478894e-311, -8.98652003e+000,
          1.14478884e-311,  1.14478894e-311,  1.14478894e-311,
          1.14478894e-311],
        [-8.98650487e+000,  1.14478893e-311,  1.14478895e-311,
          1.14478894e-311,  1.14478894e-311,  1.14478894e-311,
          1.14478884e-311,  1.14478894e-311,  1.14478894e-311,
          1.14478894e-311],
        [ 1.14478894e-311,  1.14478893e-311,  1.14478894e-311,
          1.14478894e-311,  1.14478894e-311,  1.14478894e-311,
          1.14478894e-311,  1.14478895e-311,  1.14478895e-311,
          1.14478895e-311]],

       [[ 1.14478894e-311,  1.14458542e-311,  1.14478895e-311,
          1.14478895e-311,  1.14478893e-311,  1.14478895e-311,
          1.14478893e-311,  1.14458542e-311,  1.14478884e-311,
          1.14478893e-311],
        [ 4.98662116e+000,  1.14478895e-311,  1.14478893e-311,
          1.14478894e-311,  1.14478895e-311,  3.98663750e+000,
          1.14478884e-311,  1.14458542e-311,  1.14478893e-311,
          1.14458542e-311],
        [ 2.98665407e+000,  1.14478895e-311,  1.14478895e-311,
          1.14478895e-311,  1.14478895e-311,  1.98667152e+000,
          1.14458542e-311,  1.14458542e-311,  1.14478895e-311,
          1.14478895e-311],
        [ 9.86690488e-001,  1.14478895e-311,  1.14478895e-311,
          1.14458542e-311,  1.14478884e-311,  1.14458542e-311,
          1.14478894e-311,  1.14478893e-311,  1.14458542e-311,
          1.14478895e-311],
        [ 1.14478895e-311,  1.14478884e-311,  1.14478893e-311,
          1.14478893e-311,  1.14458542e-311,  1.14478895e-311,
          9.38662745e-097,  2.07712408e-308,  1.14478658e-311,
          1.14458243e-311]]])