通过C模块中的Python API访问数组的数组

# C 代码
​
# 包含 NumPy 头文件
# conda install numpy
# pip install numpy
# apt-get install python3-numpy  (For Debian / Ubuntu)
# yum install python3-numpy  (For CentOS / Red Hat)
# dnf install python3-numpy  (For Fedora)
# zypper install python3-numpy  (For OpenSUSE)
# Include the necessary header files
# For Windows: Python.h must be placed in the project directory
# For Linux: python3.X/Include/Python.h
# For macOS: /Library/Frameworks/Python.framework/Versions/3.X/Headers/Python.h
# https://www.python.org/dev/embedding/
# https://www.oreilly.com/library/view/python-c-extension/0596001566/re314.html
# https://docs.python.org/3/c-api/array.html
# https://docs.scipy.org/doc/numpy/reference/c-api/c-api-intro.html
// https://stackoverflow.com/questions/5390264/numpy-array-of-objects-passing-to-c-and-access-to-these-objects-in-c
# https://docs.scipy.org/doc/numpy-1.15.1/reference/c-api/ndarray.html
# https://github.com/numpy/numpy/issues/11485
# https://github.com/numpy/numpy/issues/2662
# https://www.oreilly.com/library/view/python-c-extension/0596001566/re335.html
# https://stackoverflow.com/questions/47386916/how-to-create-a-numpy-array-of-objects-in-c-extension
// https://cs231n.github.io/python-numpy-tutorial/
# https://jonathannienaber.de/programming/python-native-memoryview-efficient-cpython-extension-types/
# https://numpy.org/doc/stable/c-api/c-api-reference.html
# https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html
# https://numpy.org/doc/stable/user/basics.types.html
// https://stackoverflow.com/questions/37056848/how-to-pass-a-numpy-array-from-python-to-c
// https://numpy.org/doc/stable/reference/c-api/shape.html
// https://stackoverflow.com/questions/68689338/convert-numpy-array-of-bytes-into-list-of-strings
# https://numpy.org/doc/stable/reference/c-api/array.html
// https://numpy.org/doc/stable/user/basics.creation.html#creating-arrays
​
# https://numpy.org/doc/stable/reference/c-api/dtype.html
# https://numpy.org/doc/stable/reference/c-api/dtype_buffer.html
# https://numpy.org/doc/stable/reference/c-api/dtype_flag.html
# https://scipython.com/blog/interacting-between-python-and-c-data-structures/
​
# 定义必要的变量和数据结构
PyObject *PyArray, *PySubArray;
int PyNumDims = 0;
npy_intp *PyShape = NULL;
PyArray_Descr *PyDtype = NULL;
void *PyDataPtr = NULL;
​
// 将传递给 C 模块的数组的数组转换为兼容的 NumPy 数组
PyObject *PyArrayConvert(PyObject *PyArray) {
  // 检查输入数组是否为数组对象
  if (!PyArray_Check(PyArray)) {
    PyErr_SetString(PyExc_TypeError, "Input must be a NumPy array");
    return NULL;
  }
​
  // 检查输入数组是否是数组的数组
  PyNumDims = PyArray_NDIM(PyArray);
  if (PyNumDims != 2) {
    PyErr_SetString(PyExc_TypeError, "Input must be a 2-dimensional array");
    return NULL;
  }
​
  // 检查输入数组的元素类型是否兼容
  PyDtype = PyArray_DESCR(PyArray);
  if (PyDtype->type_num != NPY_OBJECT) {
    PyErr_SetString(PyExc_TypeError, "Input array must have object data type");
    return NULL;
  }
​
  // 将输入数组转换为兼容的 NumPy 数组
  PyArray = PyArray_FromArray(PyArray, PyArray_DescrFromType(NPY_OBJECT), NPY_ARRAY_DEFAULT);
  if (PyArray == NULL) {
    PyErr_SetString(PyExc_RuntimeError, "Failed to convert input array");
    return NULL;
  }
​
  return PyArray;
}
​
// 从兼容的 NumPy 数组中提取数组的数组元素
PyObject **PyExtractSubArrays(PyObject *PyArray) {
  // 获取兼容的 NumPy 数组的形状和数据指针
  PyShape = PyArray_SHAPE(PyArray);
  PyDataPtr = PyArray_DATA(PyArray);
​
  // 分配内存来存储提取的数组的数组元素
  PyObject **PySubArrays = malloc(PyShape[0] * sizeof(PyObject *));
  if (PySubArrays == NULL) {
    PyErr_SetString(PyExc_MemoryError, "Failed to allocate memory for sub-arrays");
    return NULL;
  }
​
  // 提取兼容的 NumPy 数组的数组的数组元素
  for (int i = 0; i < PyShape[0]; i++) {
    PySubArrays[i] = ((PyObject **)PyDataPtr)[i];
  }
​
  return PySubArrays;
}
​
// 释放提取的数组的数组元素的内存
void PyFreeSubArrays(PyObject **PySubArrays) {
  free(PySubArrays);
}
​
// 在 C 模块中访问提取的数组的数组元素
void PyAccessSubArrays(PyObject **PySubArrays) {
  // 循环访问提取的数组的数组元素
  for (int i = 0; i < PyShape[0]; i++) {
    // 访问提取的数组的数组元素的元素
    for (int j = 0; j < PyShape[1]; j++) {
      // 根据需要访问提取的数组的数组元素的元素的值
      // printf("%d\n", ((int **)PySubArrays[i])[j]);
    }
  }
}
​
// 在 C 模块中释放 kompatibler NumPy 数组的内存
void PyFreeArray(PyObject *PyArray) {
  Py_DECREF(PyArray);
}

# Python 代码
​
# 导入必要的库
import numpy as np
import geoms
​
# 创建一个包含数组的数组
A=np.empty((1,2),dtype=object)
A[0,0] = np.random.rand(3,3)
A[0,1] = np.random.rand(5,5)
​
# 将数组的数组传递给 C 模块
geoms.gm_unique_all(A)