1

按照示例和 numpy C-API ( http://docs.scipy.org/doc/numpy/reference/c-api.html ),我正在尝试访问 cpp 中的 numpy 数组数据,如下所示:

#include <Python.h>
#include <frameobject.h>
#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION // TOGGLE OR NOT
#include "numpy/ndarraytypes.h"
#include "numpy/arrayobject.h"
...
// here I have passed "some_python_object" to the C code
// .. and "some_python_object" has member "infobuf" that is a numpy array
//
unsigned long* fInfoBuffer;
PyObject* infobuffer = PyObject_GetAttrString(some_python_object, "infobuf");
PyObject* x_array    = PyArray_FROM_OT(infobuffer, NPY_UINT32);
fInfoBuffer          = (unsigned long*)PyArray_DATA(x_array); // DOES NOT WORK WHEN API DEPRECATION IS TOGGLED

当 API 弃用被切换时,我在编译时得到:

error: cannot convert ‘PyObject* {aka _object*}’ to ‘PyArrayObject* {aka tagPyArrayObject*}’ for argument ‘1’ to ‘void* PyArray_DATA(PyArrayObject*)’

在 numpy 1.7+ 中这样做的合法方式是什么?

4

2 回答 2

2

您可以尝试使用更高级别的库,该库将 numpy 数组包装在具有适当容器语义的 C++ 容器中。

试用xtensorxtensor-python绑定。

还有一个 cookiecutter 可以生成一个最小的 C++ 扩展项目,其中包含用于测试的所有样板、html 文档和 setup.p...

示例:C++ 代码

#include <numeric>                        // Standard library import for std::accumulate
#include "pybind11/pybind11.h"            // Pybind11 import to define Python bindings
#include "xtensor/xmath.hpp"              // xtensor import for the C++ universal functions
#define FORCE_IMPORT_ARRAY                // numpy C api loading
#include "xtensor-python/pyarray.hpp"     // Numpy bindings

double sum_of_sines(xt::pyarray<double> &m)
{
    auto sines = xt::sin(m);
    // sines does not actually hold any value, which are only computed upon access
    return std::accumulate(sines.begin(), sines.end(), 0.0);
}

PYBIND11_PLUGIN(xtensor_python_test)
{
    xt::import_numpy();
    pybind11::module m("xtensor_python_test", "Test module for xtensor python bindings");

    m.def("sum_of_sines", sum_of_sines,
        "Computes the sum of the sines of the values of the input array");

    return m.ptr();
}

蟒蛇代码:

import numpy as np
import xtensor_python_test as xt

a = np.arange(15).reshape(3, 5)
s = xt.sum_of_sines(v)
s
于 2017-04-12T00:30:44.843 回答
0

这是因为PyArray_DATA期望一个PyArrayObject*. 您可以尝试更改的类型x_array

PyArrayObject* x_array = (PyArrayObject*) PyArray_FROM_OT(infobuffer, NPY_UINT32)
于 2019-07-13T21:12:19.800 回答