问题
In a Python extension module I've written in C++, I use the following snippet of code to convert a NumPy array into an Armadillo array for use in the C++ portion of the code:
static arma::mat convertPyArrayToArma(PyArrayObject* pyarr, int nrows, int ncols)
{
// Check if the dimensions are what I expect.
if (!checkPyArrayDimensions(pyarr, nrows, ncols)) throw WrongDimensions();
const std::vector<int> dims = getPyArrayDimensions(pyarr); // Gets the dimensions using the API
PyArray_Descr* reqDescr = PyArray_DescrFromType(NPY_DOUBLE);
if (reqDescr == NULL) throw std::bad_alloc();
// Convert the array to Fortran-ordering as required by Armadillo
PyArrayObject* cleanArr = (PyArrayObject*)PyArray_FromArray(pyarr, reqDescr,
NPY_ARRAY_FARRAY);
if (cleanArr == NULL) throw std::bad_alloc();
reqDescr = NULL; // The new reference from DescrFromType was stolen by FromArray
double* dataPtr = static_cast<double*>(PyArray_DATA(cleanArr));
arma::mat result (dataPtr, dims[0], dims[1], true); // this copies the data from cleanArr
Py_DECREF(cleanArr);
return result;
}
The problem is that when I pass this a view of a NumPy array (i.e. my_array[:, 3]), it doesn't seem to handle the strides of the underlying C array correctly. Based on the output, it seems like the array pyarr received by the function is actually the full base array, and not the view (or at least when I access the data using PyArray_DATA, I seem to be getting a pointer to the full base array). If I instead pass this function a copy of the view (i.e. my_array[:, 3].copy()), it works as expected, but I don't want to have to remember to do that every time.
So, is there a way to make PyArray_FromArray copy only the slice of the matrix I want? I tried using the flag NPY_ARRAY_ENSURECOPY, but that didn't help.
Edit 1
As suggested in the comments, here is a full working example:
In file example.cpp:
#define NPY_NO_DEPRECATED_API NPY_1_9_API_VERSION
extern "C" {
#include <Python.h>
#include <numpy/arrayobject.h>
}
#include <exception>
#include <cassert>
#include <string>
#include <type_traits>
#include <map>
#include <vector>
#include <armadillo>
class WrongDimensions : public std::exception
{
public:
WrongDimensions() {}
const char* what() const noexcept { return msg.c_str(); }
private:
std::string msg = "The dimensions were incorrect";
};
class NotImplemented : public std::exception
{
public:
NotImplemented() {}
const char* what() const noexcept { return msg.c_str(); }
private:
std::string msg = "Not implemented";
};
class BadArrayLayout : public std::exception
{
public:
BadArrayLayout() {}
const char* what() const noexcept { return msg.c_str(); }
private:
std::string msg = "The matrix was not contiguous";
};
static const std::vector<npy_intp> getPyArrayDimensions(PyArrayObject* pyarr)
{
npy_intp ndims = PyArray_NDIM(pyarr);
npy_intp* dims = PyArray_SHAPE(pyarr);
std::vector<npy_intp> result;
for (int i = 0; i < ndims; i++) {
result.push_back(dims[i]);
}
return result;
}
/* Checks the dimensions of the given array. Pass -1 for either dimension to say you don't
* care what the size is in that dimension. Pass dimensions (X, 1) for a vector.
*/
static bool checkPyArrayDimensions(PyArrayObject* pyarr, const npy_intp dim0, const npy_intp dim1)
{
const auto dims = getPyArrayDimensions(pyarr);
assert(dims.size() <= 2 && dims.size() > 0);
if (dims.size() == 1) {
return (dims[0] == dim0 || dim0 == -1) && (dim1 == 1 || dim1 == -1);
}
else {
return (dims[0] == dim0 || dim0 == -1) && (dims[1] == dim1 || dim1 == -1);
}
}
template<typename outT>
static arma::Mat<outT> convertPyArrayToArma(PyArrayObject* pyarr, int nrows, int ncols)
{
if (!checkPyArrayDimensions(pyarr, nrows, ncols)) throw WrongDimensions();
int arrTypeCode;
if (std::is_same<outT, uint16_t>::value) {
arrTypeCode = NPY_UINT16;
}
else if (std::is_same<outT, double>::value) {
arrTypeCode = NPY_DOUBLE;
}
else {
throw NotImplemented();
}
const auto dims = getPyArrayDimensions(pyarr);
if (dims.size() == 1) {
outT* dataPtr = static_cast<outT*>(PyArray_DATA(pyarr));
return arma::Col<outT>(dataPtr, dims[0], true);
}
else {
PyArray_Descr* reqDescr = PyArray_DescrFromType(arrTypeCode);
if (reqDescr == NULL) throw std::bad_alloc();
PyArrayObject* cleanArr = (PyArrayObject*)PyArray_FromArray(pyarr, reqDescr, NPY_ARRAY_FARRAY);
if (cleanArr == NULL) throw std::bad_alloc();
reqDescr = NULL; // The new reference from DescrFromType was stolen by FromArray
outT* dataPtr = static_cast<outT*>(PyArray_DATA(cleanArr));
arma::Mat<outT> result (dataPtr, dims[0], dims[1], true); // this copies the data from cleanArr
Py_DECREF(cleanArr);
return result;
}
}
static PyObject* convertArmaToPyArray(const arma::mat& matrix)
{
npy_intp ndim = matrix.is_colvec() ? 1 : 2;
npy_intp nRows = static_cast<npy_intp>(matrix.n_rows); // NOTE: This narrows the integer
npy_intp nCols = static_cast<npy_intp>(matrix.n_cols);
npy_intp dims[2] = {nRows, nCols};
PyObject* result = PyArray_SimpleNew(ndim, dims, NPY_DOUBLE);
if (result == NULL) throw std::bad_alloc();
double* resultDataPtr = static_cast<double*>(PyArray_DATA((PyArrayObject*)result));
for (int i = 0; i < nRows; i++) {
for (int j = 0; j < nCols; j++) {
resultDataPtr[i * nCols + j] = matrix(i, j);
}
}
return result;
}
extern "C" {
// An example function that takes a NumPy array and converts it to
// an arma::mat and back. This should return the array unchanged.
static PyObject* example_testFunction(PyObject* self, PyObject* args)
{
PyArrayObject* myArray = NULL;
if (!PyArg_ParseTuple(args, "O!", &PyArray_Type, &myArray)) {
return NULL;
}
PyObject* output = NULL;
try {
arma::mat myMat = convertPyArrayToArma<double>(myArray, -1, -1);
output = convertArmaToPyArray(myMat);
}
catch (const std::bad_alloc&) {
PyErr_NoMemory();
Py_XDECREF(output);
return NULL;
}
catch (const std::exception& err) {
PyErr_SetString(PyExc_RuntimeError, err.what());
Py_XDECREF(output);
return NULL;
}
return output;
}
static PyMethodDef example_methods[] =
{
{"test_function", example_testFunction, METH_VARARGS, "A test function"},
{NULL, NULL, 0, NULL}
};
static struct PyModuleDef example_module = {
PyModuleDef_HEAD_INIT,
"example", /* name of module */
NULL, /* module documentation, may be NULL */
-1, /* size of per-interpreter state of the module,
or -1 if the module keeps state in global variables. */
example_methods
};
PyMODINIT_FUNC
PyInit_example(void)
{
import_array();
PyObject* m = PyModule_Create(&example_module);
if (m == NULL) return NULL;
return m;
}
}
And setup.py for compiling:
from setuptools import setup, Extension
import numpy as np
example_module = Extension(
'example',
include_dirs=[np.get_include(), '/usr/local/include'],
libraries=['armadillo'],
library_dirs=['/usr/local/lib'],
sources=['example.cpp'],
language='c++',
extra_compile_args=['-std=c++11', '-mmacosx-version-min=10.10'],
)
setup(name='example',
ext_modules=[example_module],
)
Now assume we have the example array
a = np.array([[ 1, 2, 3, 4, 5, 6],
[ 7, 8, 9,10,11,12],
[13,14,15,16,17,18]], dtype='float64')
The function seems to work fine for multidimensional slices like a[:, :3], and it returns the matrix unaltered as I'd expect. But if I give it a single-dimensional slice, I get the wrong components unless I make a copy:
>>> example.test_function(a[:, 3])
array([ 4., 5., 6.])
>>> example.test_function(a[:, 3].copy())
array([ 4., 10., 16.])
回答1:
The view of the array is just another information-wrapper for the same data array. Numpy doesn't copy any data here. Only the information for the interpretation of the data are adjusted and the pointer to the data moved if useful.
In your code you're assuming, that the data of a vector a[:, 3] is represented as a vector in memory which wouldn't differ for NPY_ARRAY_CARRAY and NPY_ARRAY_FARRAY. But this representation you only get after creating a (fortran ordered) copy of the array itself.
To make it work I modified your convertPyArrayToArma() function a little to create a copy even if it's a vector:
template<typename outT>
static arma::Mat<outT> convertPyArrayToArma(PyArrayObject* pyarr, int nrows, int ncols)
{
if (!checkPyArrayDimensions(pyarr, nrows, ncols)) throw WrongDimensions();
int arrTypeCode;
if (std::is_same<outT, uint16_t>::value) {
arrTypeCode = NPY_UINT16;
}
else if (std::is_same<outT, double>::value) {
arrTypeCode = NPY_DOUBLE;
}
else {
throw NotImplemented();
}
PyArray_Descr* reqDescr = PyArray_DescrFromType(arrTypeCode);
if (reqDescr == NULL) throw std::bad_alloc();
PyArrayObject* cleanArr = (PyArrayObject*)PyArray_FromArray(pyarr, reqDescr, NPY_ARRAY_FARRAY);
if (cleanArr == NULL) throw std::bad_alloc();
reqDescr = NULL; // The new reference from DescrFromType was stolen by FromArray
const auto dims = getPyArrayDimensions(pyarr);
outT* dataPtr = static_cast<outT*>(PyArray_DATA(cleanArr));
// this copies the data from cleanArr
arma::Mat<outT> result;
if (dims.size() == 1) {
result = arma::Col<outT>(dataPtr, dims[0], true);
}
else {
result = arma::Mat<outT>(dataPtr, dims[0], dims[1], true);
}
Py_DECREF(cleanArr);
return result;
}
来源：https://stackoverflow.com/questions/35000565/accessing-view-of-a-numpy-array-using-the-c-api