Not clear how to expose existing C++ vector as numpy array · pybind/pybind11#1042

(37 留言) (27 反應) (0 負責人)C++ (14,677 star) (2,005 fork)batch import

enhancementhelp wanted

描述

This is a question of documentation rather than an issue. I can't find any example of the following very common scenario:

std::vector<int> some_func();
...
// We want to expose returned std::vector as a numpy array without copying
m.def("some_func", []() -> py::array {
   auto data = some_func();
   // What to do with data?? Map it with Eigen (then return what?), wrap somehow with py::buffer (how?)
})

I don't know the answer. It would be very nice to have this explained in docs since this scenario if rather common.

貢獻者指南

技術棧: pythoncpp
領域: documentationapi
議題類型: documentation
難度: 3
預計時間: 1-3 hours
活動狀態: stale
清晰度: clear
前置要求: Basic C++ and pybind11numpy basics
新手友善度: 60
研究方向: Review the existing pybind11 documentation for numpy integration and check comments in the issue for any progress or workarounds. Look at the pybind11 source code for py::array and buffer protocol examples. The goal is to write a clear example showing how to create a py::array from an existing std::vector without copying, using the buffer info or direct memory mapping. Address the common pattern in the issue body and provide a minimal working example.