gpt4 book ai didi

c++ - 如何在 boost multi_array 容器中存储值?

转载 作者:行者123 更新时间:2023-12-04 13:05:22 25 4
gpt4 key购买 nike

我正在努力访问这些值并将它们存储在 boost multi_array 容器中。我尝试使用索引方法([] 和 .at())访问元素,但抛出 错误:没有用于调用“boost::multi_array ::data(int)”的匹配函数 ,但是我可以打印数据(请参阅代码)但不知道如何存储它并再次访问它以进行进一步的计算。数据是二维的(11214, 3),但与此同时,我只想将其展平并具有一系列值。所以我的问题是如何访问元素以及如何将它们存储在容器中?

#include <boost/multi_array.hpp>
#include <boost/timer/timer.hpp>
#include <boost/range/irange.hpp>
#include <h5xx/h5xx.hpp>
#include <iostream>
#include <vector>
#include <algorithm>
#include <iterator>
#include <string>

using array_2d_t = boost::multi_array<float, 2>;
//using array_2d_t = boost::multi_array<float, 3>;
template <typename T>
void print_array(T const& array)
{
for (auto const& row : array)
{ for (auto v : row)
printf("%10f ", v);
printf("\n"); //prints a new line similar t0 \n
}
}

h5xx::dataset open_dataset(std::string const& filename) {
h5xx::file xaa(filename, h5xx::file::mode::in);
h5xx::group g(xaa, "particles/lipids/box/positions");
return h5xx::dataset(g, "value");
}

std::vector<float> cell_from_all_frames(h5xx::dataset& ds, size_t row, size_t col) {
// determine dataset shape: frames, particle count, space dimension
auto ds_shape = h5xx::dataspace(ds).extents<3>();
std::vector<float> cells(ds_shape[0]); // number of frames

std::vector<hsize_t> offsets{0, row, col};
std::vector<hsize_t> counts{ds_shape[0], 1, 1};
h5xx::slice slice(offsets, counts);

h5xx::read_dataset(ds, cells, slice);
return cells;
}

array_2d_t read_frame(std::string const& filename, unsigned frame_no) {
//h5xx::file xaa("../../data/xaa.h5", h5xx::file::mode::in);
h5xx::file xaa(filename, h5xx::file::mode::in);

h5xx::group g(xaa, "particles/lipids/box/positions");
h5xx::dataset ds(g, "value");

// determine dataset shape: frames, particle count, space dimension
auto ds_shape = h5xx::dataspace(ds).extents<3>();
array_2d_t arr(boost::extents[ds_shape[1]][ds_shape[2]]);

std::vector<hsize_t> offsets{frame_no, 0, 0};
std::vector<hsize_t> counts{1, arr.shape()[0], arr.shape()[1]};
h5xx::slice slice(offsets, counts);

h5xx::read_dataset(ds, arr, slice);
return arr;
}

int main(int argc, char const* argv[])
{
if (argc < 2) {
std::cout << "Usage: " << argv[0] << " input.h5" << std::endl;
return -1;
}

auto ds = open_dataset(argv[1]);
std::vector<float> first_cells = cell_from_all_frames(ds, 0, 0);

// set up multi-tau correlator for the computation of time correlation functions
size_t nsamples = 10; // FIXME obtain these parameters from HDF5 file. These 10 elements would be first element of first row, first element of 11214 row, first element of 11214*2 row, first element of 11214*3 row ,..., first element of 11214*10 row.
return 0;
}
在 main() 中,我从函数 read_frame 读取数据并尝试将其传递给 nsamples。我尝试了几件事,但没有用!!

最佳答案

你可以选择。
储存吗?

array_2d_t  frame = read_frame(filename, 1);
访问元素?
// access individual elements:
float ele = frame[0][3];

// or with index list:
std::array<int, 2> indices{0,3};
ele = frame(indices);
或者,如您所愿,提供数组的平面 View :
boost::multi_array_ref<float, 1> sequence(frame.origin(), boost::extents[frame.num_elements()]);
fmt::print("Sum of all {} elements: {}\n",
sequence.size(),
std::accumulate(sequence.begin(), sequence.end(), 0.f));
事实上,你可能会就地 reshape ,但是你不能改变维度,所以你得到 1 个“行”的所有单元格:
frame.reshape(std::array<size_t, 2> {1, frame.num_elements()});
// now the first "row" is the full sequence:
auto&& sequence = frame[0];
fmt::print("Sum of all {} elements: {}\n",
sequence.size(),
std::accumulate(sequence.begin(), sequence.end(), 0.f));
有很多选项可用于带或不带步幅的切片/重新索引,但我 refer to the Boost documentation防止在这里不必要地复杂化。
现场演示
#include <boost/multi_array.hpp>
#include <fmt/ranges.h>
#include <h5xx/h5xx.hpp>
#include <iostream>
#include <iterator>

using array_2d_t = boost::multi_array<float, 2>;

h5xx::dataset open_dataset(std::string const& filename) {
h5xx::file xaa(filename, h5xx::file::mode::in);
h5xx::group g(xaa, "particles/lipids/box/positions");
return h5xx::dataset(g, "value");
}

array_2d_t read_frame(h5xx::dataset& ds, unsigned frame_no) {
auto ds_shape = h5xx::dataspace(ds).extents<3>();
array_2d_t arr(boost::extents[ds_shape[1]][ds_shape[2]]);

std::vector<hsize_t> offsets{frame_no, 0, 0};
std::vector<hsize_t> counts{1, arr.shape()[0], arr.shape()[1]};
h5xx::slice slice(offsets, counts);

h5xx::read_dataset(ds, arr, slice);
return arr;
}

std::vector<float> cell_from_all_frames(h5xx::dataset& ds, size_t row, size_t col) {
// determine dataset shape: frames, particle count, space dimension
auto ds_shape = h5xx::dataspace(ds).extents<3>();
std::vector<float> cells(ds_shape[0]); // number of frames

std::vector<hsize_t> offsets{0, row, col};
std::vector<hsize_t> counts{ds_shape[0], 1, 1};
h5xx::slice slice(offsets, counts);

h5xx::read_dataset(ds, cells, slice);
return cells;
}

int main(int argc, char const* argv[])
{
if (argc < 2) {
std::cout << "Usage: " << argv[0] << " input.h5" << std::endl;
return -1;
}

auto ds = open_dataset(argv[1]);
array_2d_t frame = read_frame(ds, 1);

// access individual elements:
[[maybe_unused]] float ele = frame[0][2];

// or with index list:
std::array<int, 2> indices{0,2};
ele = frame(indices);

{
boost::multi_array_ref<float, 1> sequence(frame.origin(), boost::extents[frame.num_elements()]);
fmt::print("Sum of all {} elements: {}\n",
sequence.size(),
std::accumulate(sequence.begin(), sequence.end(), 0.f));
}

{
// in fact yuou might reshape in-place, but then you cannot change dimensiaonality
frame.reshape(std::array<size_t, 2> {1, frame.num_elements()});
// now the first "row" is the full sequence:
auto&& sequence = frame[0];
fmt::print("Sum of all {} elements: {}\n",
sequence.size(),
std::accumulate(sequence.begin(), sequence.end(), 0.f));
}

{
std::vector<float> first_cells = cell_from_all_frames(ds, 0, 0);

fmt::print("Sum of all {} first cells: {}\n",
first_cells.size(),
std::accumulate(first_cells.begin(), first_cells.end(), 0.f));
}
}
使用您不久前的 xaa.h5:
Sum of all 33642 elements: 737589.1
Sum of all 33642 elements: 737589.1
Sum of all 75 first cells: 6053.3496

关于c++ - 如何在 boost multi_array 容器中存储值?,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/69780791/

25 4 0
Copyright 2021 - 2024 cfsdn All Rights Reserved 蜀ICP备2022000587号
广告合作:1813099741@qq.com 6ren.com