histogram/examples/guide_custom_storage.cpp
Hans Dembinski 9abbe46e3d
CI & doc update, removed multiprecision::cpp_int, simpler axis implementation, use_default to set default options
* Travis uses b2 and codecov now
* replacing boost::multiprecision with custom implementation to avoid the dependency
* improved axis implementation: `update` is now a normal method
* introduced use_default tag type to set defaults
* whitespace fixes and doc update
2019-02-17 13:37:50 +01:00

48 lines
1.8 KiB
C++

// Copyright 2015-2018 Hans Dembinski
//
// Distributed under the Boost Software License, Version 1.0.
// (See accompanying file LICENSE_1_0.txt
// or copy at http://www.boost.org/LICENSE_1_0.txt)
//[ guide_custom_storage
#include <algorithm> // std::for_each
#include <array>
#include <boost/histogram.hpp>
#include <boost/histogram/algorithm/sum.hpp>
#include <functional> // std::ref
#include <unordered_map>
#include <vector>
int main() {
using namespace boost::histogram;
const auto axis = axis::regular<>(10, 0.0, 1.0);
auto data = {0.1, 0.3, 0.2, 0.7};
// Create static histogram with vector<int> as counter storage, you can use
// other arithmetic types as counters, e.g. double.
auto h1 = make_histogram_with(std::vector<int>(), axis);
std::for_each(data.begin(), data.end(), std::ref(h1));
assert(algorithm::sum(h1) == 4);
// Create static histogram with array<int, N> as counter storage which is
// allocated completely on the stack (this is very fast). N may be larger than
// the actual number of bins used; an exception is raised if N is too small to
// hold all bins.
auto h2 = make_histogram_with(std::array<int, 12>(), axis);
std::for_each(data.begin(), data.end(), std::ref(h2));
assert(algorithm::sum(h2) == 4);
// Create static histogram with unordered_map as counter storage; this
// generates a sparse histogram where only memory is allocated for bins that
// are non-zero. This sounds like a good idea for high-dimensional histograms,
// but maps come with a memory and run-time overhead. The default_storage
// usually performs better in high dimensions.
auto h3 = make_histogram_with(std::unordered_map<std::size_t, int>(), axis);
std::for_each(data.begin(), data.end(), std::ref(h3));
assert(algorithm::sum(h3) == 4);
}
//]