Histogram

Basic usage

Note

Any of the options [...] can be omitted (though the order must be preserved). The defaults are:

The behavior, in-, and output of xt::histogram() is similar to that of numpy.histogram with that difference that the bin-edges are obtained by a separate function call:

#include <xtensor/xtensor.hpp>
#include <xtensor/xhistogram.hpp>
#include <xtensor/xio.hpp>

int main()
{
    xt::xtensor<double,1> data = {1., 1., 2., 2., 3.};

    xt::xtensor<double,1> count = xt::histogram(data, std::size_t(2));

    xt::xtensor<double,1> bin_edges = xt::histogram_bin_edges(data, std::size_t(2));

    return 0;
}

Bin-edges algorithm

To customize the algorithm to be used to construct the histogram, one needs to make use of the latter xt::histogram_bin_edges(). For example:

#include <xtensor/xtensor.hpp>
#include <xtensor/xhistogram.hpp>
#include <xtensor/xio.hpp>

int main()
{
    xt::xtensor<double,1> data = {1., 1., 2., 2., 3.};

    xt::xtensor<double,1> bin_edges = xt::histogram_bin_edges(data, std::size_t(2), xt::histogram_algorithm::uniform);

    xt::xtensor<double,1> prob = xt::histogram(data, bin_edges, true);

    std::cout << bin_edges << std::endl;
    std::cout << prob << std::endl;

    return 0;
}

The following xt::histogram_algorithm are available:

  • automatic: equivalent to linspace.

  • linspace: linearly spaced bin-edges.

  • logspace: bins that logarithmically increase in size.

  • uniform: bin-edges such that the number of data points is the same in all bins (as much as possible).