Histogram

Basic usage

xt::histogram(a, bins[, weights][, density])
xt::histogram_bin_edges(a[, weights][, left, right][, bins][, mode])

Note

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

weights = xt::ones(data.shape())
density = false
left = xt::amin(data)(0)
right = Xt::amax(data)(0)
bins = 10
mode = xt::histogram::automatic

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/containers/xtensor.hpp>
#include <xtensor/misc/xhistogram.hpp>
#include <xtensor/io/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/containers/xtensor.hpp>
#include <xtensor/misc/xhistogram.hpp>
#include <xtensor/io/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).