Histogram¶
Basic usage¶
Note
xt::histogram(a, bins[, weights][, density])
xt::histogram_bin_edges(a[, weights][, left, right][, bins][, mode])
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 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 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 algorithms are available:
automatic
: equivalent tolinspace
.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).