[section Tutorial]

[section Example 1: 1d-histogram in C++]

How to make a 1d-histogram in C++ and to fill it:

[c++]
```
    #include <boost/histogram.hpp>
    #include <iostream>
    #include <cmath>

    int main(int, char**) {
        namespace bh = boost::histogram;

        // create 1d-histogram with 10 equidistant bins from -1.0 to 2.0,
        // with axis of histogram labeled as "x"
        auto h = bh::make_static_histogram(bh::regular_axis(10, -1.0, 2.0, "x"));

        // fill histogram with data
        h.fill(-1.5); // put in underflow bin
        h.fill(-1.0); // included in first bin, bin interval is semi-open
        h.fill(-0.5);
        h.fill(1.1);
        h.fill(0.3);
        h.fill(1.7);
        h.fill(2.0);  // put in overflow bin, bin interval is semi-open
        h.fill(20.0); // put in overflow bin
        h.wfill(5.0, 0.1); // fill with a weighted entry, weight is 5.0

        // access histogram counts, loop includes under- and overflow bin
        const auto& a = h.axis<0>();
        for (int i = -1, n = bh::bins(a) + 1; i < n; ++i) {
            std::cout << "bin " << i
                      << " x in [" << bh::left(a, i) << ", " << bh::right(a, i) << "): "
                      << h.value(i) << " +/- " << std::sqrt(h.variance(i))
                      << std::endl;
        }

        /* program output:

        bin -1 x in [-inf, -1): 1 +/- 1
        bin 0 x in [-1, -0.7): 1 +/- 1
        bin 1 x in [-0.7, -0.4): 1 +/- 1
        bin 2 x in [-0.4, -0.1): 0 +/- 0
        bin 3 x in [-0.1, 0.2): 5 +/- 5
        bin 4 x in [0.2, 0.5): 1 +/- 1
        bin 5 x in [0.5, 0.8): 0 +/- 0
        bin 6 x in [0.8, 1.1): 0 +/- 0
        bin 7 x in [1.1, 1.4): 1 +/- 1
        bin 8 x in [1.4, 1.7): 0 +/- 0
        bin 9 x in [1.7, 2): 1 +/- 1
        bin 10 x in [2, inf): 2 +/- 1.41421
        */
    }
```

Example 2: Fill a 2d-histogram in Python with data in Numpy arrays

[python]
```
    import histogram as bh
    import numpy as np

    # create 2d-histogram over polar coordinates, with
    # 10 equidistant bins in radius from 0 to 5 and
    # 4 equidistant bins in polar angle
    h = bh.histogram(bh.regular_axis(10, 0.0, 5.0, "radius",
                                     uoflow=False),
                     bh.polar_axis(4, 0.0, "phi"))

    # generate some numpy arrays with data to fill into histogram,
    # in this case normal distributed random numbers in x and y,
    # converted into polar coordinates
    x = np.random.randn(1000)             # generate x
    y = np.random.randn(1000)             # generate y
    rphi = np.empty((1000, 2))
    rphi[:, 0] = (x ** 2 + y ** 2) ** 0.5 # compute radius
    rphi[:, 1] = np.arctan2(y, x)         # compute phi

    # fill histogram with numpy array
    h.fill(rphi)

    # access histogram counts (no copy)
    count_matrix = np.asarray(h)

    print count_matrix

    # program output:
    #
    # [[37 26 33 37]
    #  [60 69 76 62]
    #  [48 80 80 77]
    #  [38 49 45 49]
    #  [22 24 20 23]
    #  [ 7  9  9  8]
    #  [ 3  2  3  3]
    #  [ 0  0  0  0]
    #  [ 0  1  0  0]
    #  [ 0  0  0  0]]
```

[endsect]

[endsect]