# Common pitfalls

## xarray initialization

    xt::xarray<double> a({1, 3, 4, 2});

does not initialize a 4D-array, but a 1D-array containing the values `1`, `3`, `4`, and `2`. It is strictly equivalent to

    xt::xarray<double> a = {1, 3, 4, 2};

To initialize a 4D-array with the given shape, use the `from_shape` static method:

    auto a = xt::xarray<double>::from_shape({1, 3, 4, 2});

The confusion often comes from the way `xtensor` can be initialized:

    xt::xtensor<double, 4> a = {1, 3, 4, 2};

In this case, a 4D-tensor with shape `(1, 3, 4, 2)` is initialized.

## Intermediate result

Consider the following function:

    template <class C>
    auto func(const C& c)
    {
        return (1 - func_tmp(c)) / (1 + func_tmp(c));
    }

where `func_tmp` is another unary function accepting an xtensor expression. You may be tempted to simplify it a bit:

    template <class C>
    auto func(const C& c)
    {
        auto tmp = func_tmp(c);
        return (1 - tmp) / (1 + tmp);
    }

Unfortunately, you introduced a bug; indeed, expressions in `xtensor` are not evaluated immediately, they capture their arguments by reference or copy depending on their nature, for future evaluation. Since `tmp` is an lvalue, it is captured by reference in the last statement; when the function returns, `tmp` is destroyed, leading to a dangling reference in the returned expression.

Replacing `auto tmp` with `xt::xarray<double> tmp` does not change anything, `tmp` is still an lvalue and thus captured by reference.

## Random numbers not consistent

Using a random number function from xtensor actually returns a lazy generator. That means, accessing the same element of a random number generator does not give the same random number if called twice.

    auto gen = xt::random::rand<double>({10, 10});
    auto a0 = gen(0, 0);
    auto a1 = gen(0, 0);

    // a0 != a1 !!!

You need to explicitly assign or eval a random number generator, like so:

    xt::xarray<double> xr = xt::random::rand<double>({10, 10});
    auto xr2 = eval(xt::random::rand<double>({10, 10}));

    // now xr(0, 0) == xr(0, 0) is true.

## variance arguments

When `variance` is passed an expression and an integer parameter, this latter is not the axis along which the variance must be computed, but the degree of freedom:

    xt::xtensor<double, 2> a = {{1., 2., 3.}, {4., 5., 6.}};
    std::cout << xt::variance(a, 1) << std::endl;
    // Outputs 3.5

If you want to specify an axis, you need to pass an initializer list:

    xt::xtensor<double, 2> a = {{1., 2., 3.}, {4., 5., 6.}};
    std::cout << xt::variance(a, {1}) << std::endl;
    .. Outputs { 0.666667, 0.666667 }

## fixed_shape on Windows

Builder functions such as `empty` or `ones` accept an initializer list as argument. If the elements of this list do not have the same type, a curious compilation error may occur on Windows:

    size_t N = 10ull;
    xt::xarray<int> ages = xt::empty<int>({N, 4ul});

    // error: cannot convert argument 1 from 'initializer list'
    // to 'const xt::fixed_shape<> &'

To avoid this compiler bug (for which we don’t have a workaround), ensure all the elements in the initializer list have the same type.

## Alignment of fixed-size members

Note

If you are using `C++ >= 17` you should not have to worry about this.

When building with `xsimd` (see External dependencies), if you define a structure having members of fixed-size xtensor types, you must ensure that the buffers properly aligned. For this you can use the macro `XTENSOR_FIXED_ALIGN` available in `xtensor/xtensor_config.hpp`. Consider the following example:

    template <typename T>
    class alignas(XTENSOR_FIXED_ALIGN) Foo
    {
    public:

        using allocator_type = std::conditional_t<XTENSOR_FIXED_ALIGN != 0,
                                                  xt_simd::aligned_allocator<T, XTENSOR_FIXED_ALIGN>,
                                                  std::allocator<T>>;

        Foo(T fac) : m_fac(fac)
        {
            m_bar.fill(fac);
        }

        auto get() const
        {
            return m_bar;
        }

    private:

        xt::xtensor_fixed<T, xt::xshape<10, 10>> m_bar;
        T m_fac;
    };

Whereby it is important to store the fixed-sized xtensor type (in this case `xt::xtensor_fixed<T, xt::xshape<10, 10>>`) as first member.