using System;

namespace NumSharp

{

public static partial class np

{

/// <summary>

/// Stack arrays in sequence horizontally (column wise).

/// This is equivalent to concatenation along the second axis, except for 1-D arrays where it concatenates along the first axis.Rebuilds arrays divided by hsplit.

/// This function makes most sense for arrays with up to 3 dimensions.For instance, for pixel-data with a height(first axis), width(second axis),

/// and r/g/b channels(third axis). The functions concatenate, stack and block provide more general stacking and concatenation operations.

/// </summary>

/// <param name="tup">The arrays must have the same shape along all but the second axis, except 1-D arrays which can be any length.</param>

/// <returns>The array formed by stacking the given arrays.</returns>

/// <remarks>https://docs.scipy.org/doc/numpy/reference/generated/numpy.hstack.html</remarks>

public static NDArray hstack(params NDArray[] tup)

{

if (tup == null)

throw new ArgumentNullException(nameof(tup));

if (tup.Length == 0)

throw new ArgumentException("Value cannot be an empty collection.", nameof(tup));

tup = np.atleast_1d(tup); //handle scalars

if (tup[0].shape.Length == 1)

return np.concatenate(tup, 0);

else

return np.concatenate(tup, 1);

}

}

}