using System;

using System.Collections.Generic;

using System.Linq;

using System.Runtime.InteropServices;

using System.Text;

using Numpy;

using Numpy.Models;

using Python.Runtime;

namespace Numpy

{

/// <summary>

/// Manual type conversions

/// </summary>

public partial class NumPy

{

/// <summary>

/// Return a new array with the specified shape.

///

/// If the new array is larger than the original array, then the new

/// array is filled with repeated copies of a. Note that this behavior

/// is different from a.resize(new_shape) which fills with zeros instead

/// of repeated copies of a.

///

/// Notes

///

/// Warning: This functionality does not consider axes separately,

/// i.e. it does not apply interpolation/extrapolation.

/// It fills the return array with the required number of elements, taken

/// from a as they are laid out in memory, disregarding strides and axes.

/// (This is in case the new shape is smaller. For larger, see above.)

/// This functionality is therefore not suitable to resize images,

/// or data where each axis represents a separate and distinct entity.

/// </summary>

/// <param name="a">

/// Array to be resized.

/// </param>

/// <param name="new_shape">

/// Shape of resized array.

/// </param>

/// <returns>

/// The new array is formed from the data in the old array, repeated

/// if necessary to fill out the required number of elements. The

/// data are repeated in the order that they are stored in memory.

/// </returns>

public NDarray resize(NDarray a, Shape new_shape)

{

var pyargs = ToTuple(new object[]

{

a,

new_shape,

});

dynamic py = self.InvokeMethod("resize", pyargs);

return ToCsharp<NDarray>(py);

}

}

}