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 static partial class np

{

/// <summary>

/// Gives a new shape to an array without changing its data.

///

/// Notes

///

/// It is not always possible to change the shape of an array without

/// copying the data. If you want an error to be raised when the data is copied,

/// you should assign the new shape to the shape attribute of the array:

///

/// The order keyword gives the index ordering both for fetching the values

/// from a, and then placing the values into the output array.

/// For example, let’s say you have an array:

///

/// You can think of reshaping as first raveling the array (using the given

/// index order), then inserting the elements from the raveled array into the

/// new array using the same kind of index ordering as was used for the

/// raveling.

/// </summary>

/// <param name="a">The array to reshape</param>

/// <param name="newshape">

/// The new shape should be compatible with the original shape. If

/// an integer, then the result will be a 1-D array of that length.

/// One shape dimension can be -1. In this case, the value is

/// inferred from the length of the array and remaining dimensions.

/// </param>

/// <returns>

/// This will be a new view object if possible; otherwise, it will

/// be a copy. Note there is no guarantee of the memory layout (C- or

/// Fortran- contiguous) of the returned array.

/// </returns>

public static NDarray reshape(NDarray a, params int[] newshape)

{

return NumPy.Instance.reshape(a, new Shape(newshape));

}

}

}