// Copyright (c) 2020 by Meinrad Recheis (Member of SciSharp)

// Code generated by CodeMinion: https://github.com/SciSharp/CodeMinion

using System;

using System.Collections;

using System.Collections.Generic;

using System.IO;

using System.Linq;

using System.Runtime.InteropServices;

using System.Text;

using Python.Runtime;

using Numpy.Models;

#if PYTHON_INCLUDED

using Python.Included;

#endif

namespace Numpy

{

public static partial class np

{

/// <summary>

/// Pads an array.<br></br>

///

/// Notes

///

/// For an array with rank greater than 1, some of the padding of later

/// axes is calculated from padding of previous axes.<br></br>

/// This is easiest to

/// think about with a rank 2 array where the corners of the padded array

/// are calculated by using padded values from the first axis.<br></br>

///

/// The padding function, if used, should return a rank 1 array equal in

/// length to the vector argument with padded values replaced.<br></br>

/// It has the

/// following signature:

///

/// where

/// </summary>

/// <param name="array">

/// Input array

/// </param>

/// <param name="pad_width">

/// Number of values padded to the edges of each axis.<br></br>

///

/// ((before_1, after_1), … (before_N, after_N)) unique pad widths

/// for each axis.<br></br>

///

/// ((before, after),) yields same before and after pad for each axis.<br></br>

///

/// (pad,) or int is a shortcut for before = after = pad width for all

/// axes.

/// </param>

/// <param name="mode">

/// One of the following string values or a user supplied function.

/// </param>

/// <param name="stat_length">

/// Used in ‘maximum’, ‘mean’, ‘median’, and ‘minimum’. Number of

/// values at edge of each axis used to calculate the statistic value.<br></br>

///

/// ((before_1, after_1), … (before_N, after_N)) unique statistic

/// lengths for each axis.<br></br>

///

/// ((before, after),) yields same before and after statistic lengths

/// for each axis.<br></br>

///

/// (stat_length,) or int is a shortcut for before = after = statistic

/// length for all axes.<br></br>

///

/// Default is None, to use the entire axis.

/// </param>

/// <param name="constant_values">

/// Used in ‘constant’. The values to set the padded values for each

/// axis.<br></br>

///

/// ((before_1, after_1), … (before_N, after_N)) unique pad constants

/// for each axis.<br></br>

///

/// ((before, after),) yields same before and after constants for each

/// axis.<br></br>

///

/// (constant,) or int is a shortcut for before = after = constant for

/// all axes.<br></br>

///

/// Default is 0.

/// </param>

/// <param name="end_values">

/// Used in ‘linear_ramp’. The values used for the ending value of the

/// linear_ramp and that will form the edge of the padded array.<br></br>

///

/// ((before_1, after_1), … (before_N, after_N)) unique end values

/// for each axis.<br></br>

///

/// ((before, after),) yields same before and after end values for each

/// axis.<br></br>

///

/// (constant,) or int is a shortcut for before = after = end value for

/// all axes.<br></br>

///

/// Default is 0.

/// </param>

/// <param name="reflect_type">

/// Used in ‘reflect’, and ‘symmetric’. The ‘even’ style is the

/// default with an unaltered reflection around the edge value.<br></br>

/// For

/// the ‘odd’ style, the extended part of the array is created by

/// subtracting the reflected values from two times the edge value.

/// </param>

/// <returns>

/// Padded array of rank equal to array with shape increased

/// according to pad_width.

/// </returns>

public static NDarray pad(NDarray array, NDarray pad_width, string mode, int[] stat_length = null, int[] constant_values = null, int[] end_values = null, string reflect_type = null)

{

//auto-generated code, do not change

var __self__=self;

var pyargs=ToTuple(new object[]

{

array,

pad_width,

mode,

});

var kwargs=new PyDict();

if (stat_length!=null) kwargs["stat_length"]=ToPython(stat_length);

if (constant_values!=null) kwargs["constant_values"]=ToPython(constant_values);

if (end_values!=null) kwargs["end_values"]=ToPython(end_values);

if (reflect_type!=null) kwargs["reflect_type"]=ToPython(reflect_type);

dynamic py = __self__.InvokeMethod("pad", pyargs, kwargs);

return ToCsharp<NDarray>(py);

}

}

}