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// Copyright (c) 2019 by the SciSharp Team
// 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 Python.Included;
using Numpy.Models;
namespace Numpy
{
public static partial class np
{
/// <summary>
/// Return the Bartlett window.
///
/// The Bartlett window is very similar to a triangular window, except
/// that the end points are at zero. It is often used in signal
/// processing for tapering a signal, without generating too much
/// ripple in the frequency domain.
///
/// Notes
///
/// The Bartlett window is defined as
///
/// Most references to the Bartlett window come from the signal
/// processing literature, where it is used as one of many windowing
/// functions for smoothing values. Note that convolution with this
/// window produces linear interpolation. It is also known as an
/// apodization (which means”removing the foot”, i.e. smoothing
/// discontinuities at the beginning and end of the sampled signal) or
/// tapering function. The fourier transform of the Bartlett is the product
/// of two sinc functions.
/// Note the excellent discussion in Kanasewich.
///
/// References
/// </summary>
/// <param name="M">
/// Number of points in the output window. If zero or less, an
/// empty array is returned.
/// </param>
/// <returns>
/// The triangular window, with the maximum value normalized to one
/// (the value one appears only if the number of samples is odd), with
/// the first and last samples equal to zero.
/// </returns>
public static NDarray bartlett(int M)
=> NumPy.Instance.bartlett(M);
/// <summary>
/// Return the Blackman window.
///
/// The Blackman window is a taper formed by using the first three
/// terms of a summation of cosines. It was designed to have close to the
/// minimal leakage possible. It is close to optimal, only slightly worse
/// than a Kaiser window.
///
/// Notes
///
/// The Blackman window is defined as
///
/// Most references to the Blackman window come from the signal processing
/// literature, where it is used as one of many windowing functions for
/// smoothing values. It is also known as an apodization (which means
/// “removing the foot”, i.e. smoothing discontinuities at the beginning
/// and end of the sampled signal) or tapering function. It is known as a
/// “near optimal” tapering function, almost as good (by some measures)
/// as the kaiser window.
///
/// References
///
/// Blackman, R.B. and Tukey, J.W., (1958) The measurement of power spectra,
/// Dover Publications, New York.
///
/// Oppenheim, A.V., and R.W. Schafer. Discrete-Time Signal Processing.
/// Upper Saddle River, NJ: Prentice-Hall, 1999, pp. 468-471.
/// </summary>
/// <param name="M">
/// Number of points in the output window. If zero or less, an empty
/// array is returned.
/// </param>
/// <returns>
/// The window, with the maximum value normalized to one (the value one
/// appears only if the number of samples is odd).
/// </returns>
public static NDarray blackman(int M)
=> NumPy.Instance.blackman(M);
/// <summary>
/// Return the Hamming window.
///
/// The Hamming window is a taper formed by using a weighted cosine.
///
/// Notes
///
/// The Hamming window is defined as
///
/// The Hamming was named for R. W. Hamming, an associate of J. W. Tukey
/// and is described in Blackman and Tukey. It was recommended for
/// smoothing the truncated autocovariance function in the time domain.
/// Most references to the Hamming window come from the signal processing
/// literature, where it is used as one of many windowing functions for
/// smoothing values. It is also known as an apodization (which means
/// “removing the foot”, i.e. smoothing discontinuities at the beginning
/// and end of the sampled signal) or tapering function.
///
/// References
/// </summary>
/// <param name="M">
/// Number of points in the output window. If zero or less, an
/// empty array is returned.
/// </param>
/// <returns>
/// The window, with the maximum value normalized to one (the value
/// one appears only if the number of samples is odd).
/// </returns>
public static NDarray hamming(int M)
=> NumPy.Instance.hamming(M);
/// <summary>
/// Return the Hanning window.
///
/// The Hanning window is a taper formed by using a weighted cosine.
///
/// Notes
///
/// The Hanning window is defined as
///
/// The Hanning was named for Julius von Hann, an Austrian meteorologist.
/// It is also known as the Cosine Bell. Some authors prefer that it be
/// called a Hann window, to help avoid confusion with the very similar
/// Hamming window.
///
/// Most references to the Hanning window come from the signal processing
/// literature, where it is used as one of many windowing functions for
/// smoothing values. It is also known as an apodization (which means
/// “removing the foot”, i.e. smoothing discontinuities at the beginning
/// and end of the sampled signal) or tapering function.
///
/// References
/// </summary>
/// <param name="M">
/// Number of points in the output window. If zero or less, an
/// empty array is returned.
/// </param>
/// <returns>
/// The window, with the maximum value normalized to one (the value
/// one appears only if M is odd).
/// </returns>
public static NDarray hanning(int M)
=> NumPy.Instance.hanning(M);
/// <summary>
/// Return the Kaiser window.
///
/// The Kaiser window is a taper formed by using a Bessel function.
///
/// Notes
///
/// The Kaiser window is defined as
///
/// with
///
/// where is the modified zeroth-order Bessel function.
///
/// The Kaiser was named for Jim Kaiser, who discovered a simple
/// approximation to the DPSS window based on Bessel functions. The Kaiser
/// window is a very good approximation to the Digital Prolate Spheroidal
/// Sequence, or Slepian window, which is the transform which maximizes the
/// energy in the main lobe of the window relative to total energy.
///
/// The Kaiser can approximate many other windows by varying the beta
/// parameter.
///
/// A beta value of 14 is probably a good starting point. Note that as beta
/// gets large, the window narrows, and so the number of samples needs to be
/// large enough to sample the increasingly narrow spike, otherwise NaNs will
/// get returned.
///
/// Most references to the Kaiser window come from the signal processing
/// literature, where it is used as one of many windowing functions for
/// smoothing values. It is also known as an apodization (which means
/// “removing the foot”, i.e. smoothing discontinuities at the beginning
/// and end of the sampled signal) or tapering function.
///
/// References
/// </summary>
/// <param name="M">
/// Number of points in the output window. If zero or less, an
/// empty array is returned.
/// </param>
/// <param name="beta">
/// Shape parameter for window.
/// </param>
/// <returns>
/// The window, with the maximum value normalized to one (the value
/// one appears only if the number of samples is odd).
/// </returns>
public static NDarray kaiser(int M, float beta)
=> NumPy.Instance.kaiser(M, beta);
}
}