WindowFunctions

Motivation

In both digital filter design and spectral estimation, the choice of a windowing function can play an important role in determining the quality of overall results. The main role of the window is to damp out the effects of the Gibbs phenomenon that resulted from truncation of an infinite series.

Methods

Function	Window Type
barthann	Modified Bartlett-Hann
bartlett	Bartlett
blackman	Blackman
blackmanharris	Minimum four-term Blackman-Harris
bohman	Bohman
cheb	Chebyshev
flattop	Flat top weighted
hamming	Hamming
hanning	Hann (Hanning)
nuttall	Nuttall-defined minimum 4-term Blackman-Harris
parzen	Parzen (de la Vallée Poussin)
rectangular	Rectangular
triangular	Triangular
tukey	Tukey (tapered cosine)
taylor	Taylor
gauss	Gaussian
kaiser	Kaiser
enbw	Equivalent noise bandwidth

Installation

To install the released stable version, run

using Pkg
Pkg.add("WindowFunctions")

Example

using Plots
using WindowFunctions

kaiser5(N; dtype::DataType=Float32) = kaiser(N, 5.0, 10, dtype=dtype)

winlist = [barthann,
           bartlett,
           blackman,
           blackmanharris,
           bohman,
           flattop,
           hamming,
           hanning,
           nuttall,
           parzen,
           rectangular,
           triangular,
           gauss,
           tukey,
           kaiser5]

for win in winlist
    plot(win(256, dtype=Float32),
         label=string(win),
         fillrange = 0,
         fillalpha = 0.3,
         show=true)
    ylims!(-0.13,1.18)
    xlims!(1-20,256+20)
    sleep(1)
end