cwt: Continuous Wavelet Transform

In Rwave: Time-Frequency Analysis of 1-D Signals

Continuous Wavelet Transform

Description

Computes the continuous wavelet transform with for the (complex-valued) Morlet wavelet.

Usage

cwt(input, noctave, nvoice=1, w0=2 * pi, twoD=TRUE, plot=TRUE)

Arguments

input	input signal (possibly complex-valued)
noctave	number of powers of 2 for the scale variable
nvoice	number of scales in each octave (i.e. between two consecutive powers of 2).
w0	central frequency of the wavelet.
twoD	logical variable set to T to organize the output as a 2D array (signal_size x nb_scales), otherwise, the output is a 3D array (signal_size x noctave x nvoice).
plot	if set to T, display the modulus of the continuous wavelet transform on the graphic device.

Details

The time series is padded with zeroes to avoid problems with circular versus linear convolution. This does not affect usage, as the matrix returned has the added columns removed. (JML Sep 29, 2021).

The output contains the (complex) values of the wavelet transform of the input signal. The format of the output can be

2D array (signal_size x nb_scales)

3D array (signal_size x noctave x nvoice)

Since Morlet's wavelet is not strictly speaking a wavelet (it is not of vanishing integral), artifacts may occur for certain signals.

Value

continuous (complex) wavelet transform

References

See discussions in the text of “Practical Time-Frequency Analysis”.

See Also

cwtp, cwtTh, DOG, gabor.

Examples

    x <- 1:512
    chirp <- sin(2*pi * (x + 0.002 * (x-256)^2 ) / 16)
    retChirp <- cwt(chirp, noctave=5, nvoice=12)

Rwave documentation built on Oct. 22, 2022, 1:05 a.m.