Rwave (version 1.2)

cwt: 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.

Value

• continuous (complex) wavelet transform

Details

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.

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)