R: Continuous Wavelet Transform

cwt {Rwave} | R Documentation

## 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 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)

[Package Rwave version 2.4-5 Index]