# cwt

##### Continuous Wavelet Transform

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

- Keywords
- ts

##### 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

##### Examples

```
# NOT RUN {
x <- 1:512
chirp <- sin(2*pi * (x + 0.002 * (x-256)^2 ) / 16)
retChirp <- cwt(chirp, noctave=5, nvoice=12)
# }
```

*Documentation reproduced from package Rwave, version 2.4-8, License: GPL (>= 2)*