# cwt: Continuous Wavelet Transform In Rwave: Time-Frequency Analysis of 1-D Signals

## Description

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

## Usage

 `1` ```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”.

`cwtp`, `cwtTh`, `DOG`, `gabor`.

## Examples

 ```1 2 3``` ``` x <- 1:512 chirp <- sin(2*pi * (x + 0.002 * (x-256)^2 ) / 16) retChirp <- cwt(chirp, noctave=5, nvoice=12) ```

### Example output ```
```

Rwave documentation built on Sept. 30, 2021, 1:07 a.m.