cwt {Rwave} | R Documentation |
Computes the continuous wavelet transform with for the (complex-valued) Morlet wavelet.
cwt(input, noctave, nvoice=1, w0=2 * pi, twoD=TRUE, plot=TRUE)
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.
|
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)
continuous (complex) wavelet transform
Since Morlet's wavelet is not strictly speaking a wavelet (it is not of vanishing integral), artifacts may occur for certain signals.
See discussions in the text of ``Practical Time-Frequency Analysis''.
# Compute the wavelet transform of a white noise signal. gnoise <- rnorm(512) par(mfrow=c(3,1)) plot.ts(gnoise) title("White Gaussian noise") cwtgnoise <- cwt(gnoise, 5, 12) image(Arg(cwtgnoise)) title("Phase of wavelet transform")