cwtp: Continuous Wavelet Transform with Phase Derivative

In Rwave: Time-Frequency analysis of 1-D signals

Description

Computes the continuous wavelet transform with (complex-valued) Morlet wavelet and its phase derivative.

Usage

cwtp(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 TRUE, display the modulus of the continuous wavelet transform on the graphic device.

Value

list containing the continuous (complex) wavelet transform and the phase derivative

wt	array of complex numbers for the values of the continuous wavelet transform.
f	array of the same dimensions containing the values of the derivative of the phase of the continuous wavelet transform.

References

See discussions in the text of “Practical Time-Frequency Analysis”.

See Also

cgt, cwt, cwtTh, DOG for wavelet transform, and gabor for continuous Gabor transform.

Examples

    ## discards imaginary part with error,
    ## c code does not account for Im(input)
    x <- 1:512
    chirp <- sin(2*pi * (x + 0.002 * (x-256)^2 ) / 16)
    chirp <- chirp + 1i * sin(2*pi * (x + 0.004 * (x-256)^2 ) / 16)
    retChirp <- cwtp(chirp, noctave=5, nvoice=12)

Example output

Warning message:
In cwtp(chirp, noctave = 5, nvoice = 12) :
  imaginary parts discarded in coercion

Rwave documentation built on May 2, 2019, 5:48 p.m.