cwt                  package:Rwave                  R Documentation

_C_o_n_t_i_n_u_o_u_s _W_a_v_e_l_e_t _T_r_a_n_s_f_o_r_m

_D_e_s_c_r_i_p_t_i_o_n:

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

_U_s_a_g_e:

     cwt(input, noctave, nvoice=1, w0=2 * pi, twoD=TRUE, plot=TRUE)

_A_r_g_u_m_e_n_t_s:

   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. 

_D_e_t_a_i_l_s:

     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)

_V_a_l_u_e:

     continuous (complex) wavelet transform

_W_a_r_n_i_n_g:

     Since Morlet's wavelet is not strictly speaking a wavelet (it is
     not of vanishing integral), artifacts may occur for certain
     signals.

_R_e_f_e_r_e_n_c_e_s:

     See discussions in the text of ``Practical Time-Frequency
     Analysis''.

_S_e_e _A_l_s_o:

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

_E_x_a_m_p_l_e_s:

     # 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")

