wave_signif

seas.waveletFunctions.wave_signif(Y: numpy.ndarray, dt: int, scale: int, sigtest: int = - 1, lag1: int = - 1, siglvl: float = - 1, dof: int = - 1, mother: str = - 1, param: int = - 1)

WAVE_SIGNIF Significance testing for the 1D Wavelet transform WAVELET

SIGNIF = wave_signif(Y,DT,SCALE,SIGTEST,LAG1,SIGLVL,DOF,MOTHER,PARAM)

INPUTS:

Y = the time series, or, the VARIANCE of the time series.

(If this is a single number, it is assumed to be the variance…)

DT = amount of time between each Y value, i.e. the sampling time. SCALE = the vector of scale indices, from previous call to WAVELET.

OUTPUTS:

SIGNIF = significance levels as a function of SCALE FFT_THEOR = output theoretical red-noise spectrum as fn of PERIOD

OPTIONAL INPUTS: * Note * setting any of the following to -1 will cause the default

value to be used.

SIGTEST = 0, 1, or 2. If omitted, then assume 0.

If 0 (the default), then just do a regular chi-square test,

i.e. Eqn (18) from Torrence & Compo.

If 1, then do a “time-average” test, i.e. Eqn (23).

In this case, DOF should be set to NA, the number of local wavelet spectra that were averaged together. For the Global Wavelet Spectrum, this would be NA=N, where N is the number of points in your time series.

If 2, then do a “scale-average” test, i.e. Eqns (25)-(28).

In this case, DOF should be set to a two-element vector [S1,S2], which gives the scale range that was averaged together. e.g. if one scale-averaged scales between 2 and 8, then DOF=[2,8].

LAG1 = LAG 1 Autocorrelation, used for SIGNIF levels. Default is 0.0

SIGLVL = significance level to use. Default is 0.95

DOF = degrees-of-freedom for signif test.

IF SIGTEST=0, then (automatically) DOF = 2 (or 1 for MOTHER=’DOG’) IF SIGTEST=1, then DOF = NA, the number of times averaged together. IF SIGTEST=2, then DOF = [S1,S2], the range of scales averaged.

Note: IF SIGTEST=1, then DOF can be a vector (same length as SCALEs),

in which case NA is assumed to vary with SCALE. This allows one to average different numbers of times together at different scales, or to take into account things like the Cone of Influence. See discussion following Eqn (23) in Torrence & Compo.