tc_wave_signif (pyleoclim.utils.wavelet.tc_wave_signif)

pyleoclim.utils.wavelet.tc_wave_signif(ys, ts, scale, mother, param, sigtest='chi-square', qs=[0.95], dof=None, gws=None)[source]

Asymptotic singificance testing.

Parameters

ys (numpy.array) – Values for the timeseries
ts (numpy.array) – time vector.
scale (numpy.array) – vector of scale
mother (str) – Type of mother wavelet
param (int) – mother wavelet parameter
sigtest ({'chi-square','time-average','scale-average'}, optional) –
Type of significance test to perform . The default is ‘chi-square’. - chi-square: a regular chi-square test Eq(18) from Torrence and Compo - time-average: 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. Eq23 in Torrence and Compo

-scale-average: 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].
qs (list, optional) – Significance level. The default is [0.95].
dof (None, optional) –
Degrees of freedon for signif test. The default is None, which will automatically assign:
- chi-square: DOF = 2 (or 1 for MOTHER=’DOG’)
- time-average: DOF = NA, the number of times averaged together.
-scale-average: DOF = [S1,S2], the range of scales averaged.
gws (np.array, optional) – Global wavelet spectrum. a vector of the same length as scale. If input then this is used as the theoretical background spectrum, rather than white or red noise. The default is None.

Returns

signif_level – Array of values for significance level

Return type

numpy.array

References

Torrence, C. and G. P. Compo, 1998: A Practical Guide to Wavelet Analysis. Bull. Amer. Meteor. Soc., 79, 61-78. Python routines available at http://paos.colorado.edu/research/wavelets/