prepare_wwz (pyleoclim.utils.wavelet.prepare_wwz)

pyleoclim.utils.wavelet.prepare_wwz(ys, ts, freq=None, freq_method='log', freq_kwargs=None, tau=None, len_bd=0, bc_mode='reflect', reflect_type='odd', **kwargs)[source]

Return the truncated time series with NaNs deleted and estimate frequency vector and tau

Parameters

  • ys (array) – a time series, NaNs will be deleted automatically

  • ts (array) – the time points, if ys contains any NaNs, some of the time points will be deleted accordingly

  • freq (array) – vector of frequency. If None, will be ganerated according to freq_method. may be set.

  • freq_method (str) – when freq=None, freq will be ganerated according to freq_method

  • freq_kwargs (str) – used when freq=None for certain methods

  • tau (array) – The evenly-spaced time points, namely the time shift for wavelet analysis. If the boundaries of tau are not exactly on two of the time axis points, then tau will be adjusted to be so. If None, at most 50 tau points will be generated from the input time span.

  • len_bd (int) – the number of the ghost grids want to create on each boundary

  • bc_mode (string) – {‘constant’, ‘edge’, ‘linear_ramp’, ‘maximum’, ‘mean’, ‘median’, ‘minimum’, ‘reflect’ , ‘symmetric’, ‘wrap’} For more details, see np.lib.pad()

  • reflect_type (string) – {‘even’, ‘odd’}, optional Used in ‘reflect’, and ‘symmetric’. The ‘even’ style is the default with an unaltered reflection around the edge value. For the ‘odd’ style, the extented part of the array is created by subtracting the reflected values from two times the edge value. For more details, see np.lib.pad()

Returns

  • ys_cut (array) – the truncated time series with NaNs deleted

  • ts_cut (array) – the truncated time axis of the original time series with NaNs deleted

  • freq (array) – vector of frequency

  • tau (array) – the evenly-spaced time points, namely the time shift for wavelet analysis