mtm (pyleoclim.utils.spectral.mtm)¶
- pyleoclim.utils.spectral.mtm(ys, ts, NW=None, BW=None, detrend=None, sg_kwargs=None, gaussianize=False, standardize=False, adaptive=False, jackknife=True, low_bias=True, sides='default', nfft=None)[source]¶
Retuns spectral density using a multi-taper method.
Based on the function in the time series analysis for neuroscience toolbox: http://nipy.org/nitime/api/generated/nitime.algorithms.spectral.html
- Parameters
ys (array) – a time series
ts (array) – time axis of the time series
NW (float) – The normalized half-bandwidth of the data tapers, indicating a multiple of the fundamental frequency of the DFT (Fs/N). Common choices are n/2, for n >= 4.
BW (float) – The sampling-relative bandwidth of the data tapers
detrend (str) –
- If None, no detrending is applied. Available detrending methods:
None - no detrending will be applied (default);
linear - a linear least-squares fit to ys is subtracted;
constant - the mean of ys is subtracted
savitzy-golay - ys is filtered using the Savitzky-Golay filters and the resulting filtered series is subtracted from y.
emd - Empirical mode decomposition
- sg_kwargsdict
The parameters for the Savitzky-Golay filters. see pyleoclim.utils.filter.savitzy_golay for details.
- gaussianizebool
If True, gaussianizes the timeseries
- standardizebool
If True, standardizes the timeseries
- adaptive{True/False}
Use an adaptive weighting routine to combine the PSD estimates of different tapers.
- jackknife{True/False}
Use the jackknife method to make an estimate of the PSD variance at each point.
- low_bias{True/False}
Rather than use 2NW tapers, only use the tapers that have better than 90% spectral concentration within the bandwidth (still using a maximum of 2NW tapers)
- sidesstr (optional) [ ‘default’ | ‘onesided’ | ‘twosided’ ]
This determines which sides of the spectrum to return. For complex-valued inputs, the default is two-sided, for real-valued inputs, default is one-sided Indicates whether to return a one-sided or two-sided
- Returns
res_dict – the result dictionary, including - freq (array): the frequency vector - psd (array): the spectral density vector
- Return type
dict
See also
pyleoclim.utils.spectral.periodogram
Estimate power spectral density using a periodogram
pyleoclim.utils.spectral.welch
Retuns spectral density using the welch method
pyleoclim.utils.spectral.lomb_scargle
Return the computed periodogram using lomb-scargle algorithm
pyleoclim.utils.spectral.wwz_psd
Return the psd of a timeseries using wwz method.
pyleoclim.utils.filter.savitzy_golay
Filtering using Savitzy-Golay
pyleoclim.utils.tsutils.detrend
Detrending method