lomb_scargle (pyleoclim.utils.lomb_scargle)¶
-
pyleoclim.utils.
lomb_scargle
(ys, ts, freq=None, freq_method='lomb-scargle', freq_kwargs=None, n50=3, window='hann', detrend=None, params=['default', 4, 0, 1], gaussianize=False, standardize=False, average='mean')[source]¶ Return the computed periodogram using lomb-scargle algorithm
Uses the lombscargle implementation from scipy.signal: https://scipy.github.io/devdocs/generated/scipy.signal.lombscargle.html#scipy.signal.lombscargle
- Parameters
ys (array) – a time series
ts (array) – time axis of the time series
freq (str or array) – vector of frequency. If string, uses the following method:
freq_method (str) –
- Method to generate the frequency vector if not set directly. The following options are avialable:
log
lomb-scargle (default)
welch
scale
nfft
See utils.wavelet.make_freq_vector for details
freq_kwargs (dict) – Arguments for the method chosen in freq_method. See specific functions in utils.wavelet for details By default, uses dt=median(ts), ofac=4 and hifac=1 for Lomb-Scargle
n50 (int) – The number of 50% overlapping segment to apply
window (str or tuple) –
- Desired window to use. Possible values:
boxcar
triang
blackman
hamming
hann (default)
bartlett
flattop
parzen
bohman
blackmanharris
nuttail
barthann
kaiser (needs beta)
gaussian (needs standard deviation)
general_gaussian (needs power, width)
slepian (needs width)
dpss (needs normalized half-bandwidth)
chebwin (needs attenuation)
exponential (needs decay scale)
tukey (needs taper fraction)
If the window requires no parameters, then window can be a string. If the window requires parameters, then window must be a tuple with the first argument the string name of the window, and the next arguments the needed parameters. If window is a floating point number, it is interpreted as the beta parameter of the kaiser window.
- detrendstr
- 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
- paramslist
The paramters for the Savitzky-Golay filters. The first parameter corresponds to the window size (default it set to half of the data) while the second parameter correspond to the order of the filter (default is 4). The third parameter is the order of the derivative (the default is zero, which means only smoothing.)
- gaussianizebool
If True, gaussianizes the timeseries
- standardizebool
If True, standardizes the timeseriesprep_args : dict
- average{‘mean’,’median’}
Method to use when averaging periodograms. Defaults to ‘mean’.
- Returns
res_dict – the result dictionary, including - freq (array): the frequency vector - psd (array): the spectral density vector
- Return type
dict
See also
periodogram()
Estimate power spectral density using a periodogram
mtm()
Retuns spectral density using a multi-taper method
welch()
Returns power spectral density using the Welch method
wwz_psd()
Return the psd of a timeseries using wwz method.
References
Lomb, N. R. (1976). Least-squares frequency analysis of unequally spaced data. Astrophysics and Space Science 39, 447-462.
Scargle, J. D. (1982). Studies in astronomical time series analysis. II. Statistical aspects of spectral analyis of unvenly spaced data. The Astrophysical Journal, 263(2), 835-853.
Scargle, J. D. (1982). Studies in astronomical time series analysis. II. Statistical aspects of spectral analyis of unvenly spaced data. The Astrophysical Journal, 263(2), 835-853.
Examples
>>> from pyleoclim import utils >>> import matplotlib.pyplot as plt >>> import numpy as np >>> # Create a signal >>> time = np.arange(2001) >>> f = 1/50 >>> signal = np.cos(2*np.pi*f*time) >>> # Spectral Analysis >>> res = utils.lomb_scargle(signal, time) >>> # plot >>> fig = plt.loglog( ... res['freq'], ... res['psd']) >>> plt.xlabel('Frequency') >>> plt.ylabel('PSD') >>> plt.show()
(Source code, png)