pyacs.gts.lib.filters.savitzky_golay module

Savitzky-Golay filter for Gts based on scipy.signal.medfilt. http://https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.savgol_filter.html#scipy.signal.savgol_filter Additional information on Savitzky-Golay filter from https://scipy-cookbook.readthedocs.io/items/SavitzkyGolay.html

The Savitzky Golay filter is a particular type of low-pass filter, well adapted for data smoothing. For further information see: http://www.wire.tu-bs.de/OLDWEB/mameyer/cmr/savgol.pdf (or http://www.dalkescientific.com/writings/NBN/data/savitzky_golay.py for a pre-numpy implementation).

It has the advantage of preserving the original shape and features of the signal better than other types of filtering approaches, such as moving averages techniques.

The Savitzky-Golay is a type of low-pass filter, particularly suited for smoothing noisy data. The main idea behind this approach is to make for each point a least-square fit with a polynomial of high order over a odd-sized window centered at the point.

pyacs.gts.lib.filters.savitzky_golay.savitzky_golay(self, in_place=False, verbose=True, window_length=15, polyorder=3, deriv=0, delta=1.0, mode='interp', cval=0.0)[source]

Return a filtered time series using scipy.signal.savgol_filter.

Parameters:

in_place (bool, optional) – If True, replace the current time series; otherwise return a new Gts.
verbose (bool, optional) – Verbose mode.
window_length (int, optional) – Length of the filter window (must be odd).
polyorder (int, optional) – Order of the polynomial.
deriv (int, optional) – Order of derivative (0 = smoothing).
delta (float, optional) – Sample spacing.
mode (str, optional) – Extension mode (‘interp’, ‘mirror’, etc.).
cval (float, optional) – Value for mode=’constant’.

Returns:

Filtered time series.

Return type:

Gts