time_series.savitzky_golay

Smooth and optionally differentiate data of non-uniform sampling with a Savitzky-Golay filter
A type of low-pass filter, particularly suited for smoothing noisy data
For each point makes a least-square fit of a polynomial over an odd-sized window centered at the point

Calling Sequence

import gravity_toolkit.time_series
sg = gravity_toolkit.time_series.savitzky_golay(t_in, d_in, WINDOW=13, ORDER=2)

gravity_toolkit.time_series.savitzky_golay(t_in, y_in, WINDOW=None, ORDER=2, DERIV=0, RATE=1, DATA_ERR=0)[source]

Smooth and optionally differentiate data with a Savitzky-Golay filter [51, 54]

Parameters:

t_in: float

time array

y_in: float

data magnitude array

WINDOW: int or NoneType, default None

Length of the window

Must be an odd integer

ORDER: int, default 2

Order of the polynomial used in the filtering

Must be less than (window_size - 1)

DERIV: int, default 0

Order of the derivative to compute

RATE: float, default 1

Scaling factor for output data and error

DATA_ERR: float, default 0

Estimated data error of known and equal value

Returns: